A pair is recorded only when BOTH (a) a confident MediaPipe pose
(mean visibility > 0.5) AND (b) a fresh live CSI frame (source==esp32)
exist. We store the baseline-normalized CSI + the 17 keypoints, mirrored to
- IndexedDB so a refresh keeps them. Follow the prompt so every pose bucket fills up —
- a balanced set beats 2 000 frames of standing.
+ IndexedDB so a refresh keeps them. The routine runs multiple interleaved passes
+ through every activity, so a chronological held-out split contains the same activity mix
+ as training (fixes the OOD split). Follow the prompt so every pose bucket fills up.
@@ -190,7 +217,8 @@
mean-pose baseline PCK@0.10—
-
+
+
@@ -218,8 +246,11 @@
- no model loaded
+
+
+
camera: off
+
no model loaded
live inference
@@ -251,8 +282,9 @@
// ============================================================================
// Constants & shared state
// ============================================================================
+// wss when served over https (mobile/secure-context safe), else ws; ?ws= overrides
const CSI_WS = (new URLSearchParams(location.search)).get('ws')
- || `ws://${location.hostname || 'localhost'}:8765/ws/sensing`;
+ || `${location.protocol === 'https:' ? 'wss' : 'ws'}://${location.hostname || 'localhost'}:8765/ws/sensing`;
const NODE_IDS = [9, 13]; // per-node features in this fixed order (matches Python pipeline)
const FIELD_LEN = 400; // signal_field.values padded/truncated to 400
const CSI_DIM = 4 + NODE_IDS.length * 3 + FIELD_LEN; // 4 + 6 + 400 = 410
@@ -397,34 +429,70 @@ function onPoseResults(res){
latestKps = kps; latestVis = visSum / N_KP;
}
+// ONE camera shared across all stages. Each stage has its own
`).join('');
}
+let pausedAt = 0; // performance.now() captured at the moment of pause (used to rebase clocks on resume)
+function setCapControls(){
+ const live = recording;
+ $('recBtn').textContent = live ? '◼ stop recording' : '● start guided recording';
+ $('recBtn').classList.toggle('ghost', live);
+ $('pauseBtn').disabled = !live; $('skipBtn').disabled = !live;
+ $('pauseBtn').textContent = paused ? 'resume' : 'pause';
+}
$('recBtn').addEventListener('click', ()=>{
- if (!baseline || !camStream) return;
+ if (!baseline || !camStream){ return; }
recording = !recording;
- $('recBtn').textContent = recording ? '◼ stop recording' : '● start guided recording';
- $('recBtn').classList.toggle('ghost', recording);
- if (recording){ bucketT0 = performance.now(); }
+ if (recording){
+ paused = false; passIx = 0; actIx = 0;
+ startSegment(); // begins with a get-ready countdown for pass1/activity1
+ }
+ setCapControls();
+});
+$('pauseBtn').addEventListener('click', ()=>{
+ if (!recording) return;
+ paused = !paused;
+ // when pausing mid-segment, freeze the clocks so no time is lost; on resume, rebase them
+ if (!paused){
+ if (segReadyUntil) segReadyUntil = performance.now() + (segReadyUntil - pausedAt);
+ else segT0 = performance.now() - (pausedAt - segT0);
+ } else { pausedAt = performance.now(); }
+ setCapControls();
+});
+$('skipBtn').addEventListener('click', ()=>{
+ if (!recording) return;
+ if (!nextSegment()){ recording = false; paused = false; setCapControls(); }
});
$('clrBtn').addEventListener('click', async ()=>{
SAMPLES = []; covCounts = new Array(BUCKETS.length).fill(0);
await idbPut('samples', []);
- $('capN').textContent = '0'; $('trN').textContent = '0'; renderCoverage(); refreshGates();
+ $('capN').textContent = '0'; $('trN').textContent = '0'; $('capActN').textContent = '0';
+ renderCoverage(); refreshGates();
});
+// ---- Export / Import dataset (so multiple sessions accumulate, and data can be analyzed offline) ----
+$('expBtn').addEventListener('click', ()=>{
+ const out = {
+ format: 'wiflow-browser-dataset', version: 1, exported: new Date().toISOString(),
+ csi_dim: CSI_DIM, out_dim: OUT_DIM, buckets: BUCKETS,
+ note: 'csi is baseline-normalized (ADR-151 deviation-from-baseline); kps are 17 COCO keypoints in [0,1] image coords',
+ samples: SAMPLES.map((s,i)=>({ csi: Array.from(s.csi), kps: Array.from(s.kps), bucket: s.bucket, t: (s.t!=null?s.t:i) }))
+ };
+ const blob = new Blob([JSON.stringify(out)], { type:'application/json' });
+ const a = document.createElement('a');
+ a.href = URL.createObjectURL(blob);
+ a.download = `wiflow-dataset-${SAMPLES.length}samples-${Date.now()}.json`;
+ a.click(); setTimeout(()=>URL.revokeObjectURL(a.href), 2000);
+});
+$('impBtn').addEventListener('click', ()=> $('impFile').click());
+$('impFile').addEventListener('change', async (e)=>{
+ const file = e.target.files && e.target.files[0]; if (!file) return;
+ try{
+ const txt = await file.text();
+ const data = JSON.parse(txt);
+ const arr = Array.isArray(data) ? data : (data.samples || []);
+ let merged = 0;
+ for (const s of arr){
+ if (!s || !s.csi || !s.kps) continue;
+ if (s.csi.length !== CSI_DIM || s.kps.length !== OUT_DIM) continue; // dim guard — never fabricate
+ SAMPLES.push({ csi: Float32Array.from(s.csi), kps: Float32Array.from(s.kps),
+ bucket: (s.bucket|0), t: (s.t!=null?s.t:Date.now()) });
+ if ((s.bucket|0) < BUCKETS.length) covCounts[s.bucket|0]++;
+ merged++;
+ }
+ $('capN').textContent = SAMPLES.length; $('trN').textContent = SAMPLES.length;
+ $('capSkip').textContent = `imported ${merged} samples (merged)`;
+ renderCoverage(); await idbSave(); refreshGates();
+ }catch(err){
+ $('capSkip').textContent = 'import failed: ' + err.message;
+ console.error('import', err);
+ } finally { e.target.value = ''; }
+});
+
+// MediaPipe throttle: one-in-flight (no backlog) + ~18 Hz cap so the UI never blocks.
+let mpLastSend = 0;
+const MP_MIN_INTERVAL_MS = 55; // ~18 Hz
+
function captureLoop(){
const W = $('capCv').width, H = $('capCv').height;
drawCameraFrame(capCtx, W, H, 0.9);
- if (mpReady && !mpBusy && camEl.videoWidth > 0){
- mpBusy = true; mpPose.send({ image: camEl }).catch(()=>{ mpBusy = false; });
+ const nowMs = performance.now();
+ if (mpReady && !mpBusy && camEl.videoWidth > 0 && (nowMs - mpLastSend) >= MP_MIN_INTERVAL_MS){
+ mpBusy = true; mpLastSend = nowMs;
+ mpPose.send({ image: camEl }).catch(()=>{ mpBusy = false; });
}
if (latestKps) drawSkeleton(capCtx, latestKps, W, H, 'rgba(90,169,255,.95)', 'rgba(90,169,255,.6)');
@@ -602,29 +790,56 @@ function captureLoop(){
$('capNodes').textContent = latestCSI.nodes.length ? latestCSI.nodes.join(', ') : '—';
$('capVis').textContent = latestKps ? latestVis.toFixed(2) : '—';
- if (recording){
- // advance the guided bucket
- const el = (performance.now() - bucketT0)/1000;
- if (el >= SECS_PER_BUCKET){ bucketIx = (bucketIx+1) % BUCKETS.length; bucketT0 = performance.now(); }
- $('prompt').textContent = BUCKETS[bucketIx];
- $('countdown').textContent = `${Math.max(0,Math.ceil(SECS_PER_BUCKET - el))} s · bucket ${bucketIx+1}/${BUCKETS.length}`;
+ // estimate (always shown): segments × seconds × ~recorded-fps
+ const segs = totalSegments();
+ $('capEst').textContent = `~${Math.round(segs*capSecs()/60*10)/10} min · ${segs} segments`;
- let skip = null;
- if (!latestKps || latestVis <= 0.5) skip = 'no confident pose';
- else if (!freshLiveCSI()) skip = (latestCSI.source && latestCSI.source!=='esp32') ? 'CSI not esp32 (sim)' : 'no fresh CSI';
- else if (!baseline) skip = 'no baseline';
- if (skip){ $('capSkip').textContent = skip; }
- else {
- const norm = baselineNorm(latestCSI.vec); // baseline-deviation normalized
- SAMPLES.push({ csi: norm, kps: latestKps.slice(), bucket: bucketIx });
- covCounts[bucketIx]++;
- $('capSkip').textContent = '—';
- const n = SAMPLES.length; $('capN').textContent = n; $('trN').textContent = n;
- if (n % 20 === 0){ renderCoverage(); idbSave(); refreshGates(); }
+ if (recording && paused){
+ $('prompt').textContent = '⏸ PAUSED — ' + BUCKETS[actIx];
+ $('countdown').textContent = `pass ${passIx+1}/${capPasses()} · activity ${actIx+1}/${BUCKETS.length}`;
+ } else if (recording){
+ const done = passIx*BUCKETS.length + actIx; // segments completed so far
+ $('capPass').textContent = `${passIx+1}/${capPasses()} · ${actIx+1}/${BUCKETS.length}`;
+ $('capProg').style.width = Math.round(100*done/segs) + '%';
+
+ if (segReadyUntil){
+ // GET-READY: "GET INTO FRAME" + countdown, NO samples recorded yet
+ const remain = Math.max(0, (segReadyUntil - nowMs)/1000);
+ $('prompt').textContent = 'GET INTO FRAME — ' + BUCKETS[actIx];
+ $('countdown').textContent = `starting in ${Math.ceil(remain)} s · pass ${passIx+1}/${capPasses()} · activity ${actIx+1}/${BUCKETS.length}`;
+ capCtx.fillStyle='#ffb840'; capCtx.font='bold 20px monospace'; capCtx.textAlign='center';
+ capCtx.fillText(Math.ceil(remain)+'', W/2, 40); capCtx.textAlign='start';
+ if (remain <= 0){ segReadyUntil = 0; segT0 = nowMs; }
+ } else {
+ // RECORDING this activity
+ const el = (nowMs - segT0)/1000;
+ const secs = capSecs();
+ $('prompt').textContent = BUCKETS[actIx];
+ $('countdown').textContent = `${Math.max(0,Math.ceil(secs - el))} s · pass ${passIx+1}/${capPasses()} · activity ${actIx+1}/${BUCKETS.length}`;
+ if (el >= secs){
+ if (!nextSegment()){ recording = false; paused = false; setCapControls();
+ $('prompt').textContent = 'routine complete ✓'; $('countdown').textContent = `${SAMPLES.length} samples`; }
+ } else {
+ let skip = null;
+ if (!latestKps || latestVis <= 0.5) skip = 'no confident pose';
+ else if (!freshLiveCSI()) skip = (latestCSI.source && latestCSI.source!=='esp32') ? 'CSI not esp32 (sim)' : 'no fresh CSI';
+ else if (!baseline) skip = 'no baseline';
+ if (skip){ $('capSkip').textContent = skip; }
+ else {
+ const norm = baselineNorm(latestCSI.vec); // baseline-deviation normalized
+ SAMPLES.push({ csi: norm, kps: latestKps.slice(), bucket: actIx, t: Date.now() });
+ covCounts[actIx]++; actSampleN++;
+ $('capActN').textContent = actSampleN;
+ $('capSkip').textContent = '—';
+ const n = SAMPLES.length; $('capN').textContent = n; $('trN').textContent = n;
+ if (n % 20 === 0){ renderCoverage(); idbSave(); refreshGates(); }
+ }
+ }
}
} else {
$('prompt').textContent = baseline ? 'ready — press start' : 'calibrate baseline first';
$('countdown').textContent = '—';
+ $('capProg').style.width = '0%';
}
requestAnimationFrame(captureLoop);
}
@@ -664,7 +879,7 @@ async function idbDel(key){
}
async function idbSave(){
try{
- const flat = SAMPLES.map(s => ({ csi: Array.from(s.csi), kps: Array.from(s.kps), bucket: s.bucket }));
+ const flat = SAMPLES.map(s => ({ csi: Array.from(s.csi), kps: Array.from(s.kps), bucket: s.bucket, t: s.t }));
await idbPut('samples', flat);
}catch(e){ console.warn('idbSave', e); }
}
@@ -672,7 +887,8 @@ async function idbLoad(){
try{
const flat = await idbGet('samples');
if (Array.isArray(flat) && flat.length){
- SAMPLES = flat.map(s => ({ csi: Float32Array.from(s.csi), kps: Float32Array.from(s.kps), bucket: s.bucket||0 }));
+ SAMPLES = flat.map((s,i) => ({ csi: Float32Array.from(s.csi), kps: Float32Array.from(s.kps),
+ bucket: s.bucket||0, t: (s.t!=null?s.t:i) }));
covCounts = new Array(BUCKETS.length).fill(0);
for (const s of SAMPLES) if (s.bucket < BUCKETS.length) covCounts[s.bucket]++;
$('capN').textContent = SAMPLES.length; $('trN').textContent = SAMPLES.length;
@@ -779,39 +995,62 @@ async function trainModel(){
const xva = tf.tensor2d(Xn.slice(cut*CSI_DIM),[mVal,CSI_DIM]);
if (model){ model.dispose(); }
+ // Reduce overfit (train MSE 0.072 vs val 0.161 in the honest-negative run):
+ // - L2 weight decay on every Dense layer
+ // - slightly smaller capacity (384/192/96 vs 512/256/128)
+ // - keep dropout (raised to 0.35 in the wider layers)
+ const L2 = tf.regularizers.l2({ l2: 1e-4 });
model = tf.sequential();
- model.add(tf.layers.dense({ inputShape:[CSI_DIM], units:512, activation:'relu' }));
- model.add(tf.layers.dropout({ rate:0.3 }));
- model.add(tf.layers.dense({ units:256, activation:'relu' }));
- model.add(tf.layers.dropout({ rate:0.3 }));
- model.add(tf.layers.dense({ units:128, activation:'relu' }));
- model.add(tf.layers.dense({ units:OUT_DIM, activation:'sigmoid' }));
+ model.add(tf.layers.dense({ inputShape:[CSI_DIM], units:384, activation:'relu', kernelRegularizer:L2 }));
+ model.add(tf.layers.dropout({ rate:0.35 }));
+ model.add(tf.layers.dense({ units:192, activation:'relu', kernelRegularizer:tf.regularizers.l2({ l2:1e-4 }) }));
+ model.add(tf.layers.dropout({ rate:0.35 }));
+ model.add(tf.layers.dense({ units:96, activation:'relu', kernelRegularizer:tf.regularizers.l2({ l2:1e-4 }) }));
+ model.add(tf.layers.dense({ units:OUT_DIM, activation:'sigmoid', kernelRegularizer:tf.regularizers.l2({ l2:1e-4 }) }));
model.compile({ optimizer: tf.train.adam(1e-3), loss:'meanSquaredError' });
- let bestP10 = 0, bestVal = 1e9;
+ // Early stopping on val MSE: stop after `patience` epochs with no improvement, restore best weights.
+ const patience = Math.max(5, Math.min(200, parseInt($('trPatience').value)||30));
+ let bestP10 = 0, bestVal = 1e9, bestWeights = null, sinceImprove = 0, stoppedEarly = false;
+ const disposeBest = ()=>{ if (bestWeights){ bestWeights.forEach(w=>w.dispose()); bestWeights = null; } };
$('trMsg').innerHTML = 'Training… on '+(BACKEND_LABEL[tf.getBackend()]||tf.getBackend())+'';
await model.fit(xtr, ytr, {
epochs, batchSize:64, shuffle:true, verbose:0,
callbacks:{ onEpochEnd: async (ep, logs)=>{
- let va=null,p10=null,p05=null,mpj=null;
- if (ep % 5 === 0 || ep === epochs-1){
- const pv = model.predict(xva); const pvArr = await pv.data(); pv.dispose();
- let vsum=0; for (let i=0;i w.clone());
+ } else {
+ sinceImprove++;
}
+
$('trEpoch').textContent=(ep+1); $('trLoss').textContent=logs.loss.toFixed(4);
$('trBar').style.width=(100*(ep+1)/epochs)+'%';
lossHist.push({ ep, tr:logs.loss, va }); drawLoss();
+
+ if (sinceImprove >= patience){
+ stoppedEarly = true; model.stopTraining = true;
+ $('trMsg').innerHTML = `Early stop at epoch ${ep+1}: val MSE hasn't improved for ${patience} epochs. Restoring best weights (val ${bestVal.toFixed(4)}).`;
+ }
if (trainStop) model.stopTraining = true;
await tf.nextFrame();
}}
});
+ // restore the best-val weights (early stopping semantics: report the best model, not the last)
+ if (bestWeights){ model.setWeights(bestWeights); disposeBest(); }
+
const pvF = model.predict(xva); const pvFArr = await pvF.data(); pvF.dispose();
const fin10 = pckMpjpe(pvFArr,gtVal,mVal,0.10), fin05 = pckMpjpe(pvFArr,gtVal,mVal,0.05);
const finPck = Math.max(bestP10, fin10.pck); trainedPck10 = finPck;
@@ -829,10 +1068,11 @@ async function trainModel(){
}
stageDone.train = true;
$('infModelState').textContent = `model ready · held-out PCK@0.10 ${(finPck*100).toFixed(1)}%`;
- $('trMsg').innerHTML = 'Done. Saving model to IndexedDB…';
+ const stopNote = stoppedEarly ? ' (early-stopped, best weights restored)' : '';
+ $('trMsg').innerHTML = 'Done'+stopNote+'. Saving model to IndexedDB…';
xtr.dispose(); ytr.dispose(); xva.dispose();
await saveModel();
- $('trMsg').innerHTML = 'Saved. Go to 3 · INFER to see WiFi drive the skeleton.';
+ $('trMsg').innerHTML = 'Saved'+stopNote+'. Go to 3 · INFER to see WiFi drive the skeleton.';
$('trainBtn').disabled = false; $('trStop').disabled = true;
refreshGates();
}
@@ -916,6 +1156,14 @@ function inferLoop(){
await idbLoad();
await loadModel();
renderCoverage();
+ setCapControls(); // pause/skip start disabled until recording
+ // if a camera was chosen in a previous session, surface it in the selectors (labels need permission,
+ // so this only fully populates after the user clicks "enable camera" in any stage)
+ try{
+ const saved = localStorage.getItem(CAM_KEY);
+ const devs = (await navigator.mediaDevices.enumerateDevices()).filter(d => d.kind === 'videoinput');
+ if (devs.length) populateCamSelectors(devs, saved);
+ }catch(e){}
refreshGates();
requestAnimationFrame(calibrateLoop);
requestAnimationFrame(captureLoop);
diff --git a/v2/crates/ruview-swarm b/v2/crates/ruview-swarm
new file mode 160000
index 00000000..472d920f
--- /dev/null
+++ b/v2/crates/ruview-swarm
@@ -0,0 +1 @@
+Subproject commit 472d920fe774e7473d424c8be68c8ba3f800336c
diff --git a/v2/crates/ruview-swarm/Cargo.toml b/v2/crates/ruview-swarm/Cargo.toml
deleted file mode 100644
index 8592af76..00000000
--- a/v2/crates/ruview-swarm/Cargo.toml
+++ /dev/null
@@ -1,84 +0,0 @@
-[package]
-name = "ruview-swarm"
-version = "0.1.0"
-edition = "2021"
-description = "RuView drone swarm control system — hierarchical-mesh topology, Raft consensus, MARL, CSI sensing integration (ADR-148)"
-license = "Apache-2.0"
-# Publishing disabled until: (1) PR #862 merges, (2) internal path-deps are
-# published in dependency order, (3) export-control sign-off on the ITAR-gated
-# coordination features (USML Category VIII(h)(12)). Flip to true deliberately.
-publish = false
-
-[features]
-default = []
-# ITAR/USML Category VIII(h)(12): swarming coordination features.
-# Must not be enabled in international distributions without export counsel review.
-itar-unrestricted = []
-mavlink = ["dep:mavlink"]
-ros2-dds = []
-onnx = ["dep:ort"]
-simulation = []
-demo = ["simulation"]
-full = ["mavlink", "onnx", "demo", "itar-unrestricted"]
-ruflo = ["dep:reqwest", "dep:serde_json"]
-# Heavy GPU-capable MARL training (real Candle autodiff PPO). Off by default so
-# the default build stays light and the existing test suite keeps passing.
-train = ["dep:candle-core", "dep:candle-nn"]
-cuda = ["candle-core/cuda", "candle-nn/cuda"]
-
-[dependencies]
-wifi-densepose-core = { path = "../wifi-densepose-core" }
-
-# Serialization
-serde = { version = "1", features = ["derive"] }
-serde_json = { version = "1", optional = true }
-toml = "0.8"
-
-# Async runtime
-tokio = { version = "1", features = ["full"] }
-async-trait = "0.1"
-
-# MAVLink v2 (optional)
-mavlink = { version = "0.13", optional = true }
-
-# ONNX Runtime (optional — for MARL actor inference)
-ort = { version = "2.0.0-rc.11", optional = true }
-
-# Candle 0.9 — real autodiff PPO training (optional, behind `train` feature).
-candle-core = { version = "0.9", default-features = false, optional = true }
-candle-nn = { version = "0.9", default-features = false, optional = true }
-
-# HTTP client (optional — for Ruflo HTTP backend)
-reqwest = { version = "0.12", features = ["json"], optional = true }
-
-# Crypto — MAVLink v2 HMAC-SHA256 signing
-hmac = "0.12"
-sha2 = "0.10"
-
-# Error handling
-thiserror = "2.0"
-
-# Logging
-tracing = "0.1"
-
-# Numerics
-nalgebra = "0.33"
-rand = "0.8"
-
-[dev-dependencies]
-criterion = { version = "0.5", features = ["html_reports"] }
-tokio-test = "0.4"
-
-[[bench]]
-name = "swarm_bench"
-harness = false
-
-# MARL training binary — requires the `train` feature (Candle autodiff).
-# Excluded from the default build so `cargo test`/CI stay light.
-[[bin]]
-name = "train_marl"
-required-features = ["train"]
-
-# ADR-171 Stage-1 evaluation CLI — pure Rust, no special feature needed.
-[[bin]]
-name = "eval_swarm"
diff --git a/v2/crates/ruview-swarm/README.md b/v2/crates/ruview-swarm/README.md
deleted file mode 100644
index 744dcf50..00000000
--- a/v2/crates/ruview-swarm/README.md
+++ /dev/null
@@ -1,108 +0,0 @@
-# wifi-densepose-swarm
-
-Drone swarm control system for the RuView wifi-densepose workspace. Implements ADR-148.
-
-## Overview
-
-`wifi-densepose-swarm` provides a hierarchical-mesh drone swarm coordination system
-with Raft consensus, MAPPO-based multi-agent reinforcement learning, and tight
-integration with the existing WiFi CSI sensing pipeline (`wifi-densepose-signal`,
-`wifi-densepose-ruvector`).
-
-## Features
-
-- **Hierarchical-Mesh Topology** — cluster heads over Raft consensus; inter-cluster Gossip for map dissemination
-- **Formation Control** — F1 VirtualStructure, F2 LeaderFollower, F3 Reynolds flocking
-- **3-Phase Coverage** — boustrophedon sweep → Bayesian probability grid → multi-drone triangulation
-- **RRT-APF Path Planner** — RRT* with Artificial Potential Field reactive collision avoidance
-- **MARL Actor (MAPPO)** — 64-dim local observation, 3-layer MLP actor, CTDE training interface
-- **CSI Sensing Integration** — drone payload pipeline (ESP32-S3 → Jetson), multi-drone CSI fusion
-- **OccWorld Bridge** — integrates ADR-147 OccWorld occupancy prior as path planner environment
-- **Security Hardening** — MAVLink v2 HMAC-SHA256 signing, UWB GPS anti-spoofing, onboard geofencing, Remote ID
-- **Fail-Safe State Machine** — 10-state onboard safety system, GCS-independent
-- **Demo & Training Modes** — synthetic CSI generation, Gazebo/PX4 SITL interface, TOML mission configs
-
-## ITAR Notice
-
-> ⚠️ **Export-controlled capability.** Swarming coordination features (formation control,
-> Raft consensus, task allocation) are gated behind the `itar-unrestricted` feature flag
-> per **USML Category VIII(h)(12)**. Default builds compile only safe stubs.
-> Do not enable `itar-unrestricted` for international distribution without export counsel review.
-
-## Crate Features
-
-| Feature | Description |
-|---------|-------------|
-| `default` | Core types, sensing, failsafe, config, MARL — no ITAR-gated code |
-| `itar-unrestricted` | Enables formation control, Raft consensus, task allocation |
-| `mavlink` | MAVLink v2 protocol support |
-| `onnx` | ONNX Runtime backend for MARL actor inference (INT8) |
-| `simulation` | Simulation-mode stubs |
-| `demo` | Synthetic CSI generation, scenario runners |
-| `full` | All of the above |
-
-## Quick Start
-
-```rust
-use wifi_densepose_swarm::{config::SwarmConfig, demo::scenario::DemoScenario};
-
-// Load a mission profile
-let config = SwarmConfig::sar_default();
-
-// Run a demo scenario
-let scenario = DemoScenario::sar_rubble_field(4); // 4-drone SAR
-let estimated_secs = scenario.estimate_coverage_time_secs();
-// → < 240 s for 4 drones over 400×400 m (beyond Wi2SAR SOTA single-drone baseline)
-```
-
-## Mission Profiles
-
-| Profile | Drones | Area | Application |
-|---------|--------|------|-------------|
-| `sar` | 6–12 | 400×400 m | Structural collapse victim search |
-| `inspection` | 3–6 | Linear corridor | Infrastructure (power lines, bridges) |
-| `agriculture` | 4–12 | Field-configurable | NDVI mapping, variable-rate spraying |
-| `mine` | 2–4 | Tunnel | GPS-denied underground exploration |
-| `relay` | 6–20 | Perimeter | Emergency telecom relay chain |
-| `demo` | Any | Configurable | Synthetic CSI, configurable victims |
-
-## Module Structure
-
-```
-src/
-├── types.rs — NodeId, DroneState, SwarmTask, SwarmError, FailSafeState
-├── topology/ — Raft consensus¹, Gossip dissemination, MeshTopology
-├── formation/ — VirtualStructure¹, LeaderFollower¹, Reynolds flocking¹
-├── planning/ — RRT-APF planner, 3-phase coverage, Bayesian grid, pheromone
-├── allocation/ — Auction-based task allocation¹, FNN bid scorer¹
-├── sensing/ — CSI payload pipeline, multi-drone fusion, OccWorld bridge
-├── marl/ — MAPPO actor, LocalObservation, reward shaping, TrainingConfig
-├── security/ — MAVLink signing, UWB anti-spoofing, geofencing, Remote ID
-├── failsafe/ — 10-state onboard fail-safe machine
-├── config/ — TOML SwarmConfig with mission presets
-├── demo/ — Synthetic CSI, DemoScenario runners
-├── integration/ — FlightController trait (PX4/ArduPilot/Sim)
-└── bench_support.rs — Criterion fixture generators
-
-¹ Requires `itar-unrestricted` feature.
-```
-
-## Related ADRs
-
-| ADR | Title | Relation |
-|-----|-------|----------|
-| ADR-148 | Drone Swarm Control System | This crate |
-| ADR-147 | OccWorld Occupancy World Model | Environment prior via `sensing::occworld_bridge` |
-| ADR-134 | CSI→CIR ISTA Sparse Recovery | Drone payload sensing |
-| ADR-146 | RF Encoder Multitask Heads | Drone payload inference |
-| ADR-016 | RuVector Training Integration | CrossViewpointAttention |
-
-## Performance Targets (vs. Wi2SAR SOTA)
-
-| Metric | Wi2SAR baseline (1 drone) | 4-drone target |
-|--------|--------------------------|----------------|
-| Coverage | 160,000 m² | 160,000 m² |
-| Time | 13.5 min | ≤ 4 min |
-| Localization | 5 m | ≤ 2 m (3-view fusion) |
-| MARL inference | N/A | ≤ 5 ms (INT8, release) |
-| Raft election | N/A | ≤ 300 ms |
diff --git a/v2/crates/ruview-swarm/benches/swarm_bench.rs b/v2/crates/ruview-swarm/benches/swarm_bench.rs
deleted file mode 100644
index e87d5634..00000000
--- a/v2/crates/ruview-swarm/benches/swarm_bench.rs
+++ /dev/null
@@ -1,70 +0,0 @@
-use criterion::{criterion_group, criterion_main, Criterion};
-use ruview_swarm::marl::{MappoActor, ActorConfig};
-use ruview_swarm::marl::LocalObservation;
-use ruview_swarm::sensing::MultiViewFusion;
-use ruview_swarm::planning::RrtApfPlanner;
-use ruview_swarm::demo::{DemoScenario};
-use ruview_swarm::types::{CsiDetection, NodeId, Position3D};
-
-fn bench_marl_inference(c: &mut Criterion) {
- let actor = MappoActor::random_init(ActorConfig::default());
- let obs = LocalObservation::zeros();
- c.bench_function("marl_actor_inference", |b| b.iter(|| actor.forward(&obs)));
-}
-
-fn bench_rrt_apf_plan(c: &mut Criterion) {
- let planner = RrtApfPlanner::new(3.0);
- let start = Position3D { x: 0.0, y: 0.0, z: -30.0 };
- let goal = Position3D { x: 50.0, y: 50.0, z: -30.0 };
- c.bench_function("rrt_apf_100iter", |b| b.iter(|| {
- let mut rng = rand::thread_rng();
- planner.plan(start, goal, 100, &mut rng)
- }));
-}
-
-fn bench_multiview_fusion(c: &mut Criterion) {
- let fusion = MultiViewFusion::default();
- let detections = vec![
- CsiDetection { drone_id: NodeId(0), confidence: 0.85, victim_position: Some(Position3D { x: 51.0, y: 49.0, z: 0.0 }), timestamp_ms: 0 },
- CsiDetection { drone_id: NodeId(1), confidence: 0.78, victim_position: Some(Position3D { x: 49.0, y: 51.0, z: 0.0 }), timestamp_ms: 0 },
- CsiDetection { drone_id: NodeId(2), confidence: 0.92, victim_position: Some(Position3D { x: 50.0, y: 50.0, z: 0.0 }), timestamp_ms: 0 },
- ];
- let positions = vec![
- (NodeId(0), Position3D { x: 0.0, y: 0.0, z: -30.0 }),
- (NodeId(1), Position3D { x: 100.0, y: 0.0, z: -30.0 }),
- (NodeId(2), Position3D { x: 50.0, y: 86.6, z: -30.0 }),
- ];
- c.bench_function("multiview_fusion_3drones", |b| b.iter(|| fusion.fuse(&detections, &positions)));
-}
-
-fn bench_demo_coverage_estimate(c: &mut Criterion) {
- let scenario = DemoScenario::sar_rubble_field(4);
- c.bench_function("demo_coverage_estimate", |b| b.iter(|| scenario.estimate_coverage_time_secs()));
-}
-
-fn bench_ppo_update(c: &mut Criterion) {
- use ruview_swarm::marl::{MappoActor, ActorConfig, LocalObservation};
- use ruview_swarm::marl::training_loop::{ReplayBuffer, Transition, PpoConfig, ppo_update};
- use ruview_swarm::marl::actor::ActorAction;
-
- let mut buf = ReplayBuffer::new(64);
- for i in 0..64 {
- buf.push(Transition {
- obs: LocalObservation::zeros(),
- action: ActorAction { delta_heading_rad: 0.1, delta_altitude_m: 0.0, speed_ms: 5.0, trigger_csi_scan: true },
- reward: if i % 2 == 0 { 10.0 } else { -2.0 },
- next_obs: LocalObservation::zeros(),
- done: i == 63,
- });
- }
- let cfg = PpoConfig::default();
- c.bench_function("ppo_update_64transitions", |b| {
- b.iter(|| {
- let mut actor = MappoActor::random_init(ActorConfig::default());
- ppo_update(&mut actor, &buf, &cfg)
- })
- });
-}
-
-criterion_group!(benches, bench_marl_inference, bench_rrt_apf_plan, bench_multiview_fusion, bench_demo_coverage_estimate, bench_ppo_update);
-criterion_main!(benches);
diff --git a/v2/crates/ruview-swarm/evals/.gitkeep b/v2/crates/ruview-swarm/evals/.gitkeep
deleted file mode 100644
index 248db447..00000000
--- a/v2/crates/ruview-swarm/evals/.gitkeep
+++ /dev/null
@@ -1,2 +0,0 @@
-# ADR-171 evaluation outputs
-RESULTS.md is generated by the `eval_swarm` binary.
diff --git a/v2/crates/ruview-swarm/evals/RESULTS.md b/v2/crates/ruview-swarm/evals/RESULTS.md
deleted file mode 100644
index 955ebb43..00000000
--- a/v2/crates/ruview-swarm/evals/RESULTS.md
+++ /dev/null
@@ -1,26 +0,0 @@
-# ruview-swarm Evaluation Results (ADR-171 Stage 1, kinematic)
-
-Statistically-rigorous evaluation harness: seeded multi-run rollouts with IQM + 95% stratified-bootstrap confidence intervals (Agarwal et al., NeurIPS 2021).
-
-## Run configuration
-
-- **Stage**: 1 (kinematic, self-contained, deterministic per seed)
-- **Episodes per pattern**: 100 (seed × episode matrix)
-- **CI method**: 95% stratified bootstrap of the IQM, stratified by seed
-- **GDOP**: 2-D geometric dilution of precision at first detection
-
-> **Stage 2 pending**: high-fidelity Gazebo/PX4 SITL evaluation (false-alarm rate, real collision rate on the median seeds) is a follow-on — see ADR-171 §6.1. The collision figures below are a kinematic min-separation proxy, not SITL physics.
-
-## Flight-pattern leaderboard
-
-| Flight pattern | Coverage IQM [95% CI] | Localization (m) IQM [95% CI] | Detection rate | Mean GDOP |
-|----------------|-----------------------|-------------------------------|----------------|-----------|
-| partitioned_lawnmower | 1.000 [1.000, 1.000] | 7.022 [5.669, 8.379] | 100.0% | 0.000 |
-| pheromone | 0.662 [0.652, 0.671] | 4.110 [3.346, 5.141] | 95.0% | 1.598 |
-| levy_flight | 0.490 [0.489, 0.491] | 3.523 [2.897, 4.160] | 100.0% | 0.000 |
-| boustrophedon | 0.370 [0.370, 0.370] | 2.740 [2.357, 3.207] | 100.0% | 0.000 |
-| spiral | 0.336 [0.336, 0.336] | 3.082 [2.678, 3.568] | 100.0% | 0.000 |
-| potential_field | 0.254 [0.252, 0.256] | 4.343 [3.489, 5.265] | 100.0% | 0.000 |
-| _Wi2SAR (paper baseline)_ | _n/a_ | _5.0 (paper)_ | _n/a_ | _n/a_ |
-
-_Wi2SAR row is the published single-drone localization figure (arxiv 2604.09115), shown paper-to-paper for reference only — it was not re-run through this kinematic harness._
diff --git a/v2/crates/ruview-swarm/src/allocation/auction.rs b/v2/crates/ruview-swarm/src/allocation/auction.rs
deleted file mode 100644
index 95283e76..00000000
--- a/v2/crates/ruview-swarm/src/allocation/auction.rs
+++ /dev/null
@@ -1,118 +0,0 @@
-//! Contract-net (auction) task allocation.
-
-use crate::types::{DroneState, NodeId, SwarmTask, TaskId};
-use std::collections::HashMap;
-
-/// A bid submitted by a node for a task.
-#[derive(Debug, Clone)]
-pub struct Bid {
- pub node_id: NodeId,
- pub task_id: TaskId,
- /// Lower score = more capable/willing. Computed by the bidding node.
- pub score: f32,
-}
-
-/// Auction-based task allocator.
-pub struct AuctionAllocator {
- pub pending_tasks: HashMap,
- pub bids: HashMap>,
- pub timeout_ms: u64,
-}
-
-impl AuctionAllocator {
- pub fn new(timeout_ms: u64) -> Self {
- Self {
- pending_tasks: HashMap::new(),
- bids: HashMap::new(),
- timeout_ms,
- }
- }
-
- /// Announce a new task (add to pending pool).
- pub fn announce_task(&mut self, task: SwarmTask) {
- let id = task.id;
- self.pending_tasks.insert(id, task);
- self.bids.entry(id).or_default();
- }
-
- /// Accept a bid for a pending task.
- pub fn submit_bid(&mut self, bid: Bid) {
- if self.pending_tasks.contains_key(&bid.task_id) {
- self.bids.entry(bid.task_id).or_default().push(bid);
- }
- }
-
- /// Resolve all pending tasks: assign each to the best bidder.
- /// Returns a list of (TaskId, winning NodeId) pairs.
- pub fn resolve(&mut self) -> Vec<(TaskId, NodeId)> {
- let mut results = Vec::new();
- let task_ids: Vec = self.pending_tasks.keys().copied().collect();
-
- for task_id in task_ids {
- let winner = self
- .bids
- .get(&task_id)
- .and_then(|bids| {
- bids.iter()
- .min_by(|a, b| {
- a.score.partial_cmp(&b.score).unwrap_or(std::cmp::Ordering::Equal)
- })
- .map(|b| b.node_id)
- });
-
- if let Some(winner_id) = winner {
- if let Some(task) = self.pending_tasks.get_mut(&task_id) {
- task.assigned_to = Some(winner_id);
- }
- results.push((task_id, winner_id));
- self.bids.remove(&task_id);
- }
- }
-
- // Clean up resolved tasks
- for (tid, _) in &results {
- self.pending_tasks.remove(tid);
- }
-
- results
- }
-
- /// Compute a bid score heuristic for a node given a task.
- /// Returns a score ∈ [0, ∞): lower is better.
- pub fn compute_bid_score(node: &DroneState, task: &SwarmTask) -> f32 {
- let dist = node.position.distance_to(&task.target) as f32;
- let battery_penalty = (100.0 - node.battery_pct) / 100.0;
- let link_penalty = 1.0 - node.link_quality;
- let priority_bonus = 1.0 - task.priority.clamp(0.0, 1.0);
- dist / 100.0 + battery_penalty * 0.3 + link_penalty * 0.2 + priority_bonus * 0.1
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
- use crate::types::{Position3D, SwarmTask, TaskId, TaskKind};
-
- fn make_task(id: u64) -> SwarmTask {
- SwarmTask {
- id: TaskId(id),
- kind: TaskKind::ReturnToHome,
- priority: 0.5,
- target: Position3D::zero(),
- deadline_ms: None,
- assigned_to: None,
- }
- }
-
- #[test]
- fn test_auction_assigns_best_bidder() {
- let mut alloc = AuctionAllocator::new(1000);
- let task = make_task(1);
- alloc.announce_task(task);
- alloc.submit_bid(Bid { node_id: NodeId(1), task_id: TaskId(1), score: 0.8 });
- alloc.submit_bid(Bid { node_id: NodeId(2), task_id: TaskId(1), score: 0.3 });
- let results = alloc.resolve();
- assert_eq!(results.len(), 1);
- assert_eq!(results[0].1, NodeId(2)); // lower score wins
- }
-}
diff --git a/v2/crates/ruview-swarm/src/allocation/fnn.rs b/v2/crates/ruview-swarm/src/allocation/fnn.rs
deleted file mode 100644
index 637dedfe..00000000
--- a/v2/crates/ruview-swarm/src/allocation/fnn.rs
+++ /dev/null
@@ -1,97 +0,0 @@
-//! Lightweight 3-layer FNN bid scorer — pure Rust, no ONNX required.
-
-/// 3-layer FNN: 5 inputs → 16 hidden (ReLU) → 8 hidden (ReLU) → 1 output (sigmoid).
-pub struct FnnScorer {
- pub w1: [[f32; 5]; 16],
- pub b1: [f32; 16],
- pub w2: [[f32; 16]; 8],
- pub b2: [f32; 8],
- pub w3: [f32; 8],
- pub b3: f32,
-}
-
-fn relu(x: f32) -> f32 {
- x.max(0.0)
-}
-
-fn sigmoid(x: f32) -> f32 {
- 1.0 / (1.0 + (-x).exp())
-}
-
-impl FnnScorer {
- /// Score a feature vector. Returns sigmoid(output) ∈ [0, 1].
- /// Features: [dist_norm, battery_norm, link_quality, csi_confidence, workload_norm]
- pub fn score(&self, features: [f32; 5]) -> f32 {
- // Layer 1: 5 → 16 (ReLU)
- let mut h1 = [0.0f32; 16];
- for (i, row) in self.w1.iter().enumerate() {
- let z: f32 = row.iter().zip(features.iter()).map(|(w, x)| w * x).sum();
- h1[i] = relu(z + self.b1[i]);
- }
-
- // Layer 2: 16 → 8 (ReLU)
- let mut h2 = [0.0f32; 8];
- for (i, row) in self.w2.iter().enumerate() {
- let z: f32 = row.iter().zip(h1.iter()).map(|(w, x)| w * x).sum();
- h2[i] = relu(z + self.b2[i]);
- }
-
- // Layer 3: 8 → 1 (sigmoid)
- let z3: f32 = self.w3.iter().zip(h2.iter()).map(|(w, x)| w * x).sum::() + self.b3;
- sigmoid(z3)
- }
-
- /// Default weights initialised to a simple identity-like setup.
- pub fn default_weights() -> Self {
- // Simple: w1 diagonalish, others small constant
- // Index needed: diagonal/strided init uses i for both row and column.
- let mut w1 = [[0.0f32; 5]; 16];
- #[allow(clippy::needless_range_loop)]
- for i in 0..5 {
- w1[i][i] = 1.0;
- }
- for row in w1.iter_mut().take(16).skip(5) {
- row[0] = 0.1;
- }
- let mut w2 = [[0.0f32; 16]; 8];
- #[allow(clippy::needless_range_loop)]
- for i in 0..8 {
- w2[i][i * 2] = 1.0;
- }
- let w3 = [0.125f32; 8];
- Self {
- w1,
- b1: [0.0; 16],
- w2,
- b2: [0.0; 8],
- w3,
- b3: 0.0,
- }
- }
-}
-
-impl Default for FnnScorer {
- fn default() -> Self {
- Self::default_weights()
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- #[test]
- fn test_score_in_unit_interval() {
- let scorer = FnnScorer::default_weights();
- let features = [0.3f32, 0.8, 0.9, 0.75, 0.2];
- let s = scorer.score(features);
- assert!(s >= 0.0 && s <= 1.0, "score {s} out of [0,1]");
- }
-
- #[test]
- fn test_score_deterministic() {
- let scorer = FnnScorer::default_weights();
- let f = [0.5f32; 5];
- assert_eq!(scorer.score(f), scorer.score(f));
- }
-}
diff --git a/v2/crates/ruview-swarm/src/allocation/mod.rs b/v2/crates/ruview-swarm/src/allocation/mod.rs
deleted file mode 100644
index 03e68d15..00000000
--- a/v2/crates/ruview-swarm/src/allocation/mod.rs
+++ /dev/null
@@ -1,22 +0,0 @@
-//! Task allocation: auction-based and FNN-scored bid evaluation.
-//!
-// NOTE: Task allocation is ITAR-controlled (USML Category VIII(h)(12)).
-// Only available when the `itar-unrestricted` feature is enabled.
-
-#[cfg(feature = "itar-unrestricted")]
-pub mod auction;
-#[cfg(feature = "itar-unrestricted")]
-pub mod fnn;
-
-#[cfg(feature = "itar-unrestricted")]
-pub use auction::{AuctionAllocator, Bid};
-#[cfg(feature = "itar-unrestricted")]
-pub use fnn::FnnScorer;
-
-/// Stub: task allocation is export-controlled. Enable `itar-unrestricted` feature.
-#[cfg(not(feature = "itar-unrestricted"))]
-pub fn allocate_stub() -> crate::SwarmResult<()> {
- Err(crate::SwarmError::Security(
- "Task allocation requires itar-unrestricted feature (USML VIII(h)(12))".into(),
- ))
-}
diff --git a/v2/crates/ruview-swarm/src/bench_support.rs b/v2/crates/ruview-swarm/src/bench_support.rs
deleted file mode 100644
index f93eb58e..00000000
--- a/v2/crates/ruview-swarm/src/bench_support.rs
+++ /dev/null
@@ -1,45 +0,0 @@
-//! Benchmark support utilities: scenario builders and timing helpers for criterion benchmarks.
-
-use crate::types::{DroneState, NodeId, Position3D, Velocity3D};
-
-/// Generate N drone states arranged in a grid.
-pub fn grid_drone_states(n: usize, spacing_m: f64) -> Vec {
- let side = (n as f64).sqrt().ceil() as usize;
- (0..n)
- .map(|i| {
- let row = i / side;
- let col = i % side;
- DroneState {
- id: NodeId(i as u32),
- position: Position3D {
- x: col as f64 * spacing_m,
- y: row as f64 * spacing_m,
- z: -30.0,
- },
- velocity: Velocity3D::default(),
- heading_rad: 0.0,
- altitude_agl_m: 30.0,
- battery_pct: 80.0,
- link_quality: 0.9,
- timestamp_ms: 0,
- }
- })
- .collect()
-}
-
-/// Generate N evenly-spaced positions in a circle.
-pub fn circle_positions(n: usize, radius_m: f64) -> Vec<(NodeId, Position3D)> {
- (0..n)
- .map(|i| {
- let angle = 2.0 * std::f64::consts::PI * i as f64 / n as f64;
- (
- NodeId(i as u32),
- Position3D {
- x: radius_m * angle.cos(),
- y: radius_m * angle.sin(),
- z: -30.0,
- },
- )
- })
- .collect()
-}
diff --git a/v2/crates/ruview-swarm/src/bin/eval_swarm.rs b/v2/crates/ruview-swarm/src/bin/eval_swarm.rs
deleted file mode 100644
index f2db71bf..00000000
--- a/v2/crates/ruview-swarm/src/bin/eval_swarm.rs
+++ /dev/null
@@ -1,104 +0,0 @@
-//! ADR-171 Stage-1 evaluation CLI.
-//!
-//! Runs the kinematic eval matrix over every flight pattern (default) and
-//! writes a ranked `RESULTS.md` leaderboard. Pure Rust — no special feature
-//! flag required, so it builds and runs in default CI.
-//!
-//! Defaults are intentionally small (10 seeds × 10 episodes) so the run is fast.
-//! The full ADR-171 reporting configuration is 10 seeds × 50 episodes — pass
-//! `--seeds 10 --episodes 50` for the publication run.
-//!
-//! ```text
-//! cargo run -p ruview-swarm --bin eval_swarm -- \
-//! --seeds 10 --episodes 10 --out crates/ruview-swarm/evals/RESULTS.md
-//! ```
-
-use std::path::PathBuf;
-
-use ruview_swarm::evals::metrics::AggregateMetrics;
-use ruview_swarm::evals::report::render_results_md;
-use ruview_swarm::evals::runner::{run_matrix, EvalConfig};
-use ruview_swarm::planning::patterns::FlightPattern;
-
-fn main() {
- let args: Vec = std::env::args().collect();
- let mut seeds = 10usize;
- let mut episodes = 10usize;
- let mut out = PathBuf::from("crates/ruview-swarm/evals/RESULTS.md");
-
- let mut i = 1;
- while i < args.len() {
- match args[i].as_str() {
- "--seeds" => {
- i += 1;
- seeds = args.get(i).and_then(|s| s.parse().ok()).unwrap_or(seeds);
- }
- "--episodes" => {
- i += 1;
- episodes = args.get(i).and_then(|s| s.parse().ok()).unwrap_or(episodes);
- }
- "--out" => {
- i += 1;
- if let Some(p) = args.get(i) {
- out = PathBuf::from(p);
- }
- }
- "--help" | "-h" => {
- eprintln!(
- "eval_swarm — ADR-171 Stage-1 kinematic evaluator\n\
- Usage: eval_swarm [--seeds N] [--episodes M] [--out PATH]\n\
- Defaults: --seeds 10 --episodes 10 --out crates/ruview-swarm/evals/RESULTS.md"
- );
- return;
- }
- other => {
- eprintln!("warning: ignoring unknown argument '{other}'");
- }
- }
- i += 1;
- }
-
- eprintln!(
- "Running ADR-171 Stage-1 eval: {seeds} seeds × {episodes} episodes \
- over {} flight patterns...",
- FlightPattern::all().len()
- );
-
- let mut rows: Vec<(String, AggregateMetrics)> = Vec::new();
- for (idx, pattern) in FlightPattern::all().into_iter().enumerate() {
- let mut cfg = EvalConfig::sar_small(pattern);
- cfg.seeds = seeds;
- cfg.episodes_per_seed = episodes;
- let matrix = run_matrix(&cfg);
- let agg = AggregateMetrics::from_strata(&matrix, 0x0149 ^ idx as u64);
- eprintln!(
- " {}: coverage IQM {:.3}, detection {:.0}%",
- pattern.name(),
- agg.coverage_iqm.point,
- agg.detection_rate * 100.0
- );
- rows.push((pattern.name().to_string(), agg));
- }
-
- // Rank by descending coverage point estimate.
- rows.sort_by(|a, b| {
- b.1.coverage_iqm
- .point
- .partial_cmp(&a.1.coverage_iqm.point)
- .unwrap_or(std::cmp::Ordering::Equal)
- });
-
- let md = render_results_md(&rows);
-
- if let Some(parent) = out.parent() {
- if let Err(e) = std::fs::create_dir_all(parent) {
- eprintln!("error: could not create {}: {e}", parent.display());
- std::process::exit(1);
- }
- }
- if let Err(e) = std::fs::write(&out, &md) {
- eprintln!("error: could not write {}: {e}", out.display());
- std::process::exit(1);
- }
- eprintln!("Wrote {} ({} bytes).", out.display(), md.len());
-}
diff --git a/v2/crates/ruview-swarm/src/bin/train_marl.rs b/v2/crates/ruview-swarm/src/bin/train_marl.rs
deleted file mode 100644
index 2f1e0504..00000000
--- a/v2/crates/ruview-swarm/src/bin/train_marl.rs
+++ /dev/null
@@ -1,474 +0,0 @@
-//! MARL training entry point for ruview-swarm (ADR-148 M4).
-//!
-//! Real Candle autodiff PPO training loop. Runs on CPU, or CUDA when built
-//! with `--features train,cuda` (local RTX 5080 or a GCP L4 instance).
-//!
-//! Movement is driven by a selectable `FlightPattern` (boustrophedon,
-//! partitioned, spiral, pheromone, potential, levy) and reward is shaped by a
-//! selectable `LearningPattern` (mappo, ippo, curiosity, meta). This makes each
-//! pattern produce visibly distinct trajectories + telemetry instead of every
-//! drone clustering on the orchestrator's internal coverage strategy.
-//!
-//! Usage:
-//! cargo run --release -p ruview-swarm --features train,cuda --bin train_marl -- \
-//! --episodes 5000 --drones 4 --profile sar \
-//! --flight-pattern partitioned --learn-pattern mappo_curiosity \
-//! --checkpoint-dir ./marl-checkpoints
-//!
-//! Right-sizing note: the policy is a 64→128→64 MLP. The bottleneck is
-//! environment-rollout throughput, not GPU matmul — an L4 + 16 vCPU beats an
-//! 8× A100 box for this workload at ~1/20th the cost. See scripts/gcp/.
-
-use std::collections::HashSet;
-
-use ruview_swarm::config::SwarmConfig;
-use ruview_swarm::integration::telemetry::{DroneFrame, TelemetryRecorder};
-use ruview_swarm::marl::candle_ppo::{CandlePpoConfig, CandleTrainer};
-use ruview_swarm::marl::learning::{shaped_reward, CuriosityModule, LearningPattern};
-use ruview_swarm::marl::observation::LocalObservation;
-use ruview_swarm::marl::reward::{RewardCalculator, RewardContext};
-use ruview_swarm::planning::patterns::{FlightPattern, PatternContext};
-use ruview_swarm::types::{DroneState, NodeId, Position3D, Velocity3D};
-
-struct Args {
- episodes: usize,
- drones: usize,
- profile: String,
- steps_per_episode: usize,
- checkpoint_dir: String,
- checkpoint_every: usize,
- telemetry: Option,
- telemetry_episode: usize,
- flight_pattern: String,
- learn_pattern: String,
-}
-
-impl Default for Args {
- fn default() -> Self {
- Self {
- episodes: 1000,
- drones: 4,
- profile: "sar".to_string(),
- steps_per_episode: 200,
- checkpoint_dir: "./marl-checkpoints".to_string(),
- checkpoint_every: 100,
- telemetry: None,
- telemetry_episode: 0,
- flight_pattern: "partitioned".to_string(),
- learn_pattern: "mappo".to_string(),
- }
- }
-}
-
-fn parse_args() -> Args {
- let mut args = Args::default();
- let argv: Vec = std::env::args().collect();
- let mut i = 1;
- while i < argv.len() {
- let next = || argv.get(i + 1).cloned().unwrap_or_default();
- match argv[i].as_str() {
- "--episodes" => {
- args.episodes = next().parse().unwrap_or(args.episodes);
- i += 1;
- }
- "--drones" => {
- args.drones = next().parse().unwrap_or(args.drones);
- i += 1;
- }
- "--profile" => {
- args.profile = next();
- i += 1;
- }
- "--steps" => {
- args.steps_per_episode = next().parse().unwrap_or(args.steps_per_episode);
- i += 1;
- }
- "--checkpoint-dir" => {
- args.checkpoint_dir = next();
- i += 1;
- }
- "--checkpoint-every" => {
- args.checkpoint_every = next().parse().unwrap_or(args.checkpoint_every);
- i += 1;
- }
- "--telemetry" => {
- args.telemetry = Some(next());
- i += 1;
- }
- "--telemetry-episode" => {
- args.telemetry_episode = next().parse().unwrap_or(args.telemetry_episode);
- i += 1;
- }
- "--flight-pattern" => {
- args.flight_pattern = next();
- i += 1;
- }
- "--learn-pattern" => {
- args.learn_pattern = next();
- i += 1;
- }
- "-h" | "--help" => {
- println!(
- "train_marl — ruview-swarm MARL training (ADR-148 M4)\n\
- \nOptions:\n \
- --episodes N training episodes (default 1000)\n \
- --drones N swarm size (default 4)\n \
- --profile NAME sar|inspection|mine|agriculture (default sar)\n \
- --steps N steps per episode (default 200)\n \
- --flight-pattern P boustrophedon|partitioned|spiral|pheromone|potential|levy (default partitioned)\n \
- --learn-pattern P mappo|ippo|curiosity|meta (default mappo)\n \
- --checkpoint-dir D checkpoint output dir (default ./marl-checkpoints)\n \
- --checkpoint-every N save every N episodes (default 100)\n \
- --telemetry FILE write JSONL telemetry for viz/swarm_viz.html\n \
- --telemetry-episode N which episode's steps to record spatially (default 0)"
- );
- std::process::exit(0);
- }
- other => eprintln!("warning: ignoring unknown arg {other}"),
- }
- i += 1;
- }
- args
-}
-
-fn config_for(profile: &str) -> SwarmConfig {
- match profile {
- "inspection" => SwarmConfig::inspection_default(),
- "mine" => SwarmConfig::mine_default(),
- "agriculture" => SwarmConfig::agriculture_default(),
- _ => SwarmConfig::wi2sar_reference(),
- }
-}
-
-/// Map a world coordinate to a grid cell index at `grid_res` metre resolution.
-fn cell_of(x: f64, y: f64, grid_res: f64) -> (u32, u32) {
- let gx = (x / grid_res).floor().max(0.0) as u32;
- let gy = (y / grid_res).floor().max(0.0) as u32;
- (gx, gy)
-}
-
-/// Mark every grid cell within the drone's circular scan footprint as scanned,
-/// returning how many *newly* scanned cells this step contributed.
-fn mark_scanned(
- scanned: &mut HashSet<(u32, u32)>,
- pos: &Position3D,
- scan_width_m: f64,
- grid_res: f64,
- area_w: f64,
- area_h: f64,
-) -> u32 {
- let r = scan_width_m * 0.5;
- let cols = (area_w / grid_res).ceil() as i64;
- let rows = (area_h / grid_res).ceil() as i64;
- let (cx, cy) = cell_of(pos.x, pos.y, grid_res);
- let span = (r / grid_res).ceil() as i64;
- let mut new_cells = 0u32;
- for dgx in -span..=span {
- for dgy in -span..=span {
- let gx = cx as i64 + dgx;
- let gy = cy as i64 + dgy;
- if gx < 0 || gy < 0 || gx >= cols || gy >= rows {
- continue;
- }
- // Cell centre in metres.
- let mx = (gx as f64 + 0.5) * grid_res;
- let my = (gy as f64 + 0.5) * grid_res;
- if (mx - pos.x).hypot(my - pos.y) <= r && scanned.insert((gx as u32, gy as u32)) {
- new_cells += 1;
- }
- }
- }
- new_cells
-}
-
-#[tokio::main]
-async fn main() -> Result<(), Box> {
- let args = parse_args();
- let cfg = config_for(&args.profile);
- let flight_pattern = FlightPattern::from_str(&args.flight_pattern);
- let learn_pattern = LearningPattern::from_str(&args.learn_pattern);
-
- println!(
- "MARL training: profile={} drones={} episodes={} steps/ep={} flight={} learn={} ({})",
- args.profile,
- args.drones,
- args.episodes,
- args.steps_per_episode,
- flight_pattern.name(),
- learn_pattern.name(),
- if learn_pattern.centralized_critic() {
- "CTDE / centralized critic"
- } else {
- "independent learners"
- }
- );
-
- let ppo_cfg = CandlePpoConfig::default();
- let mut trainer = CandleTrainer::new(ppo_cfg)?;
- println!("device: {:?}", trainer.net.device());
-
- let reward_calc = RewardCalculator::default();
- std::fs::create_dir_all(&args.checkpoint_dir).ok();
-
- let area_w = cfg.mission.area_width_m;
- let area_h = cfg.mission.area_height_m;
- let grid_res = cfg.mission.grid_resolution_m.max(1.0);
- let scan_w = cfg.planning.csi_scan_width_m;
- let max_speed = cfg.planning.max_speed_ms.max(0.1);
- let altitude_z = -cfg.planning.flight_altitude_m;
- let total_cells = ((area_w / grid_res).ceil() * (area_h / grid_res).ceil()).max(1.0);
-
- // Synthetic victims placed within the mission area for reward signal.
- let victims = vec![
- Position3D { x: area_w * 0.2, y: area_h * 0.3, z: 0.0 },
- Position3D { x: area_w * 0.6, y: area_h * 0.45, z: 0.0 },
- ];
-
- // Composite profile label so the viewer header surfaces the active patterns.
- let profile_label = format!(
- "{} · flight={} · learn={}",
- args.profile,
- flight_pattern.name(),
- learn_pattern.name()
- );
-
- // Optional telemetry recorder for the visualizer.
- let mut telem = match &args.telemetry {
- Some(path) => {
- let mut rec = TelemetryRecorder::create(path)?;
- rec.meta(&profile_label, args.drones, area_w, area_h, &victims)?;
- println!("telemetry → {path} (spatial steps from episode {})", args.telemetry_episode);
- Some(rec)
- }
- None => None,
- };
-
- let mut best_return = f32::MIN;
-
- for episode in 0..args.episodes {
- // Per-episode curiosity module (count-based novelty over the area).
- let mut curiosity = CuriosityModule::new(area_w, area_h, 32, 0.5);
-
- // Build drone states directly so the FlightPattern fully drives motion.
- let cols = (args.drones as f64).sqrt().ceil().max(1.0) as usize;
- let mut states: Vec = (0..args.drones)
- .map(|d| {
- let (row, col) = (d / cols, d % cols);
- let mut s = DroneState::default_at_origin(NodeId(d as u32));
- s.position = Position3D {
- x: 10.0 + col as f64 * (area_w / cols as f64),
- y: 10.0 + row as f64 * (area_h / cols.max(1) as f64),
- z: altitude_z,
- };
- s.altitude_agl_m = cfg.planning.flight_altitude_m;
- s
- })
- .collect();
-
- // Coverage tracker (shared across drones — total area scanned).
- let mut scanned: HashSet<(u32, u32)> = HashSet::new();
- // Rolling recent-positions trail for pheromone/potential patterns.
- let mut visited: Vec = Vec::with_capacity(256);
-
- // Rollout buffers (flattened across drones).
- let mut obs_buf: Vec = Vec::new();
- let mut action_buf: Vec<[f32; 4]> = Vec::new();
- let mut reward_buf: Vec = Vec::new();
- let mut value_buf: Vec = Vec::new();
- let mut done_buf: Vec = Vec::new();
-
- for step in 0..args.steps_per_episode {
- let is_last = step == args.steps_per_episode - 1;
-
- // Snapshot peer positions for this tick (observations + repulsion).
- let positions: Vec<(NodeId, Position3D)> =
- states.iter().map(|s| (s.id, s.position)).collect();
-
- // Index needed: mutates states[idx] while reading peer positions; borrow constraints.
- #[allow(clippy::needless_range_loop)]
- for idx in 0..states.len() {
- let prev_pos = states[idx].position;
- let node_id = states[idx].id;
-
- // Neighbour positions (everyone except this drone).
- let neighbors: Vec<(NodeId, Position3D)> = positions
- .iter()
- .filter(|(id, _)| *id != node_id)
- .cloned()
- .collect();
- let peers: Vec = neighbors.iter().map(|(_, p)| *p).collect();
-
- // Observation from the current (pre-move) state.
- let obs =
- LocalObservation::from_state_no_grid(&states[idx], &neighbors, None, None);
-
- // --- FlightPattern drives the next waypoint --------------------
- let ctx = PatternContext {
- drone_id: node_id,
- swarm_size: args.drones,
- current: prev_pos,
- area_w,
- area_h,
- altitude_z,
- scan_width_m: scan_w,
- step: step as u64,
- visited: &visited,
- peers: &peers,
- };
- let target = flight_pattern.next_target(&ctx);
-
- // Move one tick toward the target at max_speed (no teleport).
- let dx = target.x - prev_pos.x;
- let dy = target.y - prev_pos.y;
- let dist = dx.hypot(dy);
- let new_pos = if dist > 1e-9 {
- let stepd = dist.min(max_speed);
- Position3D {
- x: prev_pos.x + dx / dist * stepd,
- y: prev_pos.y + dy / dist * stepd,
- z: altitude_z,
- }
- } else {
- prev_pos
- };
- let heading = if dist > 1e-9 { dy.atan2(dx) } else { states[idx].heading_rad };
- let moved = prev_pos.distance_to(&new_pos);
-
- // Commit the move to the drone state.
- {
- let s = &mut states[idx];
- s.velocity = Velocity3D {
- vx: (new_pos.x - prev_pos.x),
- vy: (new_pos.y - prev_pos.y),
- vz: 0.0,
- };
- s.position = new_pos;
- s.heading_rad = heading;
- s.timestamp_ms = s.timestamp_ms.saturating_add(1000);
- }
-
- // Coverage: mark scanned footprint, count new cells.
- let new_cells =
- mark_scanned(&mut scanned, &new_pos, scan_w, grid_res, area_w, area_h);
-
- // Detection: any victim within the scan footprint.
- let detected = victims.iter().any(|v| new_pos.distance_to(v) < scan_w);
-
- // Nearest-neighbour distance (for collision shaping).
- let nearest = peers
- .iter()
- .map(|p| new_pos.distance_to(p))
- .fold(f64::MAX, f64::min);
-
- // Base extrinsic reward.
- let ctx_r = RewardContext {
- state: &states[idx],
- new_cells_covered: new_cells,
- victim_confirmed: detected,
- contributed_to_triangulation: false,
- nearest_neighbor_dist: nearest,
- geofence_breached: false,
- battery_depleted_without_rth: false,
- };
- let base = reward_calc.compute(&ctx_r);
-
- // Curiosity shaping (only when the learning pattern uses it).
- let reward = if learn_pattern.uses_curiosity() {
- let bonus = curiosity.visit_bonus(new_pos.x, new_pos.y);
- shaped_reward(learn_pattern, base, bonus)
- } else {
- base
- };
-
- let action = [
- heading as f32,
- states[idx].altitude_agl_m as f32,
- (moved / 1.0) as f32,
- 0.0,
- ];
-
- obs_buf.push(obs);
- action_buf.push(action);
- reward_buf.push(reward);
- value_buf.push(0.0); // bootstrap value (critic learns this)
- done_buf.push(is_last);
-
- // Record the move in the shared visited trail (cap length).
- visited.push(new_pos);
- }
-
- // Trim the visited trail to the most recent ~200 positions.
- if visited.len() > 200 {
- let drop = visited.len() - 200;
- visited.drain(0..drop);
- }
-
- // Record spatial telemetry for the selected episode only.
- if let Some(rec) = telem.as_mut() {
- if episode == args.telemetry_episode {
- let frames: Vec = states
- .iter()
- .map(|s| {
- let detected =
- victims.iter().any(|v| s.position.distance_to(v) < scan_w);
- DroneFrame::from_state(s, detected)
- })
- .collect();
- let coverage = scanned.len() as f64 / total_cells;
- let _ = rec.step(episode, step, step as f64, &frames, coverage);
- }
- }
- }
-
- // PPO update on the episode's rollout.
- let (advantages, returns) = trainer.compute_gae(&reward_buf, &value_buf, &done_buf);
- let old_log_probs = vec![0.0f32; obs_buf.len()];
- let (policy_loss, value_loss, _entropy) =
- trainer.update(&obs_buf, &action_buf, &advantages, &returns, &old_log_probs)?;
-
- let mean_return = if returns.is_empty() {
- 0.0
- } else {
- returns.iter().sum::() / returns.len() as f32
- };
-
- if mean_return > best_return {
- best_return = mean_return;
- }
-
- // Per-episode training-metric telemetry (every episode).
- if let Some(rec) = telem.as_mut() {
- let _ = rec.episode(episode, mean_return, policy_loss, value_loss, 0);
- }
-
- if episode % 10 == 0 || episode == args.episodes - 1 {
- let coverage_pct = scanned.len() as f64 / total_cells * 100.0;
- println!(
- "ep {:>5}/{} mean_return={:>8.3} best={:>8.3} policy_loss={:>8.4} value_loss={:>8.4} coverage={:>5.1}%",
- episode, args.episodes, mean_return, best_return, policy_loss, value_loss, coverage_pct
- );
- }
-
- // Checkpoint the trained variables periodically.
- if args.checkpoint_every > 0 && (episode + 1) % args.checkpoint_every == 0
- || episode == args.episodes - 1
- {
- let path = format!("{}/marl-ep{}.safetensors", args.checkpoint_dir, episode + 1);
- if let Err(e) = trainer.net.varmap().save(&path) {
- eprintln!("checkpoint save failed at {path}: {e}");
- } else {
- println!("checkpoint saved: {path}");
- }
- }
- }
-
- if let Some(rec) = telem.as_mut() {
- rec.flush()?;
- if let Some(path) = &args.telemetry {
- println!("telemetry written: {path} — open viz/swarm_viz.html and load it");
- }
- }
-
- println!("training complete. best mean_return={best_return:.3}");
- Ok(())
-}
diff --git a/v2/crates/ruview-swarm/src/config/mod.rs b/v2/crates/ruview-swarm/src/config/mod.rs
deleted file mode 100644
index 755bd1c0..00000000
--- a/v2/crates/ruview-swarm/src/config/mod.rs
+++ /dev/null
@@ -1,207 +0,0 @@
-//! TOML-based swarm configuration with mission profiles.
-
-use serde::{Deserialize, Serialize};
-
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct SwarmConfig {
- pub swarm: SwarmParams,
- pub formation: FormationConfig,
- pub planning: PlanningConfig,
- pub security: SecurityConfig,
- pub mission: MissionConfig,
- pub demo: Option,
-}
-
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct SwarmParams {
- pub max_agents: usize,
- pub cluster_size: usize,
- pub raft_election_timeout_ms: u64,
- pub raft_heartbeat_ms: u64,
- pub gossip_fanout: usize,
-}
-
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct FormationConfig {
- /// "virtual_structure" | "leader_follower" | "reynolds"
- pub mode: String,
- pub min_separation_m: f64,
- pub grid_spacing_m: f64,
-}
-
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct PlanningConfig {
- pub flight_altitude_m: f64,
- pub max_speed_ms: f64,
- /// Wi2SAR validated scan footprint width.
- pub csi_scan_width_m: f64,
- pub lateral_overlap_pct: f64,
- /// P(victim) threshold to trigger Phase 3 convergence.
- pub convergence_threshold: f32,
-}
-
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct SecurityConfig {
- pub mavlink_signing: bool,
- pub uwb_antispoofing: bool,
- pub uwb_tolerance_m: f64,
- pub geofence_hard_margin_m: f64,
- pub geofence_soft_margin_m: f64,
- /// Remote ID broadcast rate in Hz (FAA/EU requirement: ≥ 1 Hz).
- pub remote_id_broadcast_hz: f64,
-}
-
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct MissionConfig {
- /// "sar" | "inspection" | "agriculture" | "mine" | "relay"
- pub profile: String,
- pub area_width_m: f64,
- pub area_height_m: f64,
- pub grid_resolution_m: f64,
- pub max_flight_time_mins: f64,
-}
-
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct DemoConfig {
- pub synthetic_csi: bool,
- /// Victim positions in NED [x, y, z].
- pub victim_positions: Vec<[f64; 3]>,
- pub wind_noise_ms: f64,
- pub csi_noise_std: f64,
- pub packet_loss_pct: f64,
- pub replay_speed: f64,
-}
-
-impl SwarmConfig {
- pub fn from_toml_str(s: &str) -> Result {
- toml::from_str(s)
- }
-
- pub fn sar_default() -> Self {
- Self {
- swarm: SwarmParams {
- max_agents: 12,
- cluster_size: 4,
- raft_election_timeout_ms: 300,
- raft_heartbeat_ms: 100,
- gossip_fanout: 3,
- },
- formation: FormationConfig {
- mode: "virtual_structure".into(),
- min_separation_m: 5.0,
- grid_spacing_m: 20.0,
- },
- planning: PlanningConfig {
- flight_altitude_m: 30.0,
- max_speed_ms: 8.0,
- csi_scan_width_m: 28.0,
- lateral_overlap_pct: 20.0,
- convergence_threshold: 0.75,
- },
- security: SecurityConfig {
- mavlink_signing: true,
- uwb_antispoofing: true,
- uwb_tolerance_m: 2.0,
- geofence_hard_margin_m: 20.0,
- geofence_soft_margin_m: 50.0,
- remote_id_broadcast_hz: 1.0,
- },
- mission: MissionConfig {
- profile: "sar".into(),
- area_width_m: 500.0,
- area_height_m: 500.0,
- grid_resolution_m: 5.0,
- max_flight_time_mins: 25.0,
- },
- demo: None,
- }
- }
-
- pub fn inspection_default() -> Self {
- let mut cfg = Self::sar_default();
- cfg.mission.profile = "inspection".into();
- cfg.planning.flight_altitude_m = 15.0;
- cfg.planning.max_speed_ms = 4.0;
- cfg.formation.mode = "leader_follower".into();
- cfg
- }
-
- pub fn agriculture_default() -> Self {
- let mut cfg = Self::sar_default();
- cfg.mission.profile = "agriculture".into();
- cfg.planning.flight_altitude_m = 10.0;
- cfg.planning.max_speed_ms = 6.0;
- cfg.planning.csi_scan_width_m = 15.0;
- cfg.formation.mode = "virtual_structure".into();
- cfg.formation.grid_spacing_m = 12.0;
- cfg
- }
-
- pub fn mine_default() -> Self {
- let mut cfg = Self::sar_default();
- cfg.mission.profile = "mine".into();
- cfg.planning.flight_altitude_m = 5.0;
- cfg.planning.max_speed_ms = 2.0;
- cfg.security.uwb_antispoofing = true; // GPS-denied: UWB only
- cfg
- }
-
- /// Wi2SAR reference configuration (400×400 m, 8 m/s, 4 drones) for ADR-148 SOTA benchmark.
- /// Produces 223 s coverage estimate — below the 240 s (4-min) SOTA target.
- /// Source: Wi2SAR (arxiv 2604.09115): single drone, 160,000 m², 13.5 min.
- pub fn wi2sar_reference() -> Self {
- let mut cfg = Self::sar_default();
- cfg.mission.area_width_m = 400.0;
- cfg.mission.area_height_m = 400.0;
- cfg.planning.max_speed_ms = 8.0;
- cfg.planning.csi_scan_width_m = 28.0;
- cfg.planning.lateral_overlap_pct = 20.0;
- cfg
- }
-
- pub fn demo_default() -> Self {
- let mut cfg = Self::sar_default();
- cfg.demo = Some(DemoConfig {
- synthetic_csi: true,
- victim_positions: vec![[50.0, 80.0, 0.0], [150.0, 200.0, 0.0], [300.0, 100.0, 0.0]],
- wind_noise_ms: 2.0,
- csi_noise_std: 0.05,
- packet_loss_pct: 5.0,
- replay_speed: 1.0,
- });
- cfg
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- #[test]
- fn test_sar_default_serialization() {
- let cfg = SwarmConfig::sar_default();
- let toml_str = toml::to_string(&cfg).expect("serialize ok");
- let parsed = SwarmConfig::from_toml_str(&toml_str).expect("parse ok");
- assert_eq!(parsed.mission.profile, "sar");
- }
-
- #[test]
- fn test_demo_default_has_victims() {
- let cfg = SwarmConfig::demo_default();
- assert!(cfg.demo.is_some());
- assert_eq!(cfg.demo.unwrap().victim_positions.len(), 3);
- }
-
- #[test]
- fn test_wi2sar_reference_coverage_within_4min() {
- use crate::demo::scenario::DemoScenario;
- let scenario = DemoScenario {
- name: "Wi2SAR Reference".into(),
- config: SwarmConfig::wi2sar_reference(),
- num_drones: 4,
- victims: vec![],
- };
- let t = scenario.estimate_coverage_time_secs();
- assert!(t < 240.0, "4-drone Wi2SAR reference scenario: {}s should be < 240s (4 min SOTA)", t);
- }
-}
diff --git a/v2/crates/ruview-swarm/src/demo/mod.rs b/v2/crates/ruview-swarm/src/demo/mod.rs
deleted file mode 100644
index 621510f7..00000000
--- a/v2/crates/ruview-swarm/src/demo/mod.rs
+++ /dev/null
@@ -1,10 +0,0 @@
-//! Demo scenario runner — synthetic CSI with configurable victim positions.
-//!
-//! Wires together a [`SyntheticCsiGenerator`] and pre-built [`DemoScenario`]
-//! definitions for rapid scenario validation without real hardware.
-
-pub mod synthetic_csi;
-pub mod scenario;
-
-pub use synthetic_csi::SyntheticCsiGenerator;
-pub use scenario::{DemoScenario, ScenarioResult};
diff --git a/v2/crates/ruview-swarm/src/demo/scenario.rs b/v2/crates/ruview-swarm/src/demo/scenario.rs
deleted file mode 100644
index a9e0ee9d..00000000
--- a/v2/crates/ruview-swarm/src/demo/scenario.rs
+++ /dev/null
@@ -1,150 +0,0 @@
-//! Pre-built demo scenarios for rapid validation without hardware.
-//!
-//! Each scenario bundles a [`SwarmConfig`], victim positions, and a
-//! [`SyntheticCsiGenerator`] so integration tests can drive a complete
-//! swarm sim-loop with one call.
-
-use crate::{
- config::SwarmConfig,
- types::Position3D,
-};
-use super::synthetic_csi::SyntheticCsiGenerator;
-
-/// A self-contained demo scenario.
-pub struct DemoScenario {
- pub name: String,
- pub config: SwarmConfig,
- pub num_drones: usize,
- pub victims: Vec,
-}
-
-/// Aggregate results produced after running a scenario.
-#[derive(Debug, Clone)]
-pub struct ScenarioResult {
- pub victims_found: usize,
- pub victims_total: usize,
- pub coverage_time_secs: f64,
- pub localization_error_m: f64,
- pub collision_count: u32,
-}
-
-impl DemoScenario {
- /// Standard SAR rubble-field: 3 victims in a 400 × 400 m area.
- pub fn sar_rubble_field(num_drones: usize) -> Self {
- Self {
- name: "SAR Rubble Field".into(),
- config: SwarmConfig::demo_default(),
- num_drones,
- victims: vec![
- Position3D { x: 50.0, y: 80.0, z: 0.0 },
- Position3D { x: 150.0, y: 200.0, z: 0.0 },
- Position3D { x: 300.0, y: 100.0, z: 0.0 },
- ],
- }
- }
-
- /// Open-field search: single victim, easy detection conditions.
- pub fn open_field_search(num_drones: usize) -> Self {
- Self {
- name: "Open Field Search".into(),
- config: SwarmConfig::demo_default(),
- num_drones,
- victims: vec![
- Position3D { x: 200.0, y: 150.0, z: 0.0 },
- ],
- }
- }
-
- /// Mine/GPS-denied: victims in a narrow corridor, low speed.
- pub fn mine_corridor(num_drones: usize) -> Self {
- let mut cfg = SwarmConfig::mine_default();
- cfg.demo = Some(crate::config::DemoConfig {
- synthetic_csi: true,
- victim_positions: vec![[30.0, 10.0, -2.0], [80.0, 15.0, -2.0]],
- wind_noise_ms: 0.1,
- csi_noise_std: 0.08,
- packet_loss_pct: 10.0,
- replay_speed: 0.5,
- });
- Self {
- name: "Mine Corridor GPS-Denied".into(),
- config: cfg,
- num_drones,
- victims: vec![
- Position3D { x: 30.0, y: 10.0, z: -2.0 },
- Position3D { x: 80.0, y: 15.0, z: -2.0 },
- ],
- }
- }
-
- /// Build a [`SyntheticCsiGenerator`] from this scenario's config and victims.
- pub fn make_csi_generator(&self) -> SyntheticCsiGenerator {
- let (noise_std, detection_range_m) = self.config.demo.as_ref().map(|d| {
- (d.csi_noise_std, self.config.planning.csi_scan_width_m / 2.0)
- }).unwrap_or((0.05, 14.0));
-
- SyntheticCsiGenerator::new(self.victims.clone(), noise_std, detection_range_m)
- }
-
- /// Analytic estimate of coverage time (seconds) for this scenario.
- ///
- /// Formula: `area / (scan_strip × drones) / speed`
- ///
- /// where `scan_strip = csi_scan_width_m × (1 − lateral_overlap / 100)`.
- pub fn estimate_coverage_time_secs(&self) -> f64 {
- let p = &self.config.planning;
- let m = &self.config.mission;
- let area = m.area_width_m * m.area_height_m;
- let scan_strip = p.csi_scan_width_m * (1.0 - p.lateral_overlap_pct / 100.0);
- if scan_strip <= 0.0 || p.max_speed_ms <= 0.0 || self.num_drones == 0 {
- return f64::INFINITY;
- }
- let total_track_m = area / scan_strip;
- let per_drone_track = total_track_m / self.num_drones as f64;
- per_drone_track / p.max_speed_ms
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- #[test]
- fn test_sar_scenario_coverage_estimate_within_10min() {
- // 4-drone SAR swarm over 500 × 500 m at 8 m/s, 20% overlap, 28 m scan width.
- // Analytic upper bound: area / (scan_strip × drones × speed)
- // = 250_000 / (22.4 × 4 × 8) ≈ 349 s (< 600 s = 10 min battery limit).
- let scenario = DemoScenario::sar_rubble_field(4);
- let t = scenario.estimate_coverage_time_secs();
- assert!(
- t < 600.0,
- "4-drone SAR coverage estimate {t:.1} s exceeds 600 s (10 min) battery limit"
- );
- // Also verify the estimate is positive and finite.
- assert!(t > 0.0 && t.is_finite(), "coverage estimate {t} must be positive and finite");
- }
-
- #[test]
- fn test_open_field_single_victim() {
- let scenario = DemoScenario::open_field_search(2);
- assert_eq!(scenario.victims.len(), 1);
- assert_eq!(scenario.num_drones, 2);
- }
-
- #[test]
- fn test_mine_scenario_low_speed() {
- let scenario = DemoScenario::mine_corridor(2);
- assert!(
- scenario.config.planning.max_speed_ms <= 3.0,
- "mine scenario max speed should be ≤ 3 m/s, got {}",
- scenario.config.planning.max_speed_ms
- );
- }
-
- #[test]
- fn test_make_csi_generator_victims_match() {
- let scenario = DemoScenario::sar_rubble_field(4);
- let gen = scenario.make_csi_generator();
- assert_eq!(gen.victims.len(), scenario.victims.len());
- }
-}
diff --git a/v2/crates/ruview-swarm/src/demo/synthetic_csi.rs b/v2/crates/ruview-swarm/src/demo/synthetic_csi.rs
deleted file mode 100644
index 59657ab2..00000000
--- a/v2/crates/ruview-swarm/src/demo/synthetic_csi.rs
+++ /dev/null
@@ -1,140 +0,0 @@
-//! Synthetic CSI generator — simulates WiFi CSI victim detections without hardware.
-//!
-//! Uses exponential distance decay and configurable Gaussian noise to produce
-//! realistic CsiDetection events for scenario testing and demo mode.
-
-use rand::Rng;
-use crate::types::{CsiDetection, NodeId, Position3D};
-
-/// Generates synthetic CSI detection events for a set of victim positions.
-pub struct SyntheticCsiGenerator {
- /// Ground-truth victim positions in NED metres.
- pub victims: Vec,
- /// Std-dev of additive Gaussian noise on confidence and position estimate.
- pub noise_std: f64,
- /// Maximum range (metres) at which a drone can detect a victim.
- pub detection_range_m: f64,
-}
-
-impl SyntheticCsiGenerator {
- pub fn new(victims: Vec, noise_std: f64, detection_range_m: f64) -> Self {
- Self { victims, noise_std, detection_range_m }
- }
-
- /// Attempt to detect a victim from the given drone position.
- ///
- /// Returns the strongest detection within range, or `None` if no victim
- /// is within `detection_range_m`. Confidence is modelled as
- /// `exp(-dist / range)` plus zero-mean Gaussian noise.
- pub fn detect(
- &self,
- drone_id: NodeId,
- drone_pos: &Position3D,
- timestamp_ms: u64,
- ) -> Option {
- let mut rng = rand::thread_rng();
- let mut best: Option = None;
-
- for victim in &self.victims {
- let dist = drone_pos.distance_to(victim);
- if dist >= self.detection_range_m {
- continue;
- }
- // Exponential decay: full confidence at 0 m, ~37% at 1× range
- let base_conf = (-dist / self.detection_range_m).exp();
- let noise: f64 = rng.gen_range(-self.noise_std..self.noise_std);
- let confidence = (base_conf + noise).clamp(0.0, 1.0) as f32;
-
- if confidence <= 0.4 {
- continue;
- }
-
- // Add positional noise proportional to noise_std
- let pos_jitter = self.noise_std * 10.0;
- let est_pos = Position3D {
- x: victim.x + rng.gen_range(-pos_jitter..pos_jitter),
- y: victim.y + rng.gen_range(-pos_jitter..pos_jitter),
- z: victim.z,
- };
-
- let det = CsiDetection {
- drone_id,
- confidence,
- victim_position: Some(est_pos),
- timestamp_ms,
- };
-
- // Keep the highest-confidence detection
- match &best {
- None => best = Some(det),
- Some(b) if det.confidence > b.confidence => best = Some(det),
- _ => {}
- }
- }
-
- best
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- #[test]
- fn test_detect_close_victim() {
- // A victim right on the drone should nearly always return a detection.
- // Run 20 trials; at least 15 should detect (0.4 threshold at distance 0).
- let gen = SyntheticCsiGenerator::new(
- vec![Position3D { x: 0.0, y: 0.0, z: 0.0 }],
- 0.01,
- 28.0,
- );
- let mut hits = 0u32;
- for i in 0..20 {
- if gen.detect(NodeId(0), &Position3D::zero(), i as u64).is_some() {
- hits += 1;
- }
- }
- assert!(hits >= 15, "expected ≥15/20 detections at zero range, got {hits}");
- }
-
- #[test]
- fn test_detect_beyond_range_returns_none() {
- let gen = SyntheticCsiGenerator::new(
- vec![Position3D { x: 0.0, y: 0.0, z: 0.0 }],
- 0.01,
- 28.0,
- );
- let far_pos = Position3D { x: 1000.0, y: 1000.0, z: 0.0 };
- // All 10 attempts should return None since drone is 1414 m away.
- for i in 0..10 {
- assert!(
- gen.detect(NodeId(0), &far_pos, i).is_none(),
- "expected no detection at 1414 m"
- );
- }
- }
-
- #[test]
- fn test_best_of_two_victims_returned() {
- // Two victims: one very close (high conf), one just at boundary (low conf).
- let gen = SyntheticCsiGenerator::new(
- vec![
- Position3D { x: 1.0, y: 0.0, z: 0.0 }, // close
- Position3D { x: 27.0, y: 0.0, z: 0.0 }, // near boundary
- ],
- 0.01,
- 28.0,
- );
- // Run 10 trials; whenever both return a detection the close one should win.
- for i in 0..10 {
- if let Some(det) = gen.detect(NodeId(0), &Position3D::zero(), i) {
- assert!(
- det.confidence >= 0.4,
- "returned confidence {:.3} is below threshold",
- det.confidence
- );
- }
- }
- }
-}
diff --git a/v2/crates/ruview-swarm/src/evals/gdop.rs b/v2/crates/ruview-swarm/src/evals/gdop.rs
deleted file mode 100644
index 200c3443..00000000
--- a/v2/crates/ruview-swarm/src/evals/gdop.rs
+++ /dev/null
@@ -1,118 +0,0 @@
-//! Geometric Dilution of Precision (GDOP) for a constellation of observers.
-//!
-//! GDOP quantifies how observer geometry amplifies measurement error into
-//! position-estimate error. Build the geometry matrix `H` of unit
-//! line-of-sight (LOS) vectors from each observer to the target, form the
-//! normal matrix `HᵀH`, invert it, and take `GDOP = sqrt(trace((HᵀH)⁻¹))`.
-//!
-//! For the 2-D (x, y) localization case `H` is `N×2` and `HᵀH` is `2×2`, so a
-//! closed-form 2×2 inverse suffices (no linear-algebra dependency needed).
-//!
-//! Lower GDOP = better geometry: observers spread ~120° apart around the target
-//! give low GDOP; (near-)collinear observers give a singular/ill-conditioned
-//! `HᵀH` → GDOP → ∞.
-
-use crate::types::Position3D;
-
-/// Geometric Dilution of Precision (2-D) for `observers` viewing a `target`.
-///
-/// Lower = better geometry. A ~120° constellation → low GDOP; collinear → very
-/// large (→∞). Returns `None` if fewer than two observers, if any observer is
-/// coincident with the target (undefined LOS), or if the geometry is singular
-/// / degenerate (collinear) so `HᵀH` is not invertible.
-pub fn gdop(observers: &[Position3D], target: &Position3D) -> Option {
- if observers.len() < 2 {
- return None;
- }
-
- // Accumulate HᵀH directly (2×2 symmetric) from unit LOS vectors.
- // Row i of H is the unit vector from target → observer i in (x, y).
- let mut a = 0.0; // sum ux*ux
- let mut b = 0.0; // sum ux*uy
- let mut d = 0.0; // sum uy*uy
-
- for obs in observers {
- let dx = obs.x - target.x;
- let dy = obs.y - target.y;
- let range = (dx * dx + dy * dy).sqrt();
- if range < 1e-9 {
- // Observer on top of the target → LOS undefined.
- return None;
- }
- let ux = dx / range;
- let uy = dy / range;
- a += ux * ux;
- b += ux * uy;
- d += uy * uy;
- }
-
- // Determinant of HᵀH = [[a, b], [b, d]].
- let det = a * d - b * b;
- if det.abs() < 1e-12 {
- // Singular: observers are (near-)collinear with the target.
- return None;
- }
-
- // (HᵀH)⁻¹ = 1/det * [[d, -b], [-b, a]]; trace = (d + a) / det.
- let trace_inv = (a + d) / det;
- if trace_inv <= 0.0 || !trace_inv.is_finite() {
- return None;
- }
- Some(trace_inv.sqrt())
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- fn p(x: f64, y: f64) -> Position3D {
- Position3D { x, y, z: 0.0 }
- }
-
- #[test]
- fn test_triangle_lower_than_collinear() {
- let target = p(0.0, 0.0);
- // Three observers at 120° around the target, radius 10.
- let r = 10.0;
- let triangle = [
- p(r * 0.0_f64.cos(), r * 0.0_f64.sin()),
- p(
- r * (2.0 * std::f64::consts::PI / 3.0).cos(),
- r * (2.0 * std::f64::consts::PI / 3.0).sin(),
- ),
- p(
- r * (4.0 * std::f64::consts::PI / 3.0).cos(),
- r * (4.0 * std::f64::consts::PI / 3.0).sin(),
- ),
- ];
- // Three nearly-collinear observers (tiny y perturbation to stay invertible).
- let near_collinear = [p(5.0, 0.01), p(10.0, 0.0), p(15.0, 0.01)];
-
- let tri = gdop(&triangle, &target).expect("triangle finite GDOP");
- let col = gdop(&near_collinear, &target).expect("near-collinear finite GDOP");
- assert!(tri.is_finite(), "triangle GDOP must be finite: {tri}");
- assert!(
- tri < col,
- "120° constellation should have lower GDOP than near-collinear: tri={tri}, col={col}"
- );
- }
-
- #[test]
- fn test_collinear_degenerate() {
- let target = p(0.0, 0.0);
- // Perfectly collinear observers along +x → singular HᵀH.
- let collinear = [p(5.0, 0.0), p(10.0, 0.0), p(20.0, 0.0)];
- let g = gdop(&collinear, &target);
- assert!(
- g.is_none() || g.unwrap() > 1e6,
- "perfectly collinear geometry must be None or huge, got {g:?}"
- );
- }
-
- #[test]
- fn test_single_observer_none() {
- let target = p(0.0, 0.0);
- assert!(gdop(&[p(5.0, 5.0)], &target).is_none());
- assert!(gdop(&[], &target).is_none());
- }
-}
diff --git a/v2/crates/ruview-swarm/src/evals/metrics.rs b/v2/crates/ruview-swarm/src/evals/metrics.rs
deleted file mode 100644
index adac5850..00000000
--- a/v2/crates/ruview-swarm/src/evals/metrics.rs
+++ /dev/null
@@ -1,150 +0,0 @@
-//! Per-episode and aggregate SAR + MARL metrics (ADR-171 Stage 1).
-
-use crate::evals::stats::{stratified_bootstrap_ci, ConfidenceInterval};
-
-/// Per-episode SAR metrics (Stage 1 kinematic).
-#[derive(Debug, Clone)]
-pub struct EpisodeMetrics {
- /// Fraction of the mission area scanned at least once, in [0, 1].
- pub coverage_pct: f64,
- /// Localization error (m) of the fused victim estimate; `None` if no detection.
- pub localization_error_m: Option,
- /// GDOP of the contributing-drone constellation at detection; `None` if none.
- pub gdop_at_detection: Option,
- /// Mission-elapsed seconds to first detection; `None` if no detection.
- pub time_to_first_detection_s: Option,
- /// Whether at least one victim was detected this episode.
- pub detected: bool,
- /// Count of inter-drone proximity violations (kinematic proxy for collisions).
- pub collisions: u32,
- /// Fraction of scanned area covered by more than one drone, in [0, 1].
- pub overlap_ratio: f64,
- /// Scalar episodic return (reward-like coverage/detection objective).
- pub episodic_return: f64,
-}
-
-/// Aggregate over a seed × episode matrix with IQM + 95% bootstrap CIs.
-#[derive(Debug, Clone)]
-pub struct AggregateMetrics {
- pub coverage_iqm: ConfidenceInterval,
- /// IQM over detected episodes only (undetected episodes carry no error).
- pub localization_iqm: ConfidenceInterval,
- pub detection_rate: f64,
- pub mean_gdop: f64,
- pub return_iqm: ConfidenceInterval,
- pub n_episodes: usize,
-}
-
-impl AggregateMetrics {
- /// Aggregate a seed-stratified matrix of episodes. Each inner `Vec` is one
- /// seed's episodes; bootstrap resampling is stratified by seed so the CI
- /// reflects between-seed variance (the dominant source per ADR-171).
- pub fn from_strata(per_seed: &[Vec], boot_seed: u64) -> Self {
- const N_BOOT: usize = 1000;
-
- let coverage_strata: Vec> = per_seed
- .iter()
- .map(|s| s.iter().map(|e| e.coverage_pct).collect())
- .collect();
- let return_strata: Vec> = per_seed
- .iter()
- .map(|s| s.iter().map(|e| e.episodic_return).collect())
- .collect();
- // Localization: only detected episodes contribute. Keep stratification
- // by seed but drop empty strata so the bootstrap doesn't degenerate.
- let loc_strata: Vec> = per_seed
- .iter()
- .map(|s| {
- s.iter()
- .filter_map(|e| e.localization_error_m)
- .collect::>()
- })
- .filter(|v: &Vec| !v.is_empty())
- .collect();
-
- let mut detected = 0usize;
- let mut total = 0usize;
- let mut gdop_sum = 0.0;
- let mut gdop_n = 0usize;
- for seed in per_seed {
- for e in seed {
- total += 1;
- if e.detected {
- detected += 1;
- }
- if let Some(g) = e.gdop_at_detection {
- if g.is_finite() {
- gdop_sum += g;
- gdop_n += 1;
- }
- }
- }
- }
-
- let detection_rate = if total == 0 {
- 0.0
- } else {
- detected as f64 / total as f64
- };
- let mean_gdop = if gdop_n == 0 {
- 0.0
- } else {
- gdop_sum / gdop_n as f64
- };
-
- AggregateMetrics {
- coverage_iqm: stratified_bootstrap_ci(&coverage_strata, N_BOOT, boot_seed),
- localization_iqm: stratified_bootstrap_ci(
- &loc_strata,
- N_BOOT,
- boot_seed.wrapping_add(1),
- ),
- detection_rate,
- mean_gdop,
- return_iqm: stratified_bootstrap_ci(
- &return_strata,
- N_BOOT,
- boot_seed.wrapping_add(2),
- ),
- n_episodes: total,
- }
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- fn ep(cov: f64, loc: Option, ret: f64, detected: bool) -> EpisodeMetrics {
- EpisodeMetrics {
- coverage_pct: cov,
- localization_error_m: loc,
- gdop_at_detection: if detected { Some(2.0) } else { None },
- time_to_first_detection_s: if detected { Some(10.0) } else { None },
- detected,
- collisions: 0,
- overlap_ratio: 0.1,
- episodic_return: ret,
- }
- }
-
- #[test]
- fn test_aggregate_detection_rate_and_shape() {
- let per_seed = vec![
- vec![
- ep(0.8, Some(1.5), 80.0, true),
- ep(0.7, None, 70.0, false),
- ],
- vec![
- ep(0.9, Some(2.0), 90.0, true),
- ep(0.85, Some(1.0), 85.0, true),
- ],
- ];
- let agg = AggregateMetrics::from_strata(&per_seed, 7);
- assert_eq!(agg.n_episodes, 4);
- assert!((agg.detection_rate - 0.75).abs() < 1e-9);
- assert!(agg.coverage_iqm.lo <= agg.coverage_iqm.point);
- assert!(agg.coverage_iqm.point <= agg.coverage_iqm.hi);
- assert!(agg.mean_gdop > 0.0);
- }
-}
diff --git a/v2/crates/ruview-swarm/src/evals/mod.rs b/v2/crates/ruview-swarm/src/evals/mod.rs
deleted file mode 100644
index 4136355c..00000000
--- a/v2/crates/ruview-swarm/src/evals/mod.rs
+++ /dev/null
@@ -1,19 +0,0 @@
-//! ADR-171 statistically-rigorous evaluation harness (Stage 1, kinematic).
-//!
-//! Produces SAR + MARL metrics over a seeded N-seed × M-episode matrix with
-//! IQM + 95% stratified-bootstrap CIs, a (sigma, kappa) CSI-noise sweep, and
-//! GDOP-stratified localization error. Generates evals/RESULTS.md.
-//!
-//! Stage 2 (Gazebo/PX4 SITL high-fidelity, false-alarm + collision rate on the
-//! median seeds) is a follow-on — see ADR-171 §6.1.
-pub mod gdop;
-pub mod stats;
-pub mod metrics;
-pub mod runner;
-pub mod report;
-
-pub use gdop::gdop;
-pub use stats::{iqm, stratified_bootstrap_ci, ConfidenceInterval};
-pub use metrics::{EpisodeMetrics, AggregateMetrics};
-pub use runner::{EvalConfig, NoiseLevel, run_matrix};
-pub use report::render_results_md;
diff --git a/v2/crates/ruview-swarm/src/evals/report.rs b/v2/crates/ruview-swarm/src/evals/report.rs
deleted file mode 100644
index eb7c90a0..00000000
--- a/v2/crates/ruview-swarm/src/evals/report.rs
+++ /dev/null
@@ -1,120 +0,0 @@
-//! RESULTS.md leaderboard generator (ADR-171 Stage 1).
-
-use crate::evals::metrics::AggregateMetrics;
-use crate::evals::stats::ConfidenceInterval;
-
-/// Wi2SAR published localization baseline (paper-to-paper), metres.
-const WI2SAR_LOCALIZATION_M: f64 = 5.0;
-
-/// Format a CI as `point [lo, hi]` with two decimals.
-fn fmt_ci(ci: &ConfidenceInterval) -> String {
- format!("{:.3} [{:.3}, {:.3}]", ci.point, ci.lo, ci.hi)
-}
-
-/// Render a markdown leaderboard: one row per flight pattern with coverage
-/// IQM±CI, localization IQM±CI, detection rate, and mean GDOP — plus the
-/// Wi2SAR paper baseline row clearly labelled paper-to-paper.
-///
-/// `rows` is `(pattern_name, aggregate)`; rows are emitted in the order given,
-/// so callers should pre-sort (e.g. by descending coverage point estimate).
-pub fn render_results_md(rows: &[(String, AggregateMetrics)]) -> String {
- let mut s = String::new();
- s.push_str("# ruview-swarm Evaluation Results (ADR-171 Stage 1, kinematic)\n\n");
- s.push_str(
- "Statistically-rigorous evaluation harness: seeded multi-run rollouts with \
- IQM + 95% stratified-bootstrap confidence intervals (Agarwal et al., \
- NeurIPS 2021).\n\n",
- );
-
- // Run configuration header.
- let (n_episodes, n_seeds) = rows
- .first()
- .map(|(_, a)| {
- let n = a.n_episodes;
- // Episodes-per-seed isn't stored; report total + leave seed split to caller note.
- (n, 0usize)
- })
- .unwrap_or((0, 0));
- s.push_str("## Run configuration\n\n");
- s.push_str(&format!(
- "- **Stage**: 1 (kinematic, self-contained, deterministic per seed)\n\
- - **Episodes per pattern**: {n_episodes} (seed × episode matrix)\n\
- - **CI method**: 95% stratified bootstrap of the IQM, stratified by seed\n\
- - **GDOP**: 2-D geometric dilution of precision at first detection\n"
- ));
- let _ = n_seeds;
- s.push_str(
- "\n> **Stage 2 pending**: high-fidelity Gazebo/PX4 SITL evaluation \
- (false-alarm rate, real collision rate on the median seeds) is a \
- follow-on — see ADR-171 §6.1. The collision figures below are a \
- kinematic min-separation proxy, not SITL physics.\n\n",
- );
-
- // Leaderboard table.
- s.push_str("## Flight-pattern leaderboard\n\n");
- s.push_str(
- "| Flight pattern | Coverage IQM [95% CI] | Localization (m) IQM [95% CI] | \
- Detection rate | Mean GDOP |\n",
- );
- s.push_str(
- "|----------------|-----------------------|-------------------------------|\
- ----------------|-----------|\n",
- );
- for (name, agg) in rows {
- s.push_str(&format!(
- "| {} | {} | {} | {:.1}% | {:.3} |\n",
- name,
- fmt_ci(&agg.coverage_iqm),
- fmt_ci(&agg.localization_iqm),
- agg.detection_rate * 100.0,
- agg.mean_gdop,
- ));
- }
- // Wi2SAR paper baseline row (paper-to-paper, no kinematic re-run).
- s.push_str(&format!(
- "| _Wi2SAR (paper baseline)_ | _n/a_ | _{:.1} (paper)_ | _n/a_ | _n/a_ |\n",
- WI2SAR_LOCALIZATION_M,
- ));
-
- s.push_str(
- "\n_Wi2SAR row is the published single-drone localization figure \
- (arxiv 2604.09115), shown paper-to-paper for reference only — it was \
- not re-run through this kinematic harness._\n",
- );
-
- s
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
- use crate::evals::stats::ConfidenceInterval;
-
- fn agg(cov: f64, det: f64) -> AggregateMetrics {
- let ci = |p: f64| ConfidenceInterval { point: p, lo: p - 0.05, hi: p + 0.05 };
- AggregateMetrics {
- coverage_iqm: ci(cov),
- localization_iqm: ci(1.5),
- detection_rate: det,
- mean_gdop: 2.1,
- return_iqm: ci(80.0),
- n_episodes: 100,
- }
- }
-
- #[test]
- fn test_render_contains_rows_and_baseline() {
- let rows = vec![
- ("partitioned_lawnmower".to_string(), agg(0.92, 0.95)),
- ("levy_flight".to_string(), agg(0.40, 0.50)),
- ];
- let md = render_results_md(&rows);
- assert!(md.contains("partitioned_lawnmower"));
- assert!(md.contains("levy_flight"));
- assert!(md.contains("Wi2SAR"));
- assert!(md.contains("Stage 2 pending"));
- assert!(md.contains("95% stratified bootstrap"));
- // Coverage point estimate appears.
- assert!(md.contains("0.920"));
- }
-}
diff --git a/v2/crates/ruview-swarm/src/evals/runner.rs b/v2/crates/ruview-swarm/src/evals/runner.rs
deleted file mode 100644
index fb210ce5..00000000
--- a/v2/crates/ruview-swarm/src/evals/runner.rs
+++ /dev/null
@@ -1,364 +0,0 @@
-//! Stage-1 kinematic rollout + seed × episode matrix (ADR-171).
-//!
-//! A single `run_episode` deterministically drives `drones` drones across a
-//! mission area under a chosen [`FlightPattern`], marks coverage on a grid,
-//! simulates CSI victim detection perturbed by `(sigma, kappa)` amplitude /
-//! von-Mises-phase noise, and computes the GDOP of the contributing-drone
-//! constellation at first detection. It is self-contained and seeded — no
-//! Candle / training backend required — so it runs in CI by default.
-
-use crate::config::SwarmConfig;
-use crate::evals::gdop::gdop;
-use crate::evals::metrics::EpisodeMetrics;
-use crate::planning::patterns::{FlightPattern, PatternContext};
-use crate::types::{NodeId, Position3D};
-
-/// CSI-noise level: amplitude std `sigma` and von-Mises phase concentration `kappa`.
-/// Higher `sigma` = noisier amplitude; *lower* `kappa` = noisier phase (more diffuse).
-#[derive(Debug, Clone, Copy)]
-pub struct NoiseLevel {
- pub sigma: f64,
- pub kappa: f64,
-}
-
-/// One evaluation configuration: a flight pattern + swarm/mission parameters.
-#[derive(Debug, Clone)]
-pub struct EvalConfig {
- pub flight: FlightPattern,
- pub config: SwarmConfig,
- pub drones: usize,
- pub steps: usize,
- pub seeds: usize, // ≥10 per ADR-171
- pub episodes_per_seed: usize, // e.g. 50
- pub victims: Vec,
- pub noise: NoiseLevel,
-}
-
-impl EvalConfig {
- /// A small SAR default suitable for fast CI runs.
- pub fn sar_small(flight: FlightPattern) -> Self {
- EvalConfig {
- flight,
- config: SwarmConfig::sar_default(),
- drones: 4,
- steps: 120,
- seeds: 10,
- episodes_per_seed: 10,
- victims: vec![
- Position3D { x: 120.0, y: 90.0, z: 0.0 },
- Position3D { x: 320.0, y: 280.0, z: 0.0 },
- ],
- noise: NoiseLevel { sigma: 0.05, kappa: 8.0 },
- }
- }
-}
-
-/// Minimal reproducible LCG → f64 in [0, 1). Self-contained for determinism.
-struct Lcg(u64);
-impl Lcg {
- fn new(seed: u64) -> Self {
- Lcg(seed ^ 0xD1B5_4A32_D192_ED03)
- }
- #[inline]
- fn next_u64(&mut self) -> u64 {
- self.0 = self
- .0
- .wrapping_mul(6364136223846793005)
- .wrapping_add(1442695040888963407);
- self.0
- }
- #[inline]
- fn unit(&mut self) -> f64 {
- (self.next_u64() >> 11) as f64 / (1u64 << 53) as f64
- }
- /// Standard-normal sample via Box–Muller (deterministic).
- #[inline]
- fn normal(&mut self) -> f64 {
- let u1 = self.unit().max(1e-12);
- let u2 = self.unit();
- (-2.0 * u1.ln()).sqrt() * (2.0 * std::f64::consts::PI * u2).cos()
- }
-}
-
-/// Run one kinematic episode deterministically from `seed`.
-///
-/// Drives drones step-by-step by the flight pattern, marks a coarse coverage
-/// grid, and on the first step a drone comes within scan range of any victim
-/// records a fused localization estimate (weighted centroid of contributing
-/// drones' per-drone victim estimates, each perturbed by `(sigma, kappa)`
-/// noise) and the GDOP of those contributing drones.
-pub fn run_episode(cfg: &EvalConfig, seed: u64) -> EpisodeMetrics {
- let mut rng = Lcg::new(seed);
-
- let area_w = cfg.config.mission.area_width_m;
- let area_h = cfg.config.mission.area_height_m;
- let altitude_z = -cfg.config.planning.flight_altitude_m;
- let scan_width = cfg.config.planning.csi_scan_width_m.max(1.0);
- let min_sep = cfg.config.formation.min_separation_m.max(0.1);
- let n = cfg.drones.max(1);
-
- // Coverage grid sized so each cell ~= scan_width.
- let gx = ((area_w / scan_width).ceil() as usize).max(1);
- let gy = ((area_h / scan_width).ceil() as usize).max(1);
- let cell_w = area_w / gx as f64;
- let cell_h = area_h / gy as f64;
- let mut cover_count = vec![0u32; gx * gy];
-
- // Spread drones along the bottom edge with a small seeded jitter.
- let mut positions: Vec = (0..n)
- .map(|i| {
- let frac = (i as f64 + 0.5) / n as f64;
- Position3D {
- x: (frac * area_w + (rng.unit() - 0.5) * scan_width).clamp(0.0, area_w),
- y: (rng.unit() * scan_width).clamp(0.0, area_h),
- z: altitude_z,
- }
- })
- .collect();
-
- // Recent-visit ring buffer for pheromone / potential-field patterns.
- let mut visited: Vec = Vec::new();
- let max_visited = 32usize;
-
- let scan_range = scan_width; // detect a victim within one scan footprint
- let mut collisions = 0u32;
- let mut detected = false;
- let mut loc_error: Option = None;
- let mut gdop_val: Option = None;
- let mut t_detect: Option = None;
-
- let dt = step_seconds(cfg);
-
- for step in 0..cfg.steps {
- // Advance each drone one waypoint under the pattern.
- let snapshot = positions.clone();
- for (i, pos) in positions.iter_mut().enumerate() {
- let peers: Vec = snapshot
- .iter()
- .enumerate()
- .filter(|(j, _)| *j != i)
- .map(|(_, p)| *p)
- .collect();
- let ctx = PatternContext {
- drone_id: NodeId(i as u32),
- swarm_size: n,
- current: *pos,
- area_w,
- area_h,
- altitude_z,
- scan_width_m: scan_width,
- step: step as u64,
- visited: &visited,
- peers: &peers,
- };
- *pos = cfg.flight.next_target(&ctx);
- }
-
- // Mark coverage + record visits.
- for pos in &positions {
- let cx = ((pos.x / cell_w).floor() as i64).clamp(0, gx as i64 - 1) as usize;
- let cy = ((pos.y / cell_h).floor() as i64).clamp(0, gy as i64 - 1) as usize;
- cover_count[cy * gx + cx] = cover_count[cy * gx + cx].saturating_add(1);
- visited.push(*pos);
- }
- if visited.len() > max_visited {
- let drop = visited.len() - max_visited;
- visited.drain(0..drop);
- }
-
- // Proximity / collision check (kinematic proxy).
- for a in 0..positions.len() {
- for b in (a + 1)..positions.len() {
- let d = positions[a].distance_to(&positions[b]);
- if d < min_sep {
- collisions = collisions.saturating_add(1);
- }
- }
- }
-
- // Detection: first step any victim falls within scan range of ≥1 drone,
- // fuse a localization estimate from the contributing drones. A single
- // contributor still yields a (noisier) estimate; GDOP is only defined
- // for the multistatic ≥2-drone case and is `None` otherwise.
- if !detected {
- for victim in &cfg.victims {
- let contributors: Vec = positions
- .iter()
- .filter(|p| horiz_dist(p, victim) <= scan_range)
- .copied()
- .collect();
- if !contributors.is_empty() {
- let (est, g) = fuse_estimate(&contributors, victim, cfg.noise, &mut rng);
- loc_error = Some(horiz_dist(&est, victim));
- gdop_val = g; // None for a single contributor
- t_detect = Some((step as f64 + 1.0) * dt);
- detected = true;
- break;
- }
- }
- }
- }
-
- // Coverage + overlap.
- let total_cells = (gx * gy) as f64;
- let scanned = cover_count.iter().filter(|&&c| c > 0).count() as f64;
- let overlapped = cover_count.iter().filter(|&&c| c > 1).count() as f64;
- let coverage_pct = if total_cells > 0.0 { scanned / total_cells } else { 0.0 };
- let overlap_ratio = if scanned > 0.0 { overlapped / scanned } else { 0.0 };
-
- // Episodic return: reward coverage + detection, penalize overlap + collisions.
- let detect_bonus = if detected { 1.0 } else { 0.0 };
- let loc_term = match loc_error {
- Some(e) => (1.0 / (1.0 + e)).max(0.0),
- None => 0.0,
- };
- let episodic_return = 100.0 * coverage_pct + 30.0 * detect_bonus + 20.0 * loc_term
- - 10.0 * overlap_ratio
- - 5.0 * collisions as f64;
-
- EpisodeMetrics {
- coverage_pct,
- localization_error_m: loc_error,
- gdop_at_detection: gdop_val,
- time_to_first_detection_s: t_detect,
- detected,
- collisions,
- overlap_ratio,
- episodic_return,
- }
-}
-
-/// Per-step wall-clock seconds, derived from scan width and drone speed.
-fn step_seconds(cfg: &EvalConfig) -> f64 {
- let speed = cfg.config.planning.max_speed_ms.max(0.1);
- (cfg.config.planning.csi_scan_width_m.max(1.0) / speed).max(0.1)
-}
-
-/// Horizontal (x, y) distance, ignoring altitude.
-fn horiz_dist(a: &Position3D, b: &Position3D) -> f64 {
- (a.x - b.x).hypot(a.y - b.y)
-}
-
-/// Fuse contributing drones' per-drone victim estimates into a weighted
-/// centroid, perturbed by `(sigma, kappa)` CSI noise, and compute the GDOP of
-/// the contributing constellation.
-fn fuse_estimate(
- contributors: &[Position3D],
- victim: &Position3D,
- noise: NoiseLevel,
- rng: &mut Lcg,
-) -> (Position3D, Option) {
- // Phase noise std from von Mises concentration: sigma_phase ≈ 1/sqrt(kappa).
- let phase_std = 1.0 / noise.kappa.max(1e-3).sqrt();
- let mut sx = 0.0;
- let mut sy = 0.0;
- let mut wsum = 0.0;
- for c in contributors {
- let range = horiz_dist(c, victim).max(1e-6);
- // Each drone's estimate = true victim + range-scaled amplitude noise +
- // bearing error from phase noise (perpendicular to LOS).
- let amp = noise.sigma * range;
- let nx = rng.normal() * amp;
- let ny = rng.normal() * amp;
- // Bearing wobble: rotate LOS unit vector by a small phase-noise angle.
- let bearing = (victim.y - c.y).atan2(victim.x - c.x);
- let dtheta = rng.normal() * phase_std;
- let bx = range * (bearing + dtheta).cos();
- let by = range * (bearing + dtheta).sin();
- let est_x = c.x + bx + nx;
- let est_y = c.y + by + ny;
- // Inverse-range weighting: closer drones trusted more.
- let w = 1.0 / range;
- sx += est_x * w;
- sy += est_y * w;
- wsum += w;
- }
- let w = wsum.max(1e-9);
- let est = Position3D { x: sx / w, y: sy / w, z: 0.0 };
- let g = gdop(contributors, victim);
- (est, g)
-}
-
-/// Run the full seed × episode matrix → per-seed strata of [`EpisodeMetrics`].
-pub fn run_matrix(cfg: &EvalConfig) -> Vec> {
- (0..cfg.seeds)
- .map(|s| {
- (0..cfg.episodes_per_seed)
- .map(|e| {
- // Distinct deterministic seed per (seed, episode) cell.
- let cell_seed = (s as u64)
- .wrapping_mul(0x100_0000)
- .wrapping_add(e as u64)
- .wrapping_add(0xABCD);
- run_episode(cfg, cell_seed)
- })
- .collect()
- })
- .collect()
-}
-
-/// Standard ADR-171 noise sweep grid: cartesian product of σ × κ levels.
-pub fn default_noise_sweep() -> Vec {
- let sigmas = [0.02, 0.05, 0.10];
- let kappas = [16.0, 8.0, 4.0];
- let mut out = Vec::with_capacity(sigmas.len() * kappas.len());
- for &sigma in &sigmas {
- for &kappa in &kappas {
- out.push(NoiseLevel { sigma, kappa });
- }
- }
- out
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- #[test]
- fn test_run_episode_deterministic() {
- let cfg = EvalConfig::sar_small(FlightPattern::PartitionedLawnmower);
- let a = run_episode(&cfg, 12345);
- let b = run_episode(&cfg, 12345);
- assert_eq!(a.coverage_pct, b.coverage_pct);
- assert_eq!(a.detected, b.detected);
- assert_eq!(a.localization_error_m, b.localization_error_m);
- assert_eq!(a.collisions, b.collisions);
- assert_eq!(a.episodic_return, b.episodic_return);
- }
-
- #[test]
- fn test_partitioned_beats_levy_coverage() {
- let mut part = EvalConfig::sar_small(FlightPattern::PartitionedLawnmower);
- part.seeds = 3;
- part.episodes_per_seed = 5;
- let mut levy = part.clone();
- levy.flight = FlightPattern::LevyFlight;
-
- let part_m = run_matrix(&part);
- let levy_m = run_matrix(&levy);
- let part_agg = crate::evals::metrics::AggregateMetrics::from_strata(&part_m, 1);
- let levy_agg = crate::evals::metrics::AggregateMetrics::from_strata(&levy_m, 1);
- assert!(
- part_agg.coverage_iqm.point > levy_agg.coverage_iqm.point,
- "partitioned coverage {} should beat levy {}",
- part_agg.coverage_iqm.point,
- levy_agg.coverage_iqm.point
- );
- }
-
- #[test]
- fn test_matrix_shape() {
- let mut cfg = EvalConfig::sar_small(FlightPattern::Spiral);
- cfg.seeds = 4;
- cfg.episodes_per_seed = 6;
- let m = run_matrix(&cfg);
- assert_eq!(m.len(), 4);
- assert!(m.iter().all(|s| s.len() == 6));
- }
-
- #[test]
- fn test_noise_sweep_grid() {
- let sweep = default_noise_sweep();
- assert_eq!(sweep.len(), 9);
- }
-}
diff --git a/v2/crates/ruview-swarm/src/evals/stats.rs b/v2/crates/ruview-swarm/src/evals/stats.rs
deleted file mode 100644
index 4dc6315a..00000000
--- a/v2/crates/ruview-swarm/src/evals/stats.rs
+++ /dev/null
@@ -1,203 +0,0 @@
-//! Hand-rolled robust statistics for the evaluation harness (Agarwal 2021).
-//!
-//! Implements the interquartile mean (IQM), a 95% stratified-bootstrap
-//! confidence interval of the IQM, and the probability-of-improvement metric —
-//! the three statistics recommended by "Deep RL at the Edge of the
-//! Statistical Precipice" (Agarwal et al., NeurIPS 2021) for reporting
-//! few-seed RL results.
-//!
-//! All randomness comes from a local linear-congruential generator (LCG) seeded
-//! explicitly, so every CI is fully reproducible — no `thread_rng`, no clock.
-
-/// Interquartile mean: mean of the middle 50% of samples (drop the bottom 25%
-/// and the top 25%). Robust to outliers in either tail.
-///
-/// Small-N behaviour: with fewer than 4 samples the trim would empty the set,
-/// so it falls back to the plain arithmetic mean. An empty slice returns 0.0.
-pub fn iqm(samples: &[f64]) -> f64 {
- if samples.is_empty() {
- return 0.0;
- }
- if samples.len() < 4 {
- return samples.iter().sum::() / samples.len() as f64;
- }
- let mut sorted = samples.to_vec();
- sorted.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
- let n = sorted.len();
- let lo = n / 4; // trim bottom 25%
- let hi = n - lo; // trim top 25% (symmetric)
- let mid = &sorted[lo..hi];
- if mid.is_empty() {
- return sorted.iter().sum::() / n as f64;
- }
- mid.iter().sum::() / mid.len() as f64
-}
-
-/// A point estimate with its lower / upper 95% confidence bounds.
-#[derive(Debug, Clone, Copy)]
-pub struct ConfidenceInterval {
- pub point: f64,
- pub lo: f64,
- pub hi: f64,
-}
-
-/// Minimal reproducible LCG (Numerical Recipes constants) yielding f64 in [0,1).
-struct Lcg(u64);
-
-impl Lcg {
- fn new(seed: u64) -> Self {
- // Avoid a zero state collapsing the generator.
- Lcg(seed ^ 0x9E37_79B9_7F4A_7C15)
- }
- #[inline]
- fn next_u64(&mut self) -> u64 {
- self.0 = self
- .0
- .wrapping_mul(6364136223846793005)
- .wrapping_add(1442695040888963407);
- self.0
- }
- /// Uniform index in [0, n).
- #[inline]
- fn index(&mut self, n: usize) -> usize {
- if n == 0 {
- return 0;
- }
- (self.next_u64() >> 11) as usize % n
- }
-}
-
-/// 95% stratified-bootstrap CI of the IQM.
-///
-/// `strata` groups samples (one inner `Vec` per stratum, e.g. per task or per
-/// seed). Each bootstrap replicate resamples WITH replacement *within* each
-/// stratum (preserving the stratum sizes), pools all resampled values, and
-/// recomputes the IQM. Repeat `n_boot` times and take the 2.5 / 97.5
-/// percentiles for the CI bounds. The `point` estimate is the IQM of the pooled
-/// original samples. Deterministic for a fixed `seed`.
-pub fn stratified_bootstrap_ci(
- strata: &[Vec],
- n_boot: usize,
- seed: u64,
-) -> ConfidenceInterval {
- let pooled: Vec = strata.iter().flatten().copied().collect();
- let point = iqm(&pooled);
-
- if pooled.is_empty() || n_boot == 0 {
- return ConfidenceInterval { point, lo: point, hi: point };
- }
-
- let mut rng = Lcg::new(seed);
- let mut replicates = Vec::with_capacity(n_boot);
- let mut buf: Vec = Vec::with_capacity(pooled.len());
-
- for _ in 0..n_boot {
- buf.clear();
- for stratum in strata {
- let m = stratum.len();
- for _ in 0..m {
- buf.push(stratum[rng.index(m)]);
- }
- }
- replicates.push(iqm(&buf));
- }
-
- replicates.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
- let lo = percentile(&replicates, 2.5);
- let hi = percentile(&replicates, 97.5);
- ConfidenceInterval { point, lo, hi }
-}
-
-/// Linear-interpolated percentile of a pre-sorted slice. `p` in [0, 100].
-fn percentile(sorted: &[f64], p: f64) -> f64 {
- if sorted.is_empty() {
- return 0.0;
- }
- if sorted.len() == 1 {
- return sorted[0];
- }
- let rank = (p / 100.0) * (sorted.len() as f64 - 1.0);
- let lo = rank.floor() as usize;
- let hi = rank.ceil() as usize;
- if lo == hi {
- return sorted[lo];
- }
- let frac = rank - lo as f64;
- sorted[lo] * (1.0 - frac) + sorted[hi] * frac
-}
-
-/// Probability of improvement: P(a-sample > b-sample) over all pairs (Agarwal).
-///
-/// Counts each (a_i, b_j) pair where `a_i > b_j` as 1, a tie as 0.5, and
-/// normalizes by the pair count. 1.0 means `a` strictly dominates; ~0.5 means
-/// the two are statistically indistinguishable. Returns 0.5 if either is empty.
-pub fn probability_of_improvement(a: &[f64], b: &[f64]) -> f64 {
- if a.is_empty() || b.is_empty() {
- return 0.5;
- }
- let mut wins = 0.0;
- for &ai in a {
- for &bj in b {
- if ai > bj {
- wins += 1.0;
- } else if (ai - bj).abs() < f64::EPSILON {
- wins += 0.5;
- }
- }
- }
- wins / (a.len() as f64 * b.len() as f64)
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- #[test]
- fn test_iqm_trims_outliers() {
- // 0..=100 plus one extreme outlier; IQM should sit near the middle (~50),
- // not be dragged toward 1e9.
- let mut samples: Vec = (0..=100).map(|i| i as f64).collect();
- samples.push(1e9);
- let v = iqm(&samples);
- assert!(
- (40.0..=60.0).contains(&v),
- "IQM should be near the middle-50% mean (~50), got {v}"
- );
- }
-
- #[test]
- fn test_iqm_small() {
- // Fewer than 4 samples → plain mean.
- assert_eq!(iqm(&[2.0, 4.0]), 3.0);
- assert_eq!(iqm(&[10.0]), 10.0);
- assert_eq!(iqm(&[1.0, 2.0, 3.0]), 2.0);
- assert_eq!(iqm(&[]), 0.0);
- }
-
- #[test]
- fn test_bootstrap_ci_brackets_point() {
- let strata = vec![
- vec![1.0, 2.0, 3.0, 4.0, 5.0],
- vec![2.0, 3.0, 4.0, 5.0, 6.0],
- ];
- let ci = stratified_bootstrap_ci(&strata, 500, 42);
- assert!(ci.lo <= ci.point, "lo ≤ point: {} ≤ {}", ci.lo, ci.point);
- assert!(ci.point <= ci.hi, "point ≤ hi: {} ≤ {}", ci.point, ci.hi);
- // Deterministic: same seed → identical interval.
- let ci2 = stratified_bootstrap_ci(&strata, 500, 42);
- assert_eq!(ci.point, ci2.point);
- assert_eq!(ci.lo, ci2.lo);
- assert_eq!(ci.hi, ci2.hi);
- }
-
- #[test]
- fn test_prob_improvement_obvious() {
- assert_eq!(
- probability_of_improvement(&[10.0, 10.0, 10.0], &[0.0, 0.0, 0.0]),
- 1.0
- );
- // Identical samples → all ties → 0.5.
- let poi = probability_of_improvement(&[5.0, 5.0], &[5.0, 5.0]);
- assert!((poi - 0.5).abs() < 1e-9, "symmetric ties → ~0.5, got {poi}");
- }
-}
diff --git a/v2/crates/ruview-swarm/src/failsafe/mod.rs b/v2/crates/ruview-swarm/src/failsafe/mod.rs
deleted file mode 100644
index bcfa380e..00000000
--- a/v2/crates/ruview-swarm/src/failsafe/mod.rs
+++ /dev/null
@@ -1,191 +0,0 @@
-//! Fail-safe state machine: link loss, low battery, collision avoidance.
-
-use crate::types::DroneState;
-use serde::{Deserialize, Serialize};
-use std::time::Instant;
-
-/// Fail-safe operating state.
-#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
-pub enum FailSafeState {
- Nominal,
- AutonomousHold,
- LowBatteryWarn,
- ReturnToHome,
- EmergencyLand,
- EmergencyDiverge,
- ControlledDescent,
-}
-
-/// State machine driving fail-safe transitions.
-pub struct FailSafeMachine {
- state: FailSafeState,
- link_loss_start: Option,
- pub link_loss_hold_secs: f64,
- pub link_loss_rth_secs: f64,
- pub battery_warn_pct: f32,
- pub battery_rth_pct: f32,
- pub collision_dist_m: f64,
-}
-
-impl FailSafeMachine {
- pub fn new() -> Self {
- Self {
- state: FailSafeState::Nominal,
- link_loss_start: None,
- link_loss_hold_secs: 3.0,
- link_loss_rth_secs: 30.0,
- battery_warn_pct: 20.0,
- battery_rth_pct: 15.0,
- collision_dist_m: 1.5,
- }
- }
-
- /// Drive one tick. Returns the current state after evaluation.
- pub fn tick(
- &mut self,
- state: &DroneState,
- link_alive: bool,
- nearest_neighbor_dist: f64,
- ) -> FailSafeState {
- // Collision avoidance has highest priority.
- //
- // Fail CLOSED on a non-finite neighbour distance. `nearest_neighbor_dist`
- // is derived from peer positions (see
- // `SwarmOrchestrator::nearest_peer_distance`), which arrive over the
- // untrusted swarm comm layer as `DroneState` values whose f64 position
- // fields can deserialize to NaN/Inf. A naive `NaN < collision_dist_m`
- // evaluates to `false`, silently DISABLING collision avoidance — the
- // worst possible failure for a physical drone. Treat a non-finite
- // distance as "too close" so the swarm diverges rather than trusting a
- // poisoned reading.
- if !nearest_neighbor_dist.is_finite() || nearest_neighbor_dist < self.collision_dist_m {
- self.state = FailSafeState::EmergencyDiverge;
- return self.state.clone();
- }
-
- // Link loss handling
- if !link_alive {
- let start = self.link_loss_start.get_or_insert_with(Instant::now);
- let elapsed = start.elapsed().as_secs_f64();
- if elapsed > self.link_loss_rth_secs {
- self.state = FailSafeState::ReturnToHome;
- } else if elapsed > self.link_loss_hold_secs {
- self.state = FailSafeState::AutonomousHold;
- }
- return self.state.clone();
- } else {
- // Link restored
- self.link_loss_start = None;
- if self.state == FailSafeState::AutonomousHold {
- self.state = FailSafeState::Nominal;
- }
- }
-
- // Battery checks. A non-finite battery reading (NaN/Inf from a corrupt or
- // forged telemetry/peer message) must fail CLOSED: `NaN <= threshold` is
- // `false`, which would otherwise let a drone with an unknown battery
- // level keep flying nominally. Treat a non-finite reading as critical.
- if !state.battery_pct.is_finite() || state.battery_pct <= self.battery_rth_pct {
- self.state = FailSafeState::ReturnToHome;
- } else if state.battery_pct <= self.battery_warn_pct {
- self.state = FailSafeState::LowBatteryWarn;
- } else if self.state == FailSafeState::LowBatteryWarn {
- // Recovered from low battery (charged on the fly / wrong reading)
- self.state = FailSafeState::Nominal;
- }
-
- self.state.clone()
- }
-
- pub fn current(&self) -> &FailSafeState {
- &self.state
- }
-
- pub fn force_land(&mut self) {
- self.state = FailSafeState::EmergencyLand;
- }
-}
-
-impl Default for FailSafeMachine {
- fn default() -> Self {
- Self::new()
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
- use crate::types::NodeId;
-
- fn good_state() -> DroneState {
- let mut s = DroneState::default_at_origin(NodeId(1));
- s.battery_pct = 80.0;
- s.link_quality = 1.0;
- s
- }
-
- #[test]
- fn test_nominal_when_healthy() {
- let mut fsm = FailSafeMachine::new();
- let s = good_state();
- let result = fsm.tick(&s, true, 10.0);
- assert_eq!(result, FailSafeState::Nominal);
- }
-
- #[test]
- fn test_low_battery_warn() {
- let mut fsm = FailSafeMachine::new();
- let mut s = good_state();
- s.battery_pct = 18.0;
- let result = fsm.tick(&s, true, 10.0);
- assert_eq!(result, FailSafeState::LowBatteryWarn);
- }
-
- #[test]
- fn test_battery_rth() {
- let mut fsm = FailSafeMachine::new();
- let mut s = good_state();
- s.battery_pct = 10.0;
- let result = fsm.tick(&s, true, 10.0);
- assert_eq!(result, FailSafeState::ReturnToHome);
- }
-
- #[test]
- fn test_collision_avoidance() {
- let mut fsm = FailSafeMachine::new();
- let s = good_state();
- let result = fsm.tick(&s, true, 0.5); // too close
- assert_eq!(result, FailSafeState::EmergencyDiverge);
- }
-
- /// Security: a NaN neighbour distance (poisoned peer position over the swarm
- /// comm layer) must NOT silently disable collision avoidance. Fails on old
- /// code where `NaN < collision_dist_m` is `false` and the state stays Nominal.
- #[test]
- fn test_nan_neighbor_distance_fails_closed_to_diverge() {
- let mut fsm = FailSafeMachine::new();
- let s = good_state();
- let result = fsm.tick(&s, true, f64::NAN);
- assert_eq!(
- result,
- FailSafeState::EmergencyDiverge,
- "non-finite neighbour distance must fail closed to EmergencyDiverge"
- );
- }
-
- /// Security: a NaN battery reading must fail closed to ReturnToHome rather
- /// than being treated as a healthy battery. Fails on old code where
- /// `NaN <= battery_rth_pct` is `false` and the drone stays Nominal.
- #[test]
- fn test_nan_battery_fails_closed_to_rth() {
- let mut fsm = FailSafeMachine::new();
- let mut s = good_state();
- s.battery_pct = f32::NAN;
- let result = fsm.tick(&s, true, 10.0);
- assert_eq!(
- result,
- FailSafeState::ReturnToHome,
- "non-finite battery must fail closed to ReturnToHome"
- );
- }
-}
diff --git a/v2/crates/ruview-swarm/src/formation/leader_follower.rs b/v2/crates/ruview-swarm/src/formation/leader_follower.rs
deleted file mode 100644
index e319e534..00000000
--- a/v2/crates/ruview-swarm/src/formation/leader_follower.rs
+++ /dev/null
@@ -1,74 +0,0 @@
-//! Leader-follower formation: followers maintain offsets relative to a leader drone.
-
-use crate::types::{NodeId, Position3D};
-use serde::{Deserialize, Serialize};
-use std::collections::HashMap;
-
-/// Leader-follower formation parameters.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct LeaderFollower {
- pub leader_id: NodeId,
- /// Follower → (dx, dy, dz) offset from leader's position.
- pub offsets: HashMap,
-}
-
-impl LeaderFollower {
- pub fn new(leader_id: NodeId) -> Self {
- Self {
- leader_id,
- offsets: HashMap::new(),
- }
- }
-
- pub fn add_follower(&mut self, follower: NodeId, offset: (f64, f64, f64)) {
- self.offsets.insert(follower, offset);
- }
-
- /// Compute target position for a node given current drone positions.
- pub fn target_position(
- &self,
- node_id: NodeId,
- positions: &[(NodeId, Position3D)],
- ) -> Position3D {
- // The leader tracks its own position.
- if node_id == self.leader_id {
- return positions
- .iter()
- .find(|(id, _)| *id == self.leader_id)
- .map(|(_, p)| *p)
- .unwrap_or_default();
- }
- let leader_pos = positions
- .iter()
- .find(|(id, _)| *id == self.leader_id)
- .map(|(_, p)| *p)
- .unwrap_or_default();
-
- if let Some(&(dx, dy, dz)) = self.offsets.get(&node_id) {
- Position3D {
- x: leader_pos.x + dx,
- y: leader_pos.y + dy,
- z: leader_pos.z + dz,
- }
- } else {
- leader_pos
- }
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- #[test]
- fn test_follower_tracks_leader() {
- let mut lf = LeaderFollower::new(NodeId(0));
- lf.add_follower(NodeId(1), (-5.0, 0.0, 0.0));
- let positions = vec![
- (NodeId(0), Position3D { x: 10.0, y: 20.0, z: -30.0 }),
- ];
- let target = lf.target_position(NodeId(1), &positions);
- assert!((target.x - 5.0).abs() < 1e-6);
- assert!((target.y - 20.0).abs() < 1e-6);
- }
-}
diff --git a/v2/crates/ruview-swarm/src/formation/mod.rs b/v2/crates/ruview-swarm/src/formation/mod.rs
deleted file mode 100644
index a70fe2cb..00000000
--- a/v2/crates/ruview-swarm/src/formation/mod.rs
+++ /dev/null
@@ -1,26 +0,0 @@
-//! Formation control: virtual structure, leader-follower, Reynolds flocking.
-//!
-// NOTE: Formation control is ITAR-controlled (USML Category VIII(h)(12)).
-// Only available when the `itar-unrestricted` feature is enabled.
-
-#[cfg(feature = "itar-unrestricted")]
-pub mod virtual_structure;
-#[cfg(feature = "itar-unrestricted")]
-pub mod leader_follower;
-#[cfg(feature = "itar-unrestricted")]
-pub mod reynolds;
-
-#[cfg(feature = "itar-unrestricted")]
-pub use virtual_structure::VirtualStructure;
-#[cfg(feature = "itar-unrestricted")]
-pub use leader_follower::LeaderFollower;
-#[cfg(feature = "itar-unrestricted")]
-pub use reynolds::ReynoldsParams;
-
-/// Stub: formation control is export-controlled. Enable `itar-unrestricted` feature.
-#[cfg(not(feature = "itar-unrestricted"))]
-pub fn formation_stub() -> crate::SwarmResult<()> {
- Err(crate::SwarmError::Security(
- "Formation control requires itar-unrestricted feature (USML VIII(h)(12))".into(),
- ))
-}
diff --git a/v2/crates/ruview-swarm/src/formation/reynolds.rs b/v2/crates/ruview-swarm/src/formation/reynolds.rs
deleted file mode 100644
index 98006756..00000000
--- a/v2/crates/ruview-swarm/src/formation/reynolds.rs
+++ /dev/null
@@ -1,107 +0,0 @@
-//! Reynolds flocking: separation, alignment, cohesion.
-
-use crate::types::{NodeId, Position3D, Velocity3D};
-use serde::{Deserialize, Serialize};
-
-/// Parameters for Reynolds boid rules.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct ReynoldsParams {
- pub separation_dist_m: f64,
- pub separation_weight: f64,
- pub alignment_weight: f64,
- pub cohesion_weight: f64,
- pub k_neighbors: usize,
-}
-
-impl Default for ReynoldsParams {
- fn default() -> Self {
- Self {
- separation_dist_m: 3.0,
- separation_weight: 1.5,
- alignment_weight: 1.0,
- cohesion_weight: 0.8,
- k_neighbors: 7,
- }
- }
-}
-
-impl ReynoldsParams {
- /// Compute a desired velocity delta for `node_id` based on the three Reynolds rules.
- pub fn compute_velocity(
- &self,
- node_id: NodeId,
- positions: &[(NodeId, Position3D)],
- ) -> Velocity3D {
- let own_pos = positions.iter().find(|(id, _)| *id == node_id).map(|(_, p)| *p);
- let own_pos = match own_pos {
- Some(p) => p,
- None => return Velocity3D::default(),
- };
-
- // Sort neighbours by distance, take k nearest.
- let mut neighbours: Vec<(f64, &Position3D)> = positions
- .iter()
- .filter(|(id, _)| *id != node_id)
- .map(|(_, p)| (own_pos.distance_to(p), p))
- .collect();
- neighbours.sort_by(|a, b| a.0.partial_cmp(&b.0).unwrap_or(std::cmp::Ordering::Equal));
- neighbours.truncate(self.k_neighbors);
-
- if neighbours.is_empty() {
- return Velocity3D::default();
- }
-
- let n = neighbours.len() as f64;
-
- // --- Separation: steer away from too-close neighbours ---
- let (mut sep_x, mut sep_y, mut sep_z) = (0.0_f64, 0.0_f64, 0.0_f64);
- for (dist, p) in &neighbours {
- if *dist < self.separation_dist_m && *dist > 1e-6 {
- let factor = (self.separation_dist_m - *dist) / self.separation_dist_m;
- sep_x += (own_pos.x - p.x) / dist * factor;
- sep_y += (own_pos.y - p.y) / dist * factor;
- sep_z += (own_pos.z - p.z) / dist * factor;
- }
- }
-
- // --- Cohesion: steer toward average position ---
- let (avg_x, avg_y, avg_z) = neighbours
- .iter()
- .fold((0.0, 0.0, 0.0), |(ax, ay, az), (_, p)| (ax + p.x, ay + p.y, az + p.z));
- let coh_x = (avg_x / n) - own_pos.x;
- let coh_y = (avg_y / n) - own_pos.y;
- let coh_z = (avg_z / n) - own_pos.z;
-
- // Combine rules (alignment omitted in position-only mode — no velocity info here).
- let vx = self.separation_weight * sep_x + self.cohesion_weight * coh_x;
- let vy = self.separation_weight * sep_y + self.cohesion_weight * coh_y;
- let vz = self.separation_weight * sep_z + self.cohesion_weight * coh_z;
-
- Velocity3D { vx, vy, vz }
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- #[test]
- fn test_separation_pushes_apart() {
- let params = ReynoldsParams { separation_dist_m: 5.0, ..Default::default() };
- let positions = vec![
- (NodeId(0), Position3D { x: 0.0, y: 0.0, z: 0.0 }),
- (NodeId(1), Position3D { x: 1.0, y: 0.0, z: 0.0 }), // too close
- ];
- let vel = params.compute_velocity(NodeId(0), &positions);
- // Separation force should push node 0 in the -x direction (away from node 1)
- assert!(vel.vx < 0.0);
- }
-
- #[test]
- fn test_no_neighbours_returns_zero() {
- let params = ReynoldsParams::default();
- let positions = vec![(NodeId(0), Position3D::zero())];
- let vel = params.compute_velocity(NodeId(0), &positions);
- assert!((vel.vx.abs() + vel.vy.abs()) < 1e-9);
- }
-}
diff --git a/v2/crates/ruview-swarm/src/formation/virtual_structure.rs b/v2/crates/ruview-swarm/src/formation/virtual_structure.rs
deleted file mode 100644
index b7cb7bb8..00000000
--- a/v2/crates/ruview-swarm/src/formation/virtual_structure.rs
+++ /dev/null
@@ -1,80 +0,0 @@
-//! Virtual structure formation: fixed offsets from a shared reference point.
-
-use crate::types::{NodeId, Position3D};
-use serde::{Deserialize, Serialize};
-use std::collections::HashMap;
-
-/// Offsets from a shared reference point for each drone in the formation.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct VirtualStructure {
- /// NodeId → (dx, dy, dz) offset in metres from the reference.
- pub offsets: HashMap,
-}
-
-impl VirtualStructure {
- /// Create a rectangular grid formation with `n` drones, spaced `spacing_m` apart.
- pub fn grid_formation(n: usize, spacing_m: f64) -> Self {
- let cols = (n as f64).sqrt().ceil() as usize;
- let mut offsets = HashMap::new();
- for i in 0..n {
- let row = i / cols;
- let col = i % cols;
- offsets.insert(
- NodeId(i as u32),
- (row as f64 * spacing_m, col as f64 * spacing_m, 0.0),
- );
- }
- Self { offsets }
- }
-
- /// Create a circular formation with `n` drones evenly distributed.
- pub fn circle_formation(n: usize, radius_m: f64) -> Self {
- use std::f64::consts::TAU;
- let mut offsets = HashMap::new();
- for i in 0..n {
- let angle = TAU * i as f64 / n as f64;
- offsets.insert(
- NodeId(i as u32),
- (radius_m * angle.cos(), radius_m * angle.sin(), 0.0),
- );
- }
- Self { offsets }
- }
-
- /// Compute target position for a node, applying its offset from `reference`.
- pub fn target_position(&self, node_id: NodeId, reference: &Position3D) -> Position3D {
- if let Some(&(dx, dy, dz)) = self.offsets.get(&node_id) {
- Position3D {
- x: reference.x + dx,
- y: reference.y + dy,
- z: reference.z + dz,
- }
- } else {
- *reference
- }
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- #[test]
- fn test_grid_formation_4_drones() {
- let vs = VirtualStructure::grid_formation(4, 5.0);
- assert_eq!(vs.offsets.len(), 4);
- let ref_pos = Position3D { x: 100.0, y: 200.0, z: -30.0 };
- let p = vs.target_position(NodeId(0), &ref_pos);
- assert!((p.x - 100.0).abs() < 1e-6);
- }
-
- #[test]
- fn test_circle_formation() {
- let vs = VirtualStructure::circle_formation(4, 10.0);
- let ref_pos = Position3D::zero();
- let p = vs.target_position(NodeId(0), &ref_pos);
- // Node 0 at angle 0: x = 10, y = 0
- assert!((p.x - 10.0).abs() < 1e-6);
- assert!(p.y.abs() < 1e-6);
- }
-}
diff --git a/v2/crates/ruview-swarm/src/integration/flight_controller.rs b/v2/crates/ruview-swarm/src/integration/flight_controller.rs
deleted file mode 100644
index e26ff85b..00000000
--- a/v2/crates/ruview-swarm/src/integration/flight_controller.rs
+++ /dev/null
@@ -1,125 +0,0 @@
-//! Flight controller abstraction and simulated implementation.
-
-use crate::types::{DroneState, NodeId, Position3D};
-use async_trait::async_trait;
-use tokio::sync::Mutex;
-
-/// Flight controller operating mode.
-#[derive(Debug, Clone, PartialEq)]
-pub enum FlightMode {
- /// External position/velocity setpoints (PX4: OFFBOARD, ArduPilot: GUIDED).
- Offboard,
- Loiter,
- ReturnToLaunch,
- Land,
- Stabilize,
-}
-
-/// Abstraction over flight controller interfaces (PX4, ArduPilot, custom).
-#[async_trait]
-pub trait FlightController: Send + Sync {
- async fn set_target_position(
- &self,
- pos: &Position3D,
- speed_ms: f64,
- ) -> crate::SwarmResult<()>;
-
- async fn get_state(&self) -> crate::SwarmResult;
-
- async fn set_mode(&self, mode: FlightMode) -> crate::SwarmResult<()>;
-
- async fn arm(&self) -> crate::SwarmResult<()>;
-
- async fn disarm(&self) -> crate::SwarmResult<()>;
-
- async fn rtl(&self) -> crate::SwarmResult<()>;
-
- async fn emergency_land(&self) -> crate::SwarmResult<()>;
-}
-
-/// A simulated flight controller that immediately applies position commands.
-/// Used in tests and demo mode.
-pub struct SimulatedFlightController {
- pub state: Mutex,
-}
-
-impl SimulatedFlightController {
- pub fn new(id: NodeId) -> Self {
- Self {
- state: Mutex::new(DroneState::default_at_origin(id)),
- }
- }
-}
-
-#[async_trait]
-impl FlightController for SimulatedFlightController {
- async fn set_target_position(
- &self,
- pos: &Position3D,
- _speed_ms: f64,
- ) -> crate::SwarmResult<()> {
- let mut state = self.state.lock().await;
- state.position = *pos;
- Ok(())
- }
-
- async fn get_state(&self) -> crate::SwarmResult {
- let state = self.state.lock().await;
- Ok(state.clone())
- }
-
- async fn set_mode(&self, _mode: FlightMode) -> crate::SwarmResult<()> {
- Ok(())
- }
-
- async fn arm(&self) -> crate::SwarmResult<()> {
- Ok(())
- }
-
- async fn disarm(&self) -> crate::SwarmResult<()> {
- Ok(())
- }
-
- async fn rtl(&self) -> crate::SwarmResult<()> {
- let mut state = self.state.lock().await;
- state.position = Position3D::zero();
- Ok(())
- }
-
- async fn emergency_land(&self) -> crate::SwarmResult<()> {
- let mut state = self.state.lock().await;
- state.altitude_agl_m = 0.0;
- state.position.z = 0.0;
- Ok(())
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- #[tokio::test]
- async fn test_set_position_updates_state() {
- let fc = SimulatedFlightController::new(NodeId(0));
- let target = Position3D { x: 50.0, y: 30.0, z: -20.0 };
- fc.set_target_position(&target, 5.0).await.unwrap();
- let state = fc.get_state().await.unwrap();
- assert!((state.position.x - 50.0).abs() < 1e-6);
- assert!((state.position.y - 30.0).abs() < 1e-6);
- }
-
- #[tokio::test]
- async fn test_rtl_returns_to_origin() {
- let fc = SimulatedFlightController::new(NodeId(1));
- fc.set_target_position(
- &Position3D { x: 100.0, y: 100.0, z: -30.0 },
- 5.0,
- )
- .await
- .unwrap();
- fc.rtl().await.unwrap();
- let state = fc.get_state().await.unwrap();
- assert!(state.position.x.abs() < 1e-6);
- assert!(state.position.y.abs() < 1e-6);
- }
-}
diff --git a/v2/crates/ruview-swarm/src/integration/mavlink_messages.rs b/v2/crates/ruview-swarm/src/integration/mavlink_messages.rs
deleted file mode 100644
index 72df40a7..00000000
--- a/v2/crates/ruview-swarm/src/integration/mavlink_messages.rs
+++ /dev/null
@@ -1,222 +0,0 @@
-//! Custom MAVLink v2 message types for wifi-densepose-swarm coordination.
-//!
-//! Message IDs follow MAVLink custom dialect convention (50000+).
-//! All messages are signed via `security::mavlink_signing::MavlinkSigner`.
-
-use serde::{Deserialize, Serialize};
-use crate::types::{NodeId, Position3D, CsiDetection};
-
-/// MAVLink message ID base for swarm custom dialect.
-pub const SWARM_DIALECT_BASE: u32 = 50000;
-
-/// Message IDs for swarm custom messages.
-#[derive(Debug, Clone, Copy, PartialEq, Eq)]
-pub enum SwarmMsgId {
- /// Swarm node kinematic state broadcast (50000).
- NodeState = 50000,
- /// CSI detection report from sensing payload (50001).
- CsiReport = 50001,
- /// Task assignment from cluster head to worker (50002).
- TaskAssign = 50002,
- /// Probability grid tile update (Gossip dissemination) (50003).
- GridTileUpdate = 50003,
- /// Cluster head heartbeat + Raft term (50004).
- ClusterHeartbeat = 50004,
- /// Victim confirmation (3+ viewpoints agree) (50005).
- VictimConfirmed = 50005,
-}
-
-/// SWARM_NODE_STATE (50000): broadcast by each drone every 100 ms.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct SwarmNodeState {
- /// Sending node ID.
- pub node_id: u32,
- /// North position in local NED frame (m × 1000 = mm).
- pub pos_north_mm: i32,
- /// East position (mm).
- pub pos_east_mm: i32,
- /// Down position (mm, negative = above ground).
- pub pos_down_mm: i32,
- /// Speed m/s × 100.
- pub speed_cm_s: u16,
- /// Heading degrees × 100 (0–36000).
- pub heading_cdeg: u16,
- /// Battery percent × 10 (0–1000).
- pub battery_10th_pct: u16,
- /// Link quality 0–255 (255 = perfect).
- pub link_quality: u8,
- /// Fail-safe state (0=Nominal, 1=Hold, 2=LowBatt, 3=RTH, 4=Land, 5=Diverge, 6=Descent).
- pub failsafe_state: u8,
- /// Timestamp ms (wraps at u32 max, ~49 days).
- pub timestamp_ms: u32,
-}
-
-impl SwarmNodeState {
- pub fn from_drone_state(state: &crate::types::DroneState, failsafe: u8) -> Self {
- Self {
- node_id: state.id.0,
- pos_north_mm: (state.position.x * 1000.0) as i32,
- pos_east_mm: (state.position.y * 1000.0) as i32,
- pos_down_mm: (state.position.z * 1000.0) as i32,
- speed_cm_s: (state.velocity.magnitude() * 100.0) as u16,
- heading_cdeg: ((state.heading_rad.to_degrees().rem_euclid(360.0)) * 100.0) as u16,
- battery_10th_pct: (state.battery_pct * 10.0) as u16,
- link_quality: (state.link_quality * 255.0) as u8,
- failsafe_state: failsafe,
- timestamp_ms: state.timestamp_ms as u32,
- }
- }
-
- /// Encode to 20-byte MAVLink payload (fixed-length for efficiency).
- pub fn encode(&self) -> [u8; 20] {
- let mut buf = [0u8; 20];
- buf[0..4].copy_from_slice(&self.node_id.to_le_bytes());
- buf[4..8].copy_from_slice(&self.pos_north_mm.to_le_bytes());
- buf[8..12].copy_from_slice(&self.pos_east_mm.to_le_bytes());
- buf[12..16].copy_from_slice(&self.pos_down_mm.to_le_bytes());
- buf[16] = self.failsafe_state;
- buf[17] = self.link_quality;
- buf[18..20].copy_from_slice(&self.battery_10th_pct.to_le_bytes());
- buf
- }
-
- /// Decode from 20-byte MAVLink payload.
- pub fn decode(buf: &[u8; 20]) -> Self {
- Self {
- node_id: u32::from_le_bytes(buf[0..4].try_into().unwrap()),
- pos_north_mm: i32::from_le_bytes(buf[4..8].try_into().unwrap()),
- pos_east_mm: i32::from_le_bytes(buf[8..12].try_into().unwrap()),
- pos_down_mm: i32::from_le_bytes(buf[12..16].try_into().unwrap()),
- failsafe_state: buf[16],
- link_quality: buf[17],
- battery_10th_pct: u16::from_le_bytes(buf[18..20].try_into().unwrap()),
- speed_cm_s: 0,
- heading_cdeg: 0,
- timestamp_ms: 0,
- }
- }
-}
-
-/// SWARM_CSI_REPORT (50001): sent by sensing payload when detection confidence > threshold.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct SwarmCsiReport {
- pub node_id: u32,
- pub confidence_u8: u8, // confidence × 255
- pub has_position: bool,
- pub victim_north_mm: i32, // estimated victim position
- pub victim_east_mm: i32,
- pub victim_down_mm: i32,
- pub timestamp_ms: u32,
-}
-
-impl SwarmCsiReport {
- pub fn from_detection(det: &CsiDetection) -> Self {
- let (n, e, d) = det.victim_position
- .map(|p| ((p.x * 1000.0) as i32, (p.y * 1000.0) as i32, (p.z * 1000.0) as i32))
- .unwrap_or((0, 0, 0));
- Self {
- node_id: det.drone_id.0,
- confidence_u8: (det.confidence * 255.0) as u8,
- has_position: det.victim_position.is_some(),
- victim_north_mm: n,
- victim_east_mm: e,
- victim_down_mm: d,
- timestamp_ms: det.timestamp_ms as u32,
- }
- }
-
- pub fn to_detection(&self) -> CsiDetection {
- CsiDetection {
- drone_id: NodeId(self.node_id),
- confidence: self.confidence_u8 as f32 / 255.0,
- victim_position: if self.has_position {
- Some(Position3D {
- x: self.victim_north_mm as f64 / 1000.0,
- y: self.victim_east_mm as f64 / 1000.0,
- z: self.victim_down_mm as f64 / 1000.0,
- })
- } else {
- None
- },
- timestamp_ms: self.timestamp_ms as u64,
- }
- }
-}
-
-/// SWARM_CLUSTER_HEARTBEAT (50004): Raft leader heartbeat.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct SwarmClusterHeartbeat {
- pub leader_id: u32,
- pub raft_term: u64,
- pub cluster_size: u8,
- pub active_drones: u8,
- pub mission_phase: u8, // 0=Systematic, 1=ProbabilisticPursuit, 2=Convergence
- pub timestamp_ms: u32,
-}
-
-/// SWARM_VICTIM_CONFIRMED (50005): 3+ viewpoints confirm victim location.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct SwarmVictimConfirmed {
- pub victim_id: u8, // sequential victim counter
- pub victim_north_mm: i32,
- pub victim_east_mm: i32,
- pub victim_down_mm: i32,
- pub uncertainty_mm: u16, // localization uncertainty in mm
- pub contributing_drones: u8, // bitmask (drone 0 = bit 0)
- pub fused_confidence_u8: u8,
- pub timestamp_ms: u32,
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
- use crate::types::{DroneState, NodeId, Velocity3D};
-
- fn make_state() -> DroneState {
- DroneState {
- id: NodeId(3),
- position: Position3D { x: 100.5, y: 200.25, z: -30.0 },
- velocity: Velocity3D { vx: 5.0, vy: 0.0, vz: 0.0 },
- heading_rad: std::f64::consts::PI / 4.0,
- altitude_agl_m: 30.0,
- battery_pct: 78.5,
- link_quality: 0.92,
- timestamp_ms: 12345,
- }
- }
-
- #[test]
- fn test_node_state_encode_decode_roundtrip() {
- let state = make_state();
- let msg = SwarmNodeState::from_drone_state(&state, 0);
- let encoded = msg.encode();
- let decoded = SwarmNodeState::decode(&encoded);
- assert_eq!(decoded.node_id, 3);
- assert_eq!(decoded.pos_north_mm, 100500); // 100.5 m × 1000
- assert_eq!(decoded.failsafe_state, 0);
- }
-
- #[test]
- fn test_csi_report_roundtrip() {
- let det = CsiDetection {
- drone_id: NodeId(1),
- confidence: 0.85,
- victim_position: Some(Position3D { x: 50.0, y: 75.0, z: 0.0 }),
- timestamp_ms: 9999,
- };
- let msg = SwarmCsiReport::from_detection(&det);
- let back = msg.to_detection();
- assert!((back.confidence - 0.85).abs() < 0.01, "confidence roundtrip");
- let vp = back.victim_position.unwrap();
- assert!((vp.x - 50.0).abs() < 0.001);
- assert!((vp.y - 75.0).abs() < 0.001);
- }
-
- #[test]
- fn test_battery_encoding() {
- let mut state = make_state();
- state.battery_pct = 50.0;
- let msg = SwarmNodeState::from_drone_state(&state, 0);
- assert_eq!(msg.battery_10th_pct, 500); // 50% × 10
- }
-}
diff --git a/v2/crates/ruview-swarm/src/integration/mission_report.rs b/v2/crates/ruview-swarm/src/integration/mission_report.rs
deleted file mode 100644
index ec5fbfad..00000000
--- a/v2/crates/ruview-swarm/src/integration/mission_report.rs
+++ /dev/null
@@ -1,123 +0,0 @@
-//! Mission outcome report with victim confirmation details.
-use serde::{Deserialize, Serialize};
-
-/// A single confirmed victim with localization metadata.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct VictimReport {
- pub victim_id: u32,
- pub position: [f64; 3], // [north, east, down] NED metres
- pub localization_error_m: f64, // distance from ground-truth (sim only)
- pub uncertainty_m: f64, // fusion uncertainty ellipse
- pub contributing_drones: Vec,
- pub fused_confidence: f32,
- pub detection_time_secs: f64, // mission-elapsed time at confirmation
-}
-
-/// Complete mission outcome report.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct MissionReport {
- pub profile: String,
- pub num_drones: usize,
- pub area_m2: f64,
- pub mission_duration_secs: f64,
- pub coverage_pct: f64,
- pub victims_total: usize,
- pub victims_confirmed: usize,
- pub detection_rate: f64, // confirmed / total
- pub mean_localization_error_m: f64,
- pub collision_events: u32,
- pub victims: Vec,
- pub sota_comparison: SotaComparison,
-}
-
-/// Comparison against the Wi2SAR published baseline.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct SotaComparison {
- pub wi2sar_localization_m: f64, // 5.0 baseline
- pub our_localization_m: f64,
- pub localization_improvement_x: f64,
- pub wi2sar_coverage_time_secs: f64, // 810.0 for single drone over 160k m²
- pub our_coverage_time_secs: f64,
- pub beats_sota: bool,
-}
-
-impl MissionReport {
- pub fn detection_rate(&self) -> f64 {
- if self.victims_total == 0 {
- 1.0
- } else {
- self.victims_confirmed as f64 / self.victims_total as f64
- }
- }
-
- /// Produce a human-readable summary line.
- pub fn summary(&self) -> String {
- format!(
- "{} mission: {}/{} victims confirmed ({:.0}%), mean error {:.2}m, {:.0}% coverage in {:.1}s, {} collisions — SOTA: {}",
- self.profile,
- self.victims_confirmed,
- self.victims_total,
- self.detection_rate() * 100.0,
- self.mean_localization_error_m,
- self.coverage_pct * 100.0,
- self.mission_duration_secs,
- self.collision_events,
- if self.sota_comparison.beats_sota { "BEATEN" } else { "not beaten" },
- )
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- fn sample_sota() -> SotaComparison {
- SotaComparison {
- wi2sar_localization_m: 5.0,
- our_localization_m: 1.5,
- localization_improvement_x: 3.33,
- wi2sar_coverage_time_secs: 810.0,
- our_coverage_time_secs: 120.0,
- beats_sota: true,
- }
- }
-
- #[test]
- fn test_detection_rate_no_victims() {
- let report = MissionReport {
- profile: "sar".to_string(),
- num_drones: 2,
- area_m2: 160_000.0,
- mission_duration_secs: 100.0,
- coverage_pct: 0.5,
- victims_total: 0,
- victims_confirmed: 0,
- detection_rate: 1.0,
- mean_localization_error_m: 0.0,
- collision_events: 0,
- victims: vec![],
- sota_comparison: sample_sota(),
- };
- assert_eq!(report.detection_rate(), 1.0);
- }
-
- #[test]
- fn test_detection_rate_partial() {
- let report = MissionReport {
- profile: "sar".to_string(),
- num_drones: 4,
- area_m2: 160_000.0,
- mission_duration_secs: 100.0,
- coverage_pct: 0.8,
- victims_total: 4,
- victims_confirmed: 2,
- detection_rate: 0.5,
- mean_localization_error_m: 1.5,
- collision_events: 0,
- victims: vec![],
- sota_comparison: sample_sota(),
- };
- assert_eq!(report.detection_rate(), 0.5);
- assert!(report.summary().contains("sar mission"));
- }
-}
diff --git a/v2/crates/ruview-swarm/src/integration/mod.rs b/v2/crates/ruview-swarm/src/integration/mod.rs
deleted file mode 100644
index 87f8b94f..00000000
--- a/v2/crates/ruview-swarm/src/integration/mod.rs
+++ /dev/null
@@ -1,19 +0,0 @@
-//! External system integration: MAVLink v2, PX4 SITL, Gazebo, ROS2 DDS.
-
-pub mod mavlink_messages;
-pub mod mission_report;
-pub mod swarm_sim;
-pub mod telemetry;
-
-pub use mission_report::{MissionReport, SotaComparison, VictimReport};
-pub use telemetry::{DroneFrame, TelemetryRecorder};
-
-pub use mavlink_messages::{
- SwarmNodeState, SwarmCsiReport, SwarmClusterHeartbeat, SwarmVictimConfirmed, SwarmMsgId,
-};
-
-#[cfg(feature = "itar-unrestricted")]
-pub mod flight_controller;
-
-#[cfg(feature = "itar-unrestricted")]
-pub use flight_controller::{FlightController, FlightMode, SimulatedFlightController};
diff --git a/v2/crates/ruview-swarm/src/integration/swarm_sim.rs b/v2/crates/ruview-swarm/src/integration/swarm_sim.rs
deleted file mode 100644
index ba190ba8..00000000
--- a/v2/crates/ruview-swarm/src/integration/swarm_sim.rs
+++ /dev/null
@@ -1,487 +0,0 @@
-//! End-to-end 4-drone swarm simulation for integration testing.
-//!
-//! Simulates a complete SAR mission: systematic sweep → victim detection →
-//! multi-drone convergence. Validates M3 (CSI integration) + M7 (mission profiles).
-
-use crate::{
- config::SwarmConfig,
- integration::mission_report::{MissionReport, SotaComparison, VictimReport},
- orchestrator::SwarmOrchestrator,
- types::{NodeId, Position3D},
-};
-
-/// Result of an end-to-end simulated mission.
-#[derive(Debug, Clone)]
-pub struct SimMissionResult {
- pub total_cells_covered: u32,
- pub victims_detected: usize,
- pub elapsed_secs: f64,
- pub collision_events: u32,
- pub final_localization_error_m: Option,
- pub coverage_pct: f64,
-}
-
-/// Run an N-drone SAR swarm simulation using the Wi2SAR reference config.
-///
-/// Each step:
-/// 1. Each drone calls `step()` advancing its state machine.
-/// 2. All drone states are exchanged via simulated MAVLink broadcast.
-/// 3. Detections produced this step are collected and fused by the cluster head (drone 0).
-/// 4. Mission completes when coverage_pct > 90% or all steps are exhausted.
-pub async fn run_sar_simulation(
- num_drones: usize,
- num_steps: usize,
- dt_secs: f64,
-) -> SimMissionResult {
- let cfg = SwarmConfig::wi2sar_reference();
- let victims = vec![
- Position3D { x: 80.0, y: 120.0, z: 0.0 },
- Position3D { x: 250.0, y: 180.0, z: 0.0 },
- ];
-
- // Stagger drone starting positions across the area so they cover different cells.
- let area_w = cfg.mission.area_width_m;
- let area_h = cfg.mission.area_height_m;
- let mut drones: Vec = (0..num_drones)
- .map(|i| {
- let row = (i / 2) as f64;
- let col = (i % 2) as f64;
- SwarmOrchestrator::new_demo(
- NodeId(i as u32),
- cfg.clone(),
- Position3D {
- x: 10.0 + col * (area_w / 2.0),
- y: 10.0 + row * (area_h / 2.0),
- z: -cfg.planning.flight_altitude_m,
- },
- victims.clone(),
- )
- })
- .collect();
-
- let mut victims_detected = 0usize;
- let mut collision_events = 0u32;
- let mut final_localization_error: Option = None;
-
- for _step in 0..num_steps {
- // Step all drones (each step clears peer_detections internally).
- for drone in &mut drones {
- drone.step(dt_secs, true).await;
- }
-
- // Exchange simulated MAVLink state messages (full mesh broadcast).
- // Collect states first to avoid borrow conflicts.
- let states: Vec<_> = drones.iter().map(|d| d.state.clone()).collect();
- for drone in &mut drones {
- for state in &states {
- if state.id != drone.node_id {
- drone.receive_peer_state(state.clone());
- }
- }
- }
-
- // Gather CSI detections injected by the payload pipelines this step.
- // After step() the peer_detections vec is fresh (cleared at step start);
- // we simulate "send my detection to cluster head" by manually calling
- // receive_peer_detection on drone 0 for each other drone's local scan.
- // To avoid simultaneous borrow, collect detections before distributing.
- let local_detections: Vec<_> = drones
- .iter()
- .filter_map(|d| d.peer_detections.first().cloned())
- .collect();
-
- if !local_detections.is_empty() && num_drones > 0 {
- // Drone 0 acts as cluster head: accumulate detections for fusion.
- for det in &local_detections {
- if det.drone_id != drones[0].node_id {
- drones[0].receive_peer_detection(det.clone());
- }
- }
-
- // Attempt multi-drone fusion on cluster head.
- let all_dets: Vec<_> = drones[0].peer_detections.clone();
- if all_dets.len() >= 2 {
- let positions: Vec<(NodeId, Position3D)> = drones
- .iter()
- .map(|d| (d.node_id, d.state.position))
- .collect();
-
- if let Some(fused) = drones[0].fuse_detections(&all_dets, &positions) {
- if fused.confidence > 0.7 {
- victims_detected += 1;
-
- // Compute localization error vs nearest ground-truth victim.
- let err = victims
- .iter()
- .map(|v| fused.estimated_position.distance_to(v))
- .fold(f64::MAX, f64::min);
- final_localization_error = Some(err);
- }
- }
- }
- }
-
- // Check pairwise collision events (separation < 1.5 m).
- for i in 0..drones.len() {
- for j in (i + 1)..drones.len() {
- let dist = drones[i].state.position.distance_to(&drones[j].state.position);
- if dist < 1.5 {
- collision_events += 1;
- }
- }
- }
-
- // Early exit when sufficient coverage achieved.
- let avg_coverage = drones
- .iter()
- .map(|d| d.probability_grid.coverage_pct())
- .sum::()
- / drones.len() as f64;
- if avg_coverage > 0.90 {
- break;
- }
- }
-
- let total_cells: u32 = drones.iter().map(|d| d.stats.cells_covered).sum();
- let elapsed = drones[0].stats.elapsed_secs;
- let avg_coverage = drones
- .iter()
- .map(|d| d.probability_grid.coverage_pct())
- .sum::()
- / drones.len() as f64;
-
- SimMissionResult {
- total_cells_covered: total_cells,
- victims_detected,
- elapsed_secs: elapsed,
- collision_events,
- final_localization_error_m: final_localization_error,
- coverage_pct: avg_coverage,
- }
-}
-
-/// Run a full mission and produce a detailed MissionReport (not just SimMissionResult).
-/// This is the M7 end-to-end mission with victim confirmation.
-pub async fn run_mission_with_report(
- profile_config: SwarmConfig,
- num_drones: usize,
- victims: Vec,
- max_steps: usize,
- dt_secs: f64,
-) -> MissionReport {
- use crate::sensing::multiview::MultiViewFusion;
- use crate::types::CsiDetection;
-
- let area_m2 = profile_config.mission.area_width_m * profile_config.mission.area_height_m;
- let profile = profile_config.mission.profile.clone();
- let victims_total = victims.len();
-
- // Stagger drone starts across the area
- let mut drones: Vec = (0..num_drones)
- .map(|i| {
- let cols = (num_drones as f64).sqrt().ceil() as usize;
- let row = i / cols;
- let col = i % cols;
- SwarmOrchestrator::new_demo(
- NodeId(i as u32),
- profile_config.clone(),
- Position3D {
- x: 10.0 + col as f64 * (profile_config.mission.area_width_m / cols as f64),
- y: 10.0
- + row as f64 * (profile_config.mission.area_height_m / cols.max(1) as f64),
- z: -profile_config.planning.flight_altitude_m,
- },
- victims.clone(),
- )
- })
- .collect();
-
- let fusion = MultiViewFusion {
- min_viewpoints: 2,
- min_confidence: 0.5,
- };
- let mut confirmed_victims: Vec = Vec::new();
- let mut confirmed_positions: Vec = Vec::new();
- let mut collision_events = 0u32;
-
- for _step in 0..max_steps {
- for drone in &mut drones {
- drone.step(dt_secs, true).await;
- }
-
- // Broadcast peer states
- let states: Vec<_> = drones.iter().map(|d| d.state.clone()).collect();
- for drone in &mut drones {
- for state in &states {
- if state.id != drone.node_id {
- drone.receive_peer_state(state.clone());
- }
- }
- }
-
- // Gather detections from each drone's CSI pipeline at its current position.
- // Track which drone produced each detection so we can vector peers toward it.
- let mut step_detections: Vec = Vec::new();
- let mut detection_anchors: Vec = Vec::new();
- for drone in &drones {
- if let Some(det) = drone.csi_pipeline.scan(&drone.state.position).await {
- if let Some(vp) = det.victim_position {
- detection_anchors.push(vp);
- }
- step_detections.push(det);
- }
- }
-
- // Phase 3 convergence assist: when a single drone has a contact but no
- // second viewpoint, vector the nearest idle peer toward that contact so
- // two drones can confirm it via multi-view fusion (Wi2SAR §V convergence).
- if step_detections.len() == 1 {
- if let Some(anchor) = detection_anchors.first().copied() {
- let detector = step_detections[0].drone_id;
- // Find the nearest peer that is not the detector.
- let mut best: Option<(usize, f64)> = None;
- for (idx, drone) in drones.iter().enumerate() {
- if drone.node_id == detector {
- continue;
- }
- let d = drone.state.position.distance_to(&anchor);
- if best.map(|(_, bd)| d < bd).unwrap_or(true) {
- best = Some((idx, d));
- }
- }
- if let Some((idx, _)) = best {
- let speed = profile_config.planning.max_speed_ms.max(1.0);
- let p = drones[idx].state.position;
- let dx = anchor.x - p.x;
- let dy = anchor.y - p.y;
- let dist = (dx * dx + dy * dy).sqrt();
- if dist > 1e-6 {
- let step = speed.min(dist);
- drones[idx].state.position.x += (dx / dist) * step;
- drones[idx].state.position.y += (dy / dist) * step;
- }
- // Re-scan the vectored peer; if it now has a contact, add it.
- if let Some(det) =
- drones[idx].csi_pipeline.scan(&drones[idx].state.position).await
- {
- step_detections.push(det);
- }
- }
- }
- }
-
- // Multi-drone fusion
- if step_detections.len() >= 2 {
- let positions: Vec<(NodeId, Position3D)> =
- drones.iter().map(|d| (d.node_id, d.state.position)).collect();
- if let Some(fused) = fusion.fuse(&step_detections, &positions) {
- if fused.confidence > 0.7 {
- // Check this isn't a duplicate of an already-confirmed victim
- let is_new = confirmed_positions
- .iter()
- .all(|p| p.distance_to(&fused.estimated_position) > 10.0);
- if is_new {
- let err = victims
- .iter()
- .map(|v| fused.estimated_position.distance_to(v))
- .fold(f64::MAX, f64::min);
- confirmed_victims.push(VictimReport {
- victim_id: confirmed_victims.len() as u32,
- position: [
- fused.estimated_position.x,
- fused.estimated_position.y,
- fused.estimated_position.z,
- ],
- localization_error_m: err,
- uncertainty_m: fused.uncertainty_m,
- contributing_drones: fused
- .contributing_drones
- .iter()
- .map(|n| n.0)
- .collect(),
- fused_confidence: fused.confidence,
- detection_time_secs: drones[0].stats.elapsed_secs,
- });
- confirmed_positions.push(fused.estimated_position);
- }
- }
- }
- }
-
- // Collision avoidance: enforce minimum separation by nudging drones apart.
- // This models the formation min-separation guard so converging drones in
- // Phase 3 do not physically overlap. Runs before the collision metric so a
- // properly separated swarm records zero collision events.
- let min_sep = profile_config.formation.min_separation_m.max(1.5);
- let snapshot: Vec = drones.iter().map(|d| d.state.position).collect();
- // Index needed: mutates drones[i] while cross-indexing peers by index (i == j, i-j split).
- #[allow(clippy::needless_range_loop)]
- for i in 0..drones.len() {
- let mut push = (0.0_f64, 0.0_f64);
- for (j, other) in snapshot.iter().enumerate() {
- if i == j {
- continue;
- }
- let dx = drones[i].state.position.x - other.x;
- let dy = drones[i].state.position.y - other.y;
- let dist = (dx * dx + dy * dy).sqrt();
- if dist < min_sep && dist > 1e-6 {
- let overlap = (min_sep - dist) / 2.0;
- push.0 += (dx / dist) * overlap;
- push.1 += (dy / dist) * overlap;
- } else if dist <= 1e-6 {
- // Exactly coincident: deterministic split by index.
- push.0 += (i as f64 - j as f64) * min_sep * 0.5;
- }
- }
- drones[i].state.position.x += push.0;
- drones[i].state.position.y += push.1;
- }
-
- // Collision metric: count residual pairwise breaches after separation.
- for i in 0..drones.len() {
- for j in (i + 1)..drones.len() {
- if drones[i].state.position.distance_to(&drones[j].state.position) < 1.5 {
- collision_events += 1;
- }
- }
- }
-
- // Early exit when all victims found and coverage high
- let avg_coverage = drones.iter().map(|d| d.probability_grid.coverage_pct()).sum::()
- / drones.len() as f64;
- if confirmed_victims.len() >= victims_total && avg_coverage > 0.5 {
- break;
- }
- }
-
- let elapsed = drones[0].stats.elapsed_secs;
- let avg_coverage =
- drones.iter().map(|d| d.probability_grid.coverage_pct()).sum::() / drones.len() as f64;
- let mean_err = if confirmed_victims.is_empty() {
- 0.0
- } else {
- confirmed_victims.iter().map(|v| v.localization_error_m).sum::()
- / confirmed_victims.len() as f64
- };
-
- let victims_confirmed = confirmed_victims.len();
- let sota = SotaComparison {
- wi2sar_localization_m: 5.0,
- our_localization_m: if mean_err > 0.0 { mean_err } else { 1.732 },
- localization_improvement_x: if mean_err > 0.0 { 5.0 / mean_err } else { 2.89 },
- wi2sar_coverage_time_secs: 810.0,
- our_coverage_time_secs: elapsed,
- beats_sota: (mean_err > 0.0 && mean_err < 5.0) || mean_err == 0.0,
- };
-
- MissionReport {
- profile,
- num_drones,
- area_m2,
- mission_duration_secs: elapsed,
- coverage_pct: avg_coverage,
- victims_total,
- victims_confirmed,
- detection_rate: if victims_total == 0 {
- 1.0
- } else {
- victims_confirmed as f64 / victims_total as f64
- },
- mean_localization_error_m: mean_err,
- collision_events,
- victims: confirmed_victims,
- sota_comparison: sota,
- }
-}
-
-/// Infrastructure inspection mission (leader-follower along a linear corridor).
-pub async fn run_inspection_mission() -> MissionReport {
- let cfg = SwarmConfig::inspection_default();
- // Inspection targets along a power-line corridor
- let targets = vec![
- Position3D { x: 100.0, y: 25.0, z: 0.0 },
- Position3D { x: 500.0, y: 25.0, z: 0.0 },
- Position3D { x: 900.0, y: 25.0, z: 0.0 },
- ];
- run_mission_with_report(cfg, 4, targets, 200, 1.0).await
-}
-
-/// Underground mine mission (GPS-denied, slow, small swarm).
-pub async fn run_mine_mission() -> MissionReport {
- let cfg = SwarmConfig::mine_default();
- let trapped = vec![Position3D { x: 60.0, y: 30.0, z: 0.0 }];
- run_mission_with_report(cfg, 2, trapped, 200, 1.0).await
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- #[tokio::test]
- async fn test_4drone_sar_simulation_runs_without_panic() {
- // Quick smoke test: 20 steps at 0.5 s each = 10 simulated seconds.
- let result = run_sar_simulation(4, 20, 0.5).await;
- assert!(result.elapsed_secs > 0.0, "simulation should advance time");
- assert_eq!(result.collision_events, 0, "no collisions with proper spacing");
- }
-
- #[tokio::test]
- async fn test_4drone_coverage_advances() {
- // 100 steps at 1 s each = 100 simulated seconds.
- let result = run_sar_simulation(4, 100, 1.0).await;
- assert!(result.total_cells_covered > 0, "drones should cover cells");
- assert!(result.coverage_pct > 0.0, "some coverage should occur");
- }
-
- #[tokio::test]
- async fn test_simulation_time_tracking() {
- let result = run_sar_simulation(2, 10, 0.1).await;
- // 10 steps × 0.1 s = 1.0 s elapsed.
- assert!(
- (result.elapsed_secs - 1.0).abs() < 0.05,
- "elapsed {}s should be ~1.0s",
- result.elapsed_secs
- );
- }
-
- #[tokio::test]
- async fn test_mission_report_sar() {
- let cfg = SwarmConfig::wi2sar_reference();
- let victims = vec![
- Position3D { x: 80.0, y: 120.0, z: 0.0 },
- Position3D { x: 250.0, y: 180.0, z: 0.0 },
- ];
- let report = run_mission_with_report(cfg, 4, victims, 200, 1.0).await;
- assert_eq!(report.profile, "sar");
- assert_eq!(report.victims_total, 2);
- assert_eq!(report.collision_events, 0, "no collisions expected");
- // Report should have a valid SOTA comparison
- assert_eq!(report.sota_comparison.wi2sar_localization_m, 5.0);
- println!("SAR report: {}", report.summary());
- }
-
- #[tokio::test]
- async fn test_inspection_mission_runs() {
- let report = run_inspection_mission().await;
- assert_eq!(report.profile, "inspection");
- assert_eq!(report.num_drones, 4);
- }
-
- #[tokio::test]
- async fn test_mine_mission_runs() {
- let report = run_mine_mission().await;
- assert_eq!(report.profile, "mine");
- assert_eq!(report.num_drones, 2);
- assert_eq!(report.victims_total, 1);
- }
-
- #[cfg(feature = "ruflo")]
- #[tokio::test]
- async fn test_mission_report_serializable() {
- let cfg = SwarmConfig::wi2sar_reference();
- let report = run_mission_with_report(cfg, 2, vec![], 20, 0.5).await;
- let json = serde_json::to_string(&report);
- assert!(json.is_ok(), "MissionReport must serialize to JSON");
- }
-}
diff --git a/v2/crates/ruview-swarm/src/integration/telemetry.rs b/v2/crates/ruview-swarm/src/integration/telemetry.rs
deleted file mode 100644
index 9eaa16e8..00000000
--- a/v2/crates/ruview-swarm/src/integration/telemetry.rs
+++ /dev/null
@@ -1,183 +0,0 @@
-//! JSONL telemetry recorder for the swarm training/sim visualizer.
-//!
-//! Emits newline-delimited JSON records consumed by `viz/swarm_viz.html`:
-//! - one `meta` record (mission profile, area, ground-truth victims)
-//! - many `step` records (per-tick drone positions, coverage, detections)
-//! - optional `episode` records (per-episode training metrics)
-//!
-//! Written by hand (no serde_json dependency) so it stays in the default build
-//! and never affects the test/CI surface. The schema is flat and the only
-//! string fields are developer-controlled identifiers, so manual encoding is safe.
-
-use crate::types::{DroneState, Position3D};
-use std::fs::File;
-use std::io::{BufWriter, Write};
-use std::path::Path;
-
-/// Records swarm telemetry to a JSONL file for offline visualization.
-pub struct TelemetryRecorder {
- writer: BufWriter,
-}
-
-/// One drone's per-step visual state.
-pub struct DroneFrame {
- pub id: u32,
- pub x: f64,
- pub y: f64,
- pub heading_rad: f64,
- pub battery_pct: f32,
- pub detected: bool,
-}
-
-impl DroneFrame {
- pub fn from_state(state: &DroneState, detected: bool) -> Self {
- Self {
- id: state.id.0,
- x: state.position.x,
- y: state.position.y,
- heading_rad: state.heading_rad,
- battery_pct: state.battery_pct,
- detected,
- }
- }
-}
-
-impl TelemetryRecorder {
- /// Open a telemetry file for writing.
- pub fn create>(path: P) -> std::io::Result {
- let file = File::create(path)?;
- Ok(Self { writer: BufWriter::new(file) })
- }
-
- /// Write the one-time mission metadata header.
- pub fn meta(
- &mut self,
- profile: &str,
- drones: usize,
- area_w: f64,
- area_h: f64,
- victims: &[Position3D],
- ) -> std::io::Result<()> {
- let vics: Vec = victims
- .iter()
- .map(|v| format!("[{:.2},{:.2}]", v.x, v.y))
- .collect();
- writeln!(
- self.writer,
- r#"{{"type":"meta","profile":"{}","drones":{},"area_w":{:.2},"area_h":{:.2},"victims":[{}]}}"#,
- sanitize(profile),
- drones,
- area_w,
- area_h,
- vics.join(",")
- )
- }
-
- /// Write one simulation step (all drones at this tick).
- pub fn step(
- &mut self,
- episode: usize,
- step: usize,
- t_secs: f64,
- drones: &[DroneFrame],
- coverage_pct: f64,
- ) -> std::io::Result<()> {
- let ds: Vec = drones
- .iter()
- .map(|d| {
- format!(
- r#"{{"id":{},"x":{:.2},"y":{:.2},"hdg":{:.3},"batt":{:.1},"det":{}}}"#,
- d.id, d.x, d.y, d.heading_rad, d.battery_pct, d.detected
- )
- })
- .collect();
- writeln!(
- self.writer,
- r#"{{"type":"step","ep":{},"step":{},"t":{:.2},"coverage":{:.4},"drones":[{}]}}"#,
- episode,
- step,
- t_secs,
- coverage_pct,
- ds.join(",")
- )
- }
-
- /// Write one episode's training metrics.
- pub fn episode(
- &mut self,
- episode: usize,
- mean_return: f32,
- policy_loss: f32,
- value_loss: f32,
- victims_found: usize,
- ) -> std::io::Result<()> {
- writeln!(
- self.writer,
- r#"{{"type":"episode","ep":{},"mean_return":{:.4},"policy_loss":{:.4},"value_loss":{:.4},"victims_found":{}}}"#,
- episode, mean_return, policy_loss, value_loss, victims_found
- )
- }
-
- /// Flush buffered records to disk.
- pub fn flush(&mut self) -> std::io::Result<()> {
- self.writer.flush()
- }
-}
-
-/// Strip characters that would break the flat JSON string field.
-fn sanitize(s: &str) -> String {
- s.chars().filter(|c| *c != '"' && *c != '\\' && *c != '\n').collect()
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
- use crate::types::{NodeId, Velocity3D};
-
- fn tmp_path(name: &str) -> std::path::PathBuf {
- std::env::temp_dir().join(name)
- }
-
- #[test]
- fn test_records_valid_jsonl() {
- let path = tmp_path("ruview_telemetry_test.jsonl");
- {
- let mut rec = TelemetryRecorder::create(&path).unwrap();
- rec.meta("sar", 2, 400.0, 400.0, &[Position3D { x: 80.0, y: 120.0, z: 0.0 }])
- .unwrap();
- let state = DroneState {
- id: NodeId(0),
- position: Position3D { x: 10.5, y: 20.25, z: -30.0 },
- velocity: Velocity3D::default(),
- heading_rad: 1.57,
- altitude_agl_m: 30.0,
- battery_pct: 88.0,
- link_quality: 0.9,
- timestamp_ms: 0,
- };
- rec.step(0, 0, 0.0, &[DroneFrame::from_state(&state, true)], 0.05)
- .unwrap();
- rec.episode(0, 103.7, -61.2, 12643.3, 1).unwrap();
- rec.flush().unwrap();
- }
- let content = std::fs::read_to_string(&path).unwrap();
- let lines: Vec<&str> = content.lines().collect();
- assert_eq!(lines.len(), 3, "meta + step + episode = 3 records");
- assert!(lines[0].contains(r#""type":"meta""#));
- assert!(lines[1].contains(r#""type":"step""#));
- assert!(lines[1].contains(r#""det":true"#));
- assert!(lines[2].contains(r#""type":"episode""#));
- // Each line is balanced JSON (braces match)
- for line in &lines {
- let opens = line.matches('{').count();
- let closes = line.matches('}').count();
- assert_eq!(opens, closes, "balanced braces in: {line}");
- }
- std::fs::remove_file(&path).ok();
- }
-
- #[test]
- fn test_sanitize_strips_quotes() {
- assert_eq!(sanitize("sa\"r\n"), "sar");
- }
-}
diff --git a/v2/crates/ruview-swarm/src/lib.rs b/v2/crates/ruview-swarm/src/lib.rs
deleted file mode 100644
index cd9b8ae4..00000000
--- a/v2/crates/ruview-swarm/src/lib.rs
+++ /dev/null
@@ -1,26 +0,0 @@
-//! Drone swarm control system — ADR-148.
-//!
-//! Hierarchical-mesh topology · Raft consensus · MAPPO MARL · CSI sensing integration
-
-pub mod types;
-pub mod topology;
-pub mod formation;
-pub mod planning;
-pub mod allocation;
-pub mod sensing;
-pub mod marl;
-pub mod security;
-pub mod failsafe;
-pub mod config;
-pub mod demo;
-pub mod evals;
-pub mod integration;
-pub mod bench_support;
-pub mod orchestrator;
-pub mod ruflo;
-
-pub use types::{
- ClusterId, CsiDetection, DroneState, FailSafeState, GridCell, NodeId,
- Position3D, SwarmError, SwarmResult, SwarmRole, SwarmTask, TaskId, TaskKind, Velocity3D,
-};
-pub use config::SwarmConfig;
diff --git a/v2/crates/ruview-swarm/src/marl/actor.rs b/v2/crates/ruview-swarm/src/marl/actor.rs
deleted file mode 100644
index 21fa3e8c..00000000
--- a/v2/crates/ruview-swarm/src/marl/actor.rs
+++ /dev/null
@@ -1,196 +0,0 @@
-use super::observation::LocalObservation;
-
-/// Action output from the MAPPO actor.
-#[derive(Debug, Clone)]
-pub struct ActorAction {
- pub delta_heading_rad: f32, // [-pi/6, +pi/6] per second
- pub delta_altitude_m: f32, // [-1.0, +1.0] m per second
- pub speed_ms: f32, // [0.0, 8.0] m/s
- pub trigger_csi_scan: bool,
-}
-
-#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
-pub struct ActorConfig {
- /// Hidden layer dimensions; default [128, 64].
- pub hidden_dims: Vec,
- pub max_speed_ms: f32,
- pub max_heading_delta_rad: f32,
- pub max_altitude_delta_m: f32,
-}
-
-impl Default for ActorConfig {
- fn default() -> Self {
- Self {
- hidden_dims: vec![128, 64],
- max_speed_ms: 8.0,
- max_heading_delta_rad: std::f32::consts::PI / 6.0,
- max_altitude_delta_m: 1.0,
- }
- }
-}
-
-// ---------------------------------------------------------------------------
-// MLP helper functions
-// ---------------------------------------------------------------------------
-
-#[inline]
-fn relu(x: f32) -> f32 { x.max(0.0) }
-
-#[inline]
-fn tanh_f32(x: f32) -> f32 { x.tanh() }
-
-#[inline]
-fn sigmoid(x: f32) -> f32 { 1.0 / (1.0 + (-x).exp()) }
-
-fn matmul_vec(weights: &[Vec], input: &[f32], bias: &[f32]) -> Vec {
- weights
- .iter()
- .zip(bias.iter())
- .map(|(row, b)| row.iter().zip(input.iter()).map(|(w, x)| w * x).sum::() + b)
- .collect()
-}
-
-// ---------------------------------------------------------------------------
-// MAPPO actor
-// ---------------------------------------------------------------------------
-
-/// Simple 3-layer MLP actor (pure Rust, no ONNX).
-///
-/// For production deployment, replace with an ONNX INT8 model loaded via the
-/// `ort` crate (enable feature `onnx`). The interface — `forward(&obs) -> ActorAction`
-/// — remains identical.
-pub struct MappoActor {
- pub config: ActorConfig,
- /// Layer 1: obs_dim × hidden1
- w1: Vec>,
- b1: Vec,
- /// Layer 2: hidden1 × hidden2
- w2: Vec>,
- b2: Vec,
- /// Output layer: hidden2 × 4
- w_out: Vec>,
- b_out: Vec,
-}
-
-impl MappoActor {
- /// Create an actor with random weights using the standard observation dimension.
- ///
- /// Convenience constructor — uses `LocalObservation::DIM` as the input dimension.
- pub fn random_init(config: ActorConfig) -> Self {
- Self::random_init_with_dim(LocalObservation::DIM, config)
- }
-
- /// Create an actor with random (untrained) weights — for testing only.
- pub fn random_init_with_dim(obs_dim: usize, config: ActorConfig) -> Self {
- use rand::Rng;
- let mut rng = rand::thread_rng();
- let h1 = config.hidden_dims[0];
- let h2 = config.hidden_dims.get(1).copied().unwrap_or(64);
-
- let w1 = (0..h1)
- .map(|_| (0..obs_dim).map(|_| rng.gen_range(-0.1..0.1)).collect())
- .collect();
- let b1 = vec![0.0f32; h1];
- let w2 = (0..h2)
- .map(|_| (0..h1).map(|_| rng.gen_range(-0.1..0.1)).collect())
- .collect();
- let b2 = vec![0.0f32; h2];
- let w_out = (0..4)
- .map(|_| (0..h2).map(|_| rng.gen_range(-0.1..0.1)).collect())
- .collect();
- let b_out = vec![0.0f32; 4];
-
- Self { config, w1, b1, w2, b2, w_out, b_out }
- }
-
- /// Forward pass: observation -> action.
- pub fn forward(&self, obs: &LocalObservation) -> ActorAction {
- let input = obs.to_vec();
- let h1: Vec = matmul_vec(&self.w1, &input, &self.b1)
- .into_iter().map(relu).collect();
- let h2: Vec = matmul_vec(&self.w2, &h1, &self.b2)
- .into_iter().map(relu).collect();
- let out = matmul_vec(&self.w_out, &h2, &self.b_out);
-
- ActorAction {
- delta_heading_rad: tanh_f32(out[0]) * self.config.max_heading_delta_rad,
- delta_altitude_m: tanh_f32(out[1]) * self.config.max_altitude_delta_m,
- speed_ms: sigmoid(out[2]) * self.config.max_speed_ms,
- trigger_csi_scan: sigmoid(out[3]) > 0.5,
- }
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- fn dummy_obs() -> LocalObservation {
- LocalObservation {
- own_state: [0.5; 9],
- neighbor_relative_pos: [0.0; 18],
- grid_tile: [0.1; 25],
- csi_reading: [0.0; 5],
- task_encoding: [0.0; 7],
- }
- }
-
- #[test]
- fn forward_action_bounds() {
- let config = ActorConfig::default();
- let actor = MappoActor::random_init_with_dim(LocalObservation::DIM, config.clone());
- let action = actor.forward(&dummy_obs());
-
- assert!(action.delta_heading_rad.abs() <= config.max_heading_delta_rad + 1e-5);
- assert!(action.delta_altitude_m.abs() <= config.max_altitude_delta_m + 1e-5);
- assert!(action.speed_ms >= 0.0 && action.speed_ms <= config.max_speed_ms + 1e-5);
- }
-
- #[test]
- fn forward_deterministic_with_zero_weights() {
- // Manually craft an actor with zero weights so output is deterministic.
- let config = ActorConfig::default();
- let h1 = config.hidden_dims[0];
- let h2 = config.hidden_dims[1];
-
- let actor = MappoActor {
- w1: vec![vec![0.0; LocalObservation::DIM]; h1],
- b1: vec![0.0; h1],
- w2: vec![vec![0.0; h1]; h2],
- b2: vec![0.0; h2],
- w_out: vec![vec![0.0; h2]; 4],
- b_out: vec![0.0; 4],
- config,
- };
- let action = actor.forward(&dummy_obs());
- // tanh(0) = 0, sigmoid(0) = 0.5
- assert!((action.delta_heading_rad).abs() < 1e-6);
- assert!((action.delta_altitude_m).abs() < 1e-6);
- assert!((action.speed_ms - 4.0).abs() < 1e-4); // sigmoid(0) * 8 = 4
- }
-
- #[test]
- fn test_actor_action_bounds() {
- let cfg = ActorConfig::default();
- let actor = MappoActor::random_init(cfg.clone());
- let obs = LocalObservation::zeros();
- let action = actor.forward(&obs);
- assert!(action.delta_heading_rad.abs() <= cfg.max_heading_delta_rad * 1.001);
- assert!(action.delta_altitude_m.abs() <= cfg.max_altitude_delta_m * 1.001);
- assert!(action.speed_ms >= 0.0 && action.speed_ms <= cfg.max_speed_ms * 1.001);
- }
-
- #[test]
- fn test_actor_inference_speed() {
- let actor = MappoActor::random_init(ActorConfig::default());
- let obs = LocalObservation::zeros();
- let start = std::time::Instant::now();
- for _ in 0..1000 {
- let _ = actor.forward(&obs);
- }
- let elapsed = start.elapsed();
- // 100ms threshold in release builds; debug builds allow 10× slack
- let limit_ms = if cfg!(debug_assertions) { 1000 } else { 100 };
- assert!(elapsed.as_millis() < limit_ms, "1000 inferences took {}ms, limit {}ms", elapsed.as_millis(), limit_ms);
- }
-}
diff --git a/v2/crates/ruview-swarm/src/marl/candle_ppo.rs b/v2/crates/ruview-swarm/src/marl/candle_ppo.rs
deleted file mode 100644
index a757ef2a..00000000
--- a/v2/crates/ruview-swarm/src/marl/candle_ppo.rs
+++ /dev/null
@@ -1,268 +0,0 @@
-//! Real PPO trainer using Candle autodiff (CPU or CUDA).
-//!
-//! Replaces the finite-difference placeholder in `training_loop.rs` for actual
-//! training. The update step runs a genuine backward pass via
-//! [`candle_nn::Optimizer::backward_step`] — not a finite-difference nudge.
-//!
-//! Compiled only under the `train` feature.
-
-use candle_core::{DType, Device, Module, Result as CandleResult, Tensor};
-use candle_nn::{linear, AdamW, Linear, Optimizer, ParamsAdamW, VarBuilder, VarMap};
-
-use crate::marl::observation::LocalObservation;
-
-/// Device selection — CUDA if `cuda` feature + GPU present, else CPU.
-pub fn select_device() -> Device {
- #[cfg(feature = "cuda")]
- {
- if let Ok(d) = Device::cuda_if_available(0) {
- return d;
- }
- }
- Device::Cpu
-}
-
-/// Candle-backed actor-critic network for PPO.
-/// Input: 64-dim `LocalObservation`. Outputs: 4-dim action mean + state value.
-pub struct CandleActorCritic {
- l1: Linear,
- l2: Linear,
- action_head: Linear, // 4 outputs (heading, altitude, speed, scan-logit)
- value_head: Linear, // 1 output (state value)
- #[allow(dead_code)]
- log_std: Tensor, // learnable log-std for the 3 continuous actions
- device: Device,
- varmap: VarMap,
-}
-
-impl CandleActorCritic {
- pub fn new(device: Device) -> CandleResult {
- let varmap = VarMap::new();
- let vb = VarBuilder::from_varmap(&varmap, DType::F32, &device);
- let obs_dim = LocalObservation::DIM; // 64
- let l1 = linear(obs_dim, 128, vb.pp("l1"))?;
- let l2 = linear(128, 64, vb.pp("l2"))?;
- let action_head = linear(64, 4, vb.pp("action"))?;
- let value_head = linear(64, 1, vb.pp("value"))?;
- // `get` on a varmap-backed builder registers a trainable variable.
- let log_std = vb.get(3, "log_std")?;
- Ok(Self {
- l1,
- l2,
- action_head,
- value_head,
- log_std,
- device,
- varmap,
- })
- }
-
- /// Forward: obs batch `[B, 64]` → (action_mean `[B,4]`, value `[B,1]`).
- pub fn forward(&self, obs: &Tensor) -> CandleResult<(Tensor, Tensor)> {
- let h = self.l1.forward(obs)?.relu()?;
- let h = self.l2.forward(&h)?.relu()?;
- let action_mean = self.action_head.forward(&h)?;
- let value = self.value_head.forward(&h)?;
- Ok((action_mean, value))
- }
-
- pub fn varmap(&self) -> &VarMap {
- &self.varmap
- }
- pub fn device(&self) -> &Device {
- &self.device
- }
-}
-
-/// PPO training config (real version).
-#[derive(Debug, Clone)]
-pub struct CandlePpoConfig {
- pub lr: f64,
- pub clip_epsilon: f32,
- pub gamma: f32,
- pub gae_lambda: f32,
- pub entropy_coeff: f32,
- pub value_coeff: f32,
- pub epochs: usize,
- pub minibatch: usize,
-}
-
-impl Default for CandlePpoConfig {
- fn default() -> Self {
- Self {
- lr: 3e-4,
- clip_epsilon: 0.2,
- gamma: 0.99,
- gae_lambda: 0.95,
- entropy_coeff: 0.01,
- value_coeff: 0.5,
- epochs: 10,
- minibatch: 64,
- }
- }
-}
-
-/// PPO trainer with real Candle autodiff.
-///
-/// One PPO training step runs over a batch of
-/// `(obs, action, advantage, return, old_log_prob)` and returns
-/// `(policy_loss, value_loss, entropy)`. Uses the clipped surrogate objective
-/// with GAE advantages.
-pub struct CandleTrainer {
- pub net: CandleActorCritic,
- optimizer: AdamW,
- config: CandlePpoConfig,
-}
-
-impl CandleTrainer {
- pub fn new(config: CandlePpoConfig) -> CandleResult {
- let device = select_device();
- let net = CandleActorCritic::new(device)?;
- let params = ParamsAdamW {
- lr: config.lr,
- ..Default::default()
- };
- let optimizer = AdamW::new(net.varmap().all_vars(), params)?;
- Ok(Self {
- net,
- optimizer,
- config,
- })
- }
-
- /// Compute GAE advantages and returns from rewards + values + dones.
- pub fn compute_gae(
- &self,
- rewards: &[f32],
- values: &[f32],
- dones: &[bool],
- ) -> (Vec, Vec) {
- let n = rewards.len();
- let mut advantages = vec![0.0f32; n];
- let mut returns = vec![0.0f32; n];
- let mut gae = 0.0f32;
- for t in (0..n).rev() {
- let next_value = if t + 1 < n { values[t + 1] } else { 0.0 };
- let next_nonterminal = if dones[t] { 0.0 } else { 1.0 };
- let delta =
- rewards[t] + self.config.gamma * next_value * next_nonterminal - values[t];
- gae = delta + self.config.gamma * self.config.gae_lambda * next_nonterminal * gae;
- advantages[t] = gae;
- returns[t] = gae + values[t];
- }
- (advantages, returns)
- }
-
- /// Run a PPO update on a batch. `obs_batch` aligned with
- /// `actions`/`advantages`/`returns`/`old_log_probs`.
- /// Returns `(mean_policy_loss, mean_value_loss, mean_entropy)`.
- pub fn update(
- &mut self,
- obs_batch: &[LocalObservation],
- _actions: &[[f32; 4]],
- advantages: &[f32],
- returns: &[f32],
- _old_log_probs: &[f32],
- ) -> CandleResult<(f32, f32, f32)> {
- let device = self.net.device().clone();
- let b = obs_batch.len();
- if b == 0 {
- return Ok((0.0, 0.0, 0.0));
- }
-
- // Build obs tensor [B, 64]
- let obs_flat: Vec = obs_batch.iter().flat_map(|o| o.to_vec()).collect();
- let obs_t = Tensor::from_vec(obs_flat, (b, LocalObservation::DIM), &device)?;
- let adv_t = Tensor::from_vec(advantages.to_vec(), b, &device)?;
- let ret_t = Tensor::from_vec(returns.to_vec(), b, &device)?;
-
- let mut last = (0.0f32, 0.0f32, 0.0f32);
- for _epoch in 0..self.config.epochs {
- let (action_mean, value) = self.net.forward(&obs_t)?;
- // Value loss: MSE(value, returns)
- let value = value.squeeze(1)?;
- let value_loss = value.sub(&ret_t)?.sqr()?.mean_all()?;
- // Policy: use action_mean[:,0] (heading) as a tractable Gaussian
- // log-prob proxy (full multivariate is possible; keep it stable for
- // the first real version).
- let pred_action = action_mean.narrow(1, 0, 1)?.squeeze(1)?;
- // Surrogate: -(advantage * pred_action) as a differentiable policy
- // signal. This is a simplified-but-REAL gradient (not finite-diff):
- // the optimizer runs an actual backward pass over the network.
- let surrogate = adv_t.mul(&pred_action)?.mean_all()?;
- let policy_loss = surrogate.neg()?;
- let total = (policy_loss.clone()
- + value_loss.affine(self.config.value_coeff as f64, 0.0)?)?;
- self.optimizer.backward_step(&total)?;
- last = (
- policy_loss.to_scalar::().unwrap_or(0.0),
- value_loss.to_scalar::().unwrap_or(0.0),
- 0.0,
- );
- }
- Ok(last)
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- #[test]
- fn test_device_selects_cpu_by_default() {
- let d = select_device();
- // Without the `cuda` feature this must be CPU.
- assert!(matches!(d, Device::Cpu));
- }
-
- #[test]
- fn test_actor_critic_forward_shapes() {
- let net = CandleActorCritic::new(Device::Cpu).unwrap();
- let obs = Tensor::zeros((4, LocalObservation::DIM), DType::F32, &Device::Cpu).unwrap();
- let (action_mean, value) = net.forward(&obs).unwrap();
- assert_eq!(action_mean.dims(), &[4, 4]);
- assert_eq!(value.dims(), &[4, 1]);
- }
-
- #[test]
- fn test_compute_gae_terminal() {
- let trainer = CandleTrainer::new(CandlePpoConfig::default()).unwrap();
- let rewards = vec![1.0, 1.0, 1.0];
- let values = vec![0.0, 0.0, 0.0];
- let dones = vec![false, false, true];
- let (adv, ret) = trainer.compute_gae(&rewards, &values, &dones);
- assert_eq!(adv.len(), 3);
- assert_eq!(ret.len(), 3);
- // Last step terminal → advantage == reward (no bootstrap).
- assert!((adv[2] - 1.0).abs() < 1e-5, "terminal advantage = reward, got {}", adv[2]);
- }
-
- #[test]
- fn test_real_autodiff_update_runs() {
- let mut trainer = CandleTrainer::new(CandlePpoConfig {
- epochs: 3,
- ..Default::default()
- })
- .unwrap();
- let obs = vec![LocalObservation::zeros(); 8];
- let actions = vec![[0.0f32; 4]; 8];
- let advantages = vec![1.0f32; 8];
- let returns = vec![2.0f32; 8];
- let old_log_probs = vec![0.0f32; 8];
- let (pl, vl, ent) = trainer
- .update(&obs, &actions, &advantages, &returns, &old_log_probs)
- .unwrap();
- assert!(pl.is_finite(), "policy loss finite");
- assert!(vl.is_finite(), "value loss finite");
- assert_eq!(ent, 0.0);
- // Value loss must be positive (predicted value starts ~0, target = 2.0).
- assert!(vl > 0.0, "value loss should be > 0, got {}", vl);
- }
-
- #[test]
- fn test_update_empty_batch() {
- let mut trainer = CandleTrainer::new(CandlePpoConfig::default()).unwrap();
- let r = trainer.update(&[], &[], &[], &[], &[]).unwrap();
- assert_eq!(r, (0.0, 0.0, 0.0));
- }
-}
diff --git a/v2/crates/ruview-swarm/src/marl/learning.rs b/v2/crates/ruview-swarm/src/marl/learning.rs
deleted file mode 100644
index f8cdd6bd..00000000
--- a/v2/crates/ruview-swarm/src/marl/learning.rs
+++ /dev/null
@@ -1,301 +0,0 @@
-//! Selectable self-learning strategies for swarm MARL.
-//!
-//! - Mappo: centralized-critic, decentralized-execution (CTDE). Best cooperative
-//! performance; the centralized critic sees global state during training.
-//! - Ippo: independent PPO — each agent learns alone, no shared critic. Robust to
-//! adversarial/jamming conditions and partial observability; weaker coordination.
-//! - MappoCuriosity: MAPPO + intrinsic-curiosity reward bonus for exploration in
-//! sparse-reward regimes (count-based novelty over visited regions).
-//! - MetaRl: MAML-style fast adaptation — a base policy + per-deployment fast-weights
-//! that adapt in a few in-flight steps to wind/sensor drift.
-//!
-//! Pure Rust — always compiled (no Candle needed). This is the *strategy* layer;
-//! the gradient backend lives in `candle_ppo.rs` behind the `train` feature.
-
-/// Which self-learning strategy the swarm trains under. Selectable at runtime.
-#[derive(Debug, Clone, Copy, PartialEq, Eq, Default)]
-pub enum LearningPattern {
- /// Centralized critic, decentralized execution (CTDE).
- #[default]
- Mappo,
- /// Independent PPO — each agent learns alone, no shared critic.
- Ippo,
- /// MAPPO plus count-based intrinsic-curiosity reward bonus.
- MappoCuriosity,
- /// MAML-style fast adaptation with per-deployment fast-weights.
- MetaRl,
-}
-
-impl LearningPattern {
- /// Parse from a short identifier. Unknown strings fall back to the default
- /// (Mappo). Accepts both canonical names and friendly aliases.
- // Intentional inherent infallible parser (returns Self, not Result); shipped API.
- #[allow(clippy::should_implement_trait)]
- pub fn from_str(s: &str) -> Self {
- match s.trim().to_ascii_lowercase().as_str() {
- "mappo" => LearningPattern::Mappo,
- "ippo" => LearningPattern::Ippo,
- "curiosity" | "mappocuriosity" | "mappo_curiosity" => {
- LearningPattern::MappoCuriosity
- }
- "meta" | "metarl" | "meta_rl" => LearningPattern::MetaRl,
- _ => LearningPattern::default(),
- }
- }
-
- /// Canonical short name. `from_str(p.name()) == p` for every variant.
- pub fn name(&self) -> &'static str {
- match self {
- LearningPattern::Mappo => "mappo",
- LearningPattern::Ippo => "ippo",
- LearningPattern::MappoCuriosity => "curiosity",
- LearningPattern::MetaRl => "meta",
- }
- }
-
- /// Whether this strategy uses a centralized critic (CTDE) vs independent.
- pub fn centralized_critic(&self) -> bool {
- matches!(
- self,
- LearningPattern::Mappo
- | LearningPattern::MappoCuriosity
- | LearningPattern::MetaRl
- )
- }
-
- /// Whether an intrinsic-curiosity bonus is added to the reward.
- pub fn uses_curiosity(&self) -> bool {
- matches!(self, LearningPattern::MappoCuriosity)
- }
-}
-
-// ---------------------------------------------------------------------------
-// Curiosity: count-based intrinsic motivation
-// ---------------------------------------------------------------------------
-
-/// Count-based intrinsic-motivation module.
-///
-/// Maintains a visitation count over a coarse `grid × grid` spatial map of the
-/// mission area. The intrinsic bonus for visiting a cell is `beta / sqrt(count)`,
-/// computed *before* the visit is recorded — so novelty decays as a region is
-/// re-visited. This rewards exploration in sparse-reward regimes.
-pub struct CuriosityModule {
- counts: Vec,
- grid: u32,
- cell_w: f64,
- cell_h: f64,
- beta: f32,
-}
-
-impl CuriosityModule {
- /// Build a curiosity grid covering an `area_w × area_h` metre region split
- /// into `grid × grid` cells. `beta` scales the intrinsic bonus magnitude.
- pub fn new(area_w: f64, area_h: f64, grid: u32, beta: f32) -> Self {
- let g = grid.max(1);
- let cells = (g as usize) * (g as usize);
- let cell_w = if area_w > 0.0 { area_w / g as f64 } else { 1.0 };
- let cell_h = if area_h > 0.0 { area_h / g as f64 } else { 1.0 };
- Self {
- counts: vec![0; cells],
- grid: g,
- cell_w,
- cell_h,
- beta,
- }
- }
-
- /// Map a world-coordinate to a flat cell index, clamped to the grid.
- fn cell_index(&self, x: f64, y: f64) -> usize {
- let gx = ((x / self.cell_w).floor() as i64).clamp(0, self.grid as i64 - 1) as usize;
- let gy = ((y / self.cell_h).floor() as i64).clamp(0, self.grid as i64 - 1) as usize;
- gy * self.grid as usize + gx
- }
-
- /// Record a visit and return the intrinsic reward bonus for novelty.
- ///
- /// The bonus is `beta / sqrt(count)` using the count *before* this visit is
- /// counted (a never-before-seen cell starts at count 1, giving the full
- /// `beta` bonus; the cell's count is then incremented).
- pub fn visit_bonus(&mut self, x: f64, y: f64) -> f32 {
- let idx = self.cell_index(x, y);
- // count BEFORE increment, treated as at least 1 for the first visit.
- let prior = self.counts[idx] + 1;
- let bonus = self.beta / (prior as f32).sqrt();
- self.counts[idx] = self.counts[idx].saturating_add(1);
- bonus
- }
-
- /// Total recorded visits across the whole grid.
- pub fn total_visits(&self) -> u64 {
- self.counts.iter().map(|&c| c as u64).sum()
- }
-}
-
-// ---------------------------------------------------------------------------
-// Meta-RL: MAML-style fast-weight adapter
-// ---------------------------------------------------------------------------
-
-/// MAML-style fast-weight adapter for few-shot in-flight adaptation.
-///
-/// Holds a meta-learned `base` vector of policy adjustments plus a `fast` vector
-/// of per-deployment deltas. The fast-weights adapt with a gradient-free inner
-/// step driven by the advantage signal, letting a freshly deployed swarm tune to
-/// local wind / sensor drift within a handful of steps. `reset_fast` clears the
-/// deployment-specific deltas while keeping the meta-learned base.
-pub struct MetaAdapter {
- base: Vec,
- fast: Vec,
- inner_lr: f32,
-}
-
-impl MetaAdapter {
- /// New adapter with a zeroed `dim`-length base and fast-weight vector.
- pub fn new(dim: usize, inner_lr: f32) -> Self {
- Self {
- base: vec![0.0; dim],
- fast: vec![0.0; dim],
- inner_lr,
- }
- }
-
- /// One inner-loop adaptation step from an advantage signal (few-shot).
- ///
- /// Moves the fast-weights along `advantage * feature_grad`, scaled by the
- /// inner learning rate — the gradient-free MAML inner update used while in
- /// flight. `feature_grad` shorter than the weight vector adapts only its
- /// leading dimensions; extra entries are ignored.
- pub fn adapt(&mut self, advantage: f32, feature_grad: &[f32]) {
- let n = self.fast.len().min(feature_grad.len());
- for (f, &g) in self.fast.iter_mut().zip(feature_grad.iter()).take(n) {
- *f += self.inner_lr * advantage * g;
- }
- }
-
- /// Current effective weights (base + fast).
- pub fn effective(&self) -> Vec {
- self.base
- .iter()
- .zip(self.fast.iter())
- .map(|(b, f)| b + f)
- .collect()
- }
-
- /// Reset fast-weights for a new deployment (keeps the meta-learned base).
- pub fn reset_fast(&mut self) {
- for f in self.fast.iter_mut() {
- *f = 0.0;
- }
- }
-
- /// Fold the current fast-weights into the meta-learned base (outer-loop
- /// consolidation) and clear the fast deltas.
- pub fn consolidate(&mut self) {
- for (b, f) in self.base.iter_mut().zip(self.fast.iter()) {
- *b += *f;
- }
- self.reset_fast();
- }
-}
-
-// ---------------------------------------------------------------------------
-// Reward shaping helper
-// ---------------------------------------------------------------------------
-
-/// Shape a base reward according to the selected learning pattern.
-///
-/// For curiosity-based patterns the intrinsic `curiosity_bonus` is added to the
-/// extrinsic `base`; for all other patterns the base reward passes through.
-pub fn shaped_reward(pattern: LearningPattern, base: f32, curiosity_bonus: f32) -> f32 {
- if pattern.uses_curiosity() {
- base + curiosity_bonus
- } else {
- base
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- const ALL: [LearningPattern; 4] = [
- LearningPattern::Mappo,
- LearningPattern::Ippo,
- LearningPattern::MappoCuriosity,
- LearningPattern::MetaRl,
- ];
-
- #[test]
- fn test_pattern_from_str_roundtrip() {
- for p in ALL {
- assert_eq!(
- LearningPattern::from_str(p.name()),
- p,
- "round-trip failed for {}",
- p.name()
- );
- }
- }
-
- #[test]
- fn test_centralized_vs_independent() {
- // Mappo IS centralized (CTDE); Ippo is NOT (independent learners).
- assert!(LearningPattern::Mappo.centralized_critic());
- assert!(!LearningPattern::Ippo.centralized_critic());
- // Curiosity and MetaRl are MAPPO-family → centralized.
- assert!(LearningPattern::MappoCuriosity.centralized_critic());
- assert!(LearningPattern::MetaRl.centralized_critic());
- }
-
- #[test]
- fn test_curiosity_bonus_decreases() {
- let mut cm = CuriosityModule::new(100.0, 100.0, 10, 1.0);
- let first = cm.visit_bonus(50.0, 50.0);
- let second = cm.visit_bonus(50.0, 50.0); // same cell again
- assert!(
- second < first,
- "novelty should decay: first={first}, second={second}"
- );
- }
-
- #[test]
- fn test_curiosity_bonus_in_bounds() {
- let mut cm = CuriosityModule::new(100.0, 100.0, 8, 0.5);
- // In-bounds, out-of-bounds, and negative coords all clamp safely.
- for &(x, y) in &[(0.0, 0.0), (50.0, 50.0), (999.0, -999.0), (-5.0, 1000.0)] {
- let b = cm.visit_bonus(x, y);
- assert!(b.is_finite(), "bonus must be finite, got {b}");
- assert!(b >= 0.0, "bonus must be >= 0, got {b}");
- }
- }
-
- #[test]
- fn test_meta_adapter_changes_weights() {
- let mut ma = MetaAdapter::new(4, 0.1);
- let base = ma.effective();
- ma.adapt(2.0, &[1.0, -1.0, 0.5, 0.0]);
- let adapted = ma.effective();
- assert_ne!(base, adapted, "adapt() must change effective weights");
- ma.reset_fast();
- assert_eq!(
- base,
- ma.effective(),
- "reset_fast() must restore the meta-learned base"
- );
- }
-
- #[test]
- fn test_shaped_reward_curiosity_only() {
- let base = 10.0;
- let bonus = 3.0;
- // MappoCuriosity adds the bonus.
- assert_eq!(
- shaped_reward(LearningPattern::MappoCuriosity, base, bonus),
- base + bonus
- );
- // Mappo does not.
- assert_eq!(shaped_reward(LearningPattern::Mappo, base, bonus), base);
- // Ippo and MetaRl also ignore the bonus.
- assert_eq!(shaped_reward(LearningPattern::Ippo, base, bonus), base);
- assert_eq!(shaped_reward(LearningPattern::MetaRl, base, bonus), base);
- }
-}
diff --git a/v2/crates/ruview-swarm/src/marl/mod.rs b/v2/crates/ruview-swarm/src/marl/mod.rs
deleted file mode 100644
index 42df6cb5..00000000
--- a/v2/crates/ruview-swarm/src/marl/mod.rs
+++ /dev/null
@@ -1,20 +0,0 @@
-pub mod actor;
-pub mod learning;
-pub mod observation;
-pub mod reward;
-pub mod role_attention;
-pub mod trainer;
-pub mod training_loop;
-
-pub use actor::{MappoActor, ActorConfig, ActorAction};
-pub use learning::{LearningPattern, CuriosityModule, MetaAdapter, shaped_reward};
-pub use observation::LocalObservation;
-pub use reward::{RewardCalculator, RewardContext};
-pub use role_attention::{NodeRole, RoleAttention, triangulation_geometry_penalty};
-pub use trainer::{TrainingConfig, TrainingMode, DomainRandomizationConfig};
-pub use training_loop::{ReplayBuffer, Transition, PpoConfig, UpdateStats, ppo_update};
-
-#[cfg(feature = "train")]
-pub mod candle_ppo;
-#[cfg(feature = "train")]
-pub use candle_ppo::{CandleActorCritic, CandlePpoConfig, CandleTrainer, select_device};
diff --git a/v2/crates/ruview-swarm/src/marl/observation.rs b/v2/crates/ruview-swarm/src/marl/observation.rs
deleted file mode 100644
index c90fe5e8..00000000
--- a/v2/crates/ruview-swarm/src/marl/observation.rs
+++ /dev/null
@@ -1,218 +0,0 @@
-use crate::types::{DroneState, NodeId, Position3D, GridCell, CsiDetection};
-
-/// Local observation vector for a single drone agent.
-/// Feeds into the MAPPO actor network.
-///
-/// Dimension breakdown:
-/// - own_state: 9 (pos xyz, vel xyz, heading, battery, link_quality)
-/// - neighbor_relative_pos: 18 (K=6 neighbours × 3 floats each)
-/// - grid_tile: 25 (5×5 cell victim probabilities)
-/// - csi_reading: 5 (confidence, est pos xyz, has_detection flag)
-/// - task_encoding: 7 (target xyz, deadline_norm, task_type one-hot × 3)
-///
-/// TOTAL: 64
-#[derive(Debug, Clone)]
-pub struct LocalObservation {
- /// Own state: [pos_x, pos_y, pos_z, vel_x, vel_y, vel_z, heading, battery, link_quality]
- pub own_state: [f32; 9],
- /// K=6 nearest-neighbour relative positions: [dx, dy, dz] × 6 = 18 floats
- pub neighbor_relative_pos: [f32; 18],
- /// 5×5 grid tile centred on drone position: victim_probability × 25
- pub grid_tile: [f32; 25],
- /// CSI reading: [confidence, est_x, est_y, est_z, has_detection]
- pub csi_reading: [f32; 5],
- /// Current task: [target_x, target_y, target_z, deadline_norm, task_type_one_hot × 3]
- pub task_encoding: [f32; 7],
-}
-
-impl LocalObservation {
- pub const DIM: usize = 9 + 18 + 25 + 5 + 7; // = 64
-
- /// Return an observation with all fields zeroed.
- pub fn zeros() -> Self {
- Self {
- own_state: [0.0; 9],
- neighbor_relative_pos: [0.0; 18],
- grid_tile: [0.0; 25],
- csi_reading: [0.0; 5],
- task_encoding: [0.0; 7],
- }
- }
-
- pub fn to_vec(&self) -> Vec {
- let mut v = Vec::with_capacity(Self::DIM);
- v.extend_from_slice(&self.own_state);
- v.extend_from_slice(&self.neighbor_relative_pos);
- v.extend_from_slice(&self.grid_tile);
- v.extend_from_slice(&self.csi_reading);
- v.extend_from_slice(&self.task_encoding);
- v
- }
-
- pub fn from_state(
- state: &DroneState,
- neighbors: &[(NodeId, Position3D)],
- grid_tile: [[GridCell; 5]; 5],
- csi_detection: Option<&crate::types::CsiDetection>,
- task_target: Option<&Position3D>,
- ) -> Self {
- let own_state = [
- state.position.x as f32 / 1000.0, // normalised to km
- state.position.y as f32 / 1000.0,
- state.position.z as f32 / 100.0,
- state.velocity.vx as f32 / 20.0, // normalised to max speed
- state.velocity.vy as f32 / 20.0,
- state.velocity.vz as f32 / 5.0,
- state.heading_rad as f32 / std::f32::consts::PI,
- state.battery_pct / 100.0,
- state.link_quality,
- ];
-
- let mut neighbor_relative_pos = [0.0f32; 18];
- for (i, (_, pos)) in neighbors.iter().take(6).enumerate() {
- let base = i * 3;
- neighbor_relative_pos[base] = (pos.x - state.position.x) as f32 / 100.0;
- neighbor_relative_pos[base + 1] = (pos.y - state.position.y) as f32 / 100.0;
- neighbor_relative_pos[base + 2] = (pos.z - state.position.z) as f32 / 10.0;
- }
-
- let mut grid_flat = [0.0f32; 25];
- for (r, row) in grid_tile.iter().enumerate() {
- for (c, cell) in row.iter().enumerate() {
- grid_flat[r * 5 + c] = cell.victim_probability;
- }
- }
-
- let csi_reading = if let Some(det) = csi_detection {
- let vp = det.victim_position.unwrap_or(state.position);
- [det.confidence, (vp.x / 100.0) as f32, (vp.y / 100.0) as f32, (vp.z / 10.0) as f32, 1.0]
- } else {
- [0.0, 0.0, 0.0, 0.0, 0.0]
- };
-
- let task_encoding: [f32; 7] = if let Some(target) = task_target {
- [
- (target.x / 100.0) as f32,
- (target.y / 100.0) as f32,
- (target.z / 10.0) as f32,
- 1.0, // deadline_norm: placeholder
- 1.0, 0.0, 0.0, // task_type one-hot: CoverCell
- ]
- } else {
- [0.0f32; 7]
- };
-
- Self {
- own_state,
- neighbor_relative_pos,
- grid_tile: grid_flat,
- csi_reading,
- task_encoding,
- }
- }
-
- /// Build an observation from a drone state without a pre-computed grid tile.
- /// The grid_tile component is left as zeros; use `from_state` when you have
- /// a populated grid available.
- pub fn from_state_no_grid(
- state: &DroneState,
- neighbors: &[(NodeId, Position3D)],
- csi_detection: Option<&CsiDetection>,
- task_target: Option<&Position3D>,
- ) -> Self {
- let own_state = [
- (state.position.x / 1000.0) as f32,
- (state.position.y / 1000.0) as f32,
- (state.position.z / 100.0) as f32,
- (state.velocity.vx / 20.0) as f32,
- (state.velocity.vy / 20.0) as f32,
- (state.velocity.vz / 5.0) as f32,
- (state.heading_rad / std::f64::consts::PI) as f32,
- state.battery_pct / 100.0,
- state.link_quality,
- ];
-
- let mut neighbor_relative_pos = [0.0f32; 18];
- for (i, (_, pos)) in neighbors.iter().take(6).enumerate() {
- let base = i * 3;
- neighbor_relative_pos[base] = ((pos.x - state.position.x) / 100.0) as f32;
- neighbor_relative_pos[base+1] = ((pos.y - state.position.y) / 100.0) as f32;
- neighbor_relative_pos[base+2] = ((pos.z - state.position.z) / 10.0) as f32;
- }
-
- let csi_reading = match csi_detection {
- Some(det) => {
- let vp = det.victim_position.unwrap_or(state.position);
- [det.confidence, (vp.x / 100.0) as f32, (vp.y / 100.0) as f32, (vp.z / 10.0) as f32, 1.0]
- }
- None => [0.0; 5],
- };
-
- let task_encoding: [f32; 7] = match task_target {
- Some(t) => [(t.x / 100.0) as f32, (t.y / 100.0) as f32, (t.z / 10.0) as f32, 1.0, 1.0, 0.0, 0.0],
- None => [0.0; 7],
- };
-
- Self {
- own_state,
- neighbor_relative_pos,
- grid_tile: [0.0; 25],
- csi_reading,
- task_encoding,
- }
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
- use crate::types::{DroneState, NodeId};
-
- #[test]
- fn observation_dimension() {
- assert_eq!(LocalObservation::DIM, 64);
- }
-
- #[test]
- fn to_vec_length() {
- let obs = LocalObservation {
- own_state: [0.0; 9],
- neighbor_relative_pos: [0.0; 18],
- grid_tile: [0.0; 25],
- csi_reading: [0.0; 5],
- task_encoding: [0.0; 7],
- };
- assert_eq!(obs.to_vec().len(), LocalObservation::DIM);
- }
-
- #[test]
- fn from_state_produces_correct_dim() {
- let state = DroneState::default_at_origin(NodeId(0));
- let grid = [[GridCell::default(); 5]; 5];
- let obs = LocalObservation::from_state(&state, &[], grid, None, None);
- assert_eq!(obs.to_vec().len(), LocalObservation::DIM);
- }
-
- #[test]
- fn test_observation_dim() {
- let obs = LocalObservation::zeros();
- assert_eq!(obs.to_vec().len(), LocalObservation::DIM);
- }
-
- #[test]
- fn test_from_state_battery_normalised() {
- use crate::types::Velocity3D;
- let state = DroneState {
- id: NodeId(0),
- position: Default::default(),
- velocity: Velocity3D::default(),
- heading_rad: 0.0,
- altitude_agl_m: 30.0,
- battery_pct: 75.0,
- link_quality: 0.9,
- timestamp_ms: 0,
- };
- let obs = LocalObservation::from_state_no_grid(&state, &[], None, None);
- assert!((obs.own_state[7] - 0.75).abs() < 1e-4, "battery should be normalised to 0.75");
- }
-}
diff --git a/v2/crates/ruview-swarm/src/marl/reward.rs b/v2/crates/ruview-swarm/src/marl/reward.rs
deleted file mode 100644
index 462e3fbb..00000000
--- a/v2/crates/ruview-swarm/src/marl/reward.rs
+++ /dev/null
@@ -1,144 +0,0 @@
-use crate::types::DroneState;
-
-/// Reward function for the MAPPO training loop.
-///
-/// Shaped reward components:
-/// +coverage_reward per new grid cell visited
-/// +detection_reward per confirmed victim detection
-/// +triangulation_reward per contribution to a triangulation event
-/// idle_penalty when no useful work done this step
-/// collision_penalty when nearest neighbour < min_separation_m
-/// geofence_penalty when drone breaches the mission boundary
-/// battery_depletion_penalty when battery runs out outside RTH range
-pub struct RewardCalculator {
- pub coverage_reward: f32,
- pub detection_reward: f32,
- pub triangulation_reward: f32,
- pub idle_penalty: f32,
- pub collision_penalty: f32,
- pub geofence_penalty: f32,
- pub battery_depletion_penalty: f32,
- pub min_separation_m: f64,
-}
-
-impl Default for RewardCalculator {
- fn default() -> Self {
- Self {
- coverage_reward: 10.0,
- detection_reward: 50.0,
- triangulation_reward: 5.0,
- idle_penalty: -2.0,
- collision_penalty: -100.0,
- geofence_penalty: -50.0,
- battery_depletion_penalty: -30.0,
- min_separation_m: 1.5,
- }
- }
-}
-
-/// Context needed to compute the reward for a single agent step.
-pub struct RewardContext<'a> {
- pub state: &'a DroneState,
- pub new_cells_covered: u32,
- pub victim_confirmed: bool,
- pub contributed_to_triangulation: bool,
- /// Distance to nearest neighbour, in metres.
- pub nearest_neighbor_dist: f64,
- pub geofence_breached: bool,
- pub battery_depleted_without_rth: bool,
-}
-
-impl RewardCalculator {
- /// Compute the scalar reward for one agent at one timestep.
- pub fn compute(&self, ctx: &RewardContext) -> f32 {
- let mut reward = 0.0f32;
-
- reward += ctx.new_cells_covered as f32 * self.coverage_reward;
-
- if ctx.victim_confirmed {
- reward += self.detection_reward;
- }
- if ctx.contributed_to_triangulation {
- reward += self.triangulation_reward;
- }
- // Idle penalty only when no positive work was done.
- if ctx.new_cells_covered == 0 && !ctx.victim_confirmed {
- reward += self.idle_penalty;
- }
- if ctx.nearest_neighbor_dist < self.min_separation_m {
- reward += self.collision_penalty;
- }
- if ctx.geofence_breached {
- reward += self.geofence_penalty;
- }
- if ctx.battery_depleted_without_rth {
- reward += self.battery_depletion_penalty;
- }
-
- reward
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
- use crate::types::{DroneState, NodeId};
-
- fn mk_state() -> DroneState {
- DroneState::default_at_origin(NodeId(0))
- }
-
- #[test]
- fn detection_reward_dominates() {
- let calc = RewardCalculator::default();
- let state = mk_state();
- let ctx = RewardContext {
- state: &state,
- new_cells_covered: 1,
- victim_confirmed: true,
- contributed_to_triangulation: false,
- nearest_neighbor_dist: 10.0,
- geofence_breached: false,
- battery_depleted_without_rth: false,
- };
- let r = calc.compute(&ctx);
- // 10 (coverage) + 50 (detection) = 60
- assert!((r - 60.0).abs() < 1e-4, "reward={}", r);
- }
-
- #[test]
- fn collision_dominates_idle() {
- let calc = RewardCalculator::default();
- let state = mk_state();
- let ctx = RewardContext {
- state: &state,
- new_cells_covered: 0,
- victim_confirmed: false,
- contributed_to_triangulation: false,
- nearest_neighbor_dist: 0.5, // < 1.5 m threshold
- geofence_breached: false,
- battery_depleted_without_rth: false,
- };
- let r = calc.compute(&ctx);
- // -2 (idle) + -100 (collision) = -102
- assert!((r - (-102.0)).abs() < 1e-4, "reward={}", r);
- }
-
- #[test]
- fn test_collision_dominates() {
- let calc = RewardCalculator::default();
- let state = mk_state();
- // 3 covered cells = +30, victim = false, collision = -100 → net -70
- let ctx = RewardContext {
- state: &state,
- new_cells_covered: 3,
- victim_confirmed: false,
- contributed_to_triangulation: false,
- nearest_neighbor_dist: 1.0, // collision (< 1.5 m threshold)
- geofence_breached: false,
- battery_depleted_without_rth: false,
- };
- let r = calc.compute(&ctx);
- assert!(r < 0.0, "collision (-100) should dominate coverage (+30), reward={}", r);
- }
-}
diff --git a/v2/crates/ruview-swarm/src/marl/role_attention.rs b/v2/crates/ruview-swarm/src/marl/role_attention.rs
deleted file mode 100644
index cd65a8ea..00000000
--- a/v2/crates/ruview-swarm/src/marl/role_attention.rs
+++ /dev/null
@@ -1,169 +0,0 @@
-//! A-MAPPO heterogeneous-role attention for sensor vs relay swarm nodes.
-//!
-//! Addresses four edge cases in heterogeneous swarms:
-//! 1. Attention collapse onto sensor nodes (relays produce no CSI → get zeroed out)
-//! 2. Variable neighbor cardinality (sensor clusters bunch, relays spread)
-//! 3. Flocking↔triangulation geometry tension (gated by role)
-//! 4. Relay→cluster-head handoff non-stationarity (role-dropout)
-//!
-//! Pure Rust — compiled in every build (no `train`/candle dependency).
-
-#[derive(Debug, Clone, Copy, PartialEq, Eq)]
-pub enum NodeRole {
- Sensor,
- Relay,
- ClusterHead,
-}
-
-impl NodeRole {
- /// One-hot role embedding appended to attention keys.
- pub fn embedding(&self) -> [f32; 3] {
- match self {
- NodeRole::Sensor => [1.0, 0.0, 0.0],
- NodeRole::Relay => [0.0, 1.0, 0.0],
- NodeRole::ClusterHead => [0.0, 0.0, 1.0],
- }
- }
-}
-
-pub struct RoleAttention {
- /// Minimum attention weight floor for relay nodes (prevents collapse).
- pub relay_floor: f32,
- /// Temperature for softmax.
- pub temperature: f32,
-}
-
-impl Default for RoleAttention {
- fn default() -> Self {
- Self { relay_floor: 0.05, temperature: 1.0 }
- }
-}
-
-impl RoleAttention {
- /// Compute role-aware attention weights over neighbors.
- /// `scores`: raw attention logits per neighbor. `roles`: each neighbor's role.
- /// Returns normalized weights with a floor applied to relay nodes so the
- /// comms backbone is never fully attention-starved.
- pub fn weights(&self, scores: &[f32], roles: &[NodeRole]) -> Vec {
- if scores.is_empty() {
- return vec![];
- }
- // Softmax with temperature
- let max = scores.iter().cloned().fold(f32::MIN, f32::max);
- let exps: Vec = scores
- .iter()
- .map(|s| ((s - max) / self.temperature).exp())
- .collect();
- let sum: f32 = exps.iter().sum();
- let mut w: Vec = exps.iter().map(|e| e / sum).collect();
- // Apply relay floor
- for (wi, role) in w.iter_mut().zip(roles.iter()) {
- if *role == NodeRole::Relay && *wi < self.relay_floor {
- *wi = self.relay_floor;
- }
- }
- // Renormalize
- let s: f32 = w.iter().sum();
- if s > 0.0 {
- for wi in w.iter_mut() {
- *wi /= s;
- }
- }
- w
- }
-
- /// Role-segmented attention: separate sensor-pool and relay-pool so a flat
- /// softmax over k-nearest (mostly same-role) doesn't break.
- pub fn segmented_weights(&self, scores: &[f32], roles: &[NodeRole]) -> Vec {
- let sensor_idx: Vec =
- (0..roles.len()).filter(|&i| roles[i] != NodeRole::Relay).collect();
- let relay_idx: Vec =
- (0..roles.len()).filter(|&i| roles[i] == NodeRole::Relay).collect();
- let mut out = vec![0.0f32; scores.len()];
- // Each pool gets a fixed share of the attention mass (if both populated).
- let pools = [(&sensor_idx, 0.6f32), (&relay_idx, 0.4f32)];
- let active_pools = pools.iter().filter(|(idx, _)| !idx.is_empty()).count();
- for (idx, mass) in pools.iter() {
- if idx.is_empty() {
- continue;
- }
- let pool_mass = if active_pools == 1 { 1.0 } else { *mass };
- let pool_scores: Vec = idx.iter().map(|&i| scores[i]).collect();
- let max = pool_scores.iter().cloned().fold(f32::MIN, f32::max);
- let exps: Vec = pool_scores
- .iter()
- .map(|s| ((s - max) / self.temperature).exp())
- .collect();
- let sum: f32 = exps.iter().sum();
- for (k, &i) in idx.iter().enumerate() {
- out[i] = pool_mass * exps[k] / sum;
- }
- }
- out
- }
-}
-
-/// Reward modifier protecting triangulation baseline geometry (ADR-148 §4.2).
-/// Penalizes sensor triads whose 3-nearest intersection angle drops below the
-/// minimum that keeps multi-view CSI fusion viable. Gated to SENSOR role only —
-/// relays are not dragged into triangulation geometry.
-pub fn triangulation_geometry_penalty(
- self_role: NodeRole,
- nearest_angles_deg: &[f32], // intersection angles to the 3 nearest sensors
- min_angle_deg: f32, // default 30.0
- penalty: f32, // e.g. -5.0
-) -> f32 {
- if self_role != NodeRole::Sensor {
- return 0.0;
- }
- let below = nearest_angles_deg
- .iter()
- .filter(|&&a| a < min_angle_deg)
- .count();
- below as f32 * penalty
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- #[test]
- fn test_relay_floor_prevents_collapse() {
- let attn = RoleAttention { relay_floor: 0.1, temperature: 1.0 };
- // Sensor scores high, relay scores near zero → relay would collapse
- let scores = vec![5.0, 5.0, -10.0];
- let roles = vec![NodeRole::Sensor, NodeRole::Sensor, NodeRole::Relay];
- let w = attn.weights(&scores, &roles);
- assert!(w[2] >= 0.09, "relay weight {} should respect floor", w[2]);
- let sum: f32 = w.iter().sum();
- assert!((sum - 1.0).abs() < 1e-4, "weights must sum to 1, got {}", sum);
- }
-
- #[test]
- fn test_segmented_splits_pools() {
- let attn = RoleAttention::default();
- let scores = vec![1.0, 1.0, 1.0];
- let roles = vec![NodeRole::Sensor, NodeRole::Sensor, NodeRole::Relay];
- let w = attn.segmented_weights(&scores, &roles);
- let relay_mass = w[2];
- assert!(relay_mass > 0.3 && relay_mass < 0.5, "relay pool ~0.4 mass, got {}", relay_mass);
- }
-
- #[test]
- fn test_triangulation_penalty_sensor_only() {
- // Relay: no penalty even with bad geometry
- assert_eq!(
- triangulation_geometry_penalty(NodeRole::Relay, &[10.0, 15.0, 20.0], 30.0, -5.0),
- 0.0
- );
- // Sensor: penalized per angle below 30°
- let p = triangulation_geometry_penalty(NodeRole::Sensor, &[10.0, 15.0, 40.0], 30.0, -5.0);
- assert_eq!(p, -10.0, "two angles below 30° → 2 × -5.0");
- }
-
- #[test]
- fn test_role_embedding_onehot() {
- assert_eq!(NodeRole::Sensor.embedding(), [1.0, 0.0, 0.0]);
- assert_eq!(NodeRole::Relay.embedding(), [0.0, 1.0, 0.0]);
- }
-}
diff --git a/v2/crates/ruview-swarm/src/marl/trainer.rs b/v2/crates/ruview-swarm/src/marl/trainer.rs
deleted file mode 100644
index 4860b632..00000000
--- a/v2/crates/ruview-swarm/src/marl/trainer.rs
+++ /dev/null
@@ -1,133 +0,0 @@
-use serde::{Deserialize, Serialize};
-
-/// Which environment the MARL training loop runs against.
-#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Default)]
-pub enum TrainingMode {
- /// Pure Rust simulation — no real hardware or external simulator.
- Simulation,
- /// Gazebo + PX4 SITL (requires Gazebo running on localhost).
- GazeboPx4Sitl { host: String, port: u16 },
- /// Hardware-in-the-loop: real drones, simulated mission world.
- HardwareInTheLoop,
- /// Demo mode: synthetic CSI with configurable victim positions.
- #[default]
- Demo,
-}
-
-/// Full MAPPO training configuration.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct TrainingConfig {
- pub mode: TrainingMode,
- pub num_drones: usize,
- pub num_episodes: usize,
- pub max_steps_per_episode: usize,
- /// PPO clip epsilon.
- pub clip_epsilon: f32,
- /// Generalised Advantage Estimation lambda.
- pub gae_lambda: f32,
- /// Adam learning rate.
- pub lr: f32,
- /// Entropy coefficient (encourages exploration).
- pub entropy_coeff: f32,
- /// Number of transitions per PPO update batch.
- pub batch_size: usize,
- /// PPO epochs per update step.
- pub ppo_epochs: usize,
- /// Domain randomisation settings applied per episode.
- pub domain_rand: DomainRandomizationConfig,
-}
-
-impl Default for TrainingConfig {
- fn default() -> Self {
- Self {
- mode: TrainingMode::Demo,
- num_drones: 4,
- num_episodes: 1000,
- max_steps_per_episode: 2000,
- clip_epsilon: 0.2,
- gae_lambda: 0.95,
- lr: 3e-4,
- entropy_coeff: 0.01,
- batch_size: 2048,
- ppo_epochs: 10,
- domain_rand: DomainRandomizationConfig::default(),
- }
- }
-}
-
-/// Per-episode domain randomisation parameters.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct DomainRandomizationConfig {
- /// Maximum wind speed (Dryden turbulence model), m/s.
- pub wind_max_ms: f64,
- /// Gaussian noise standard deviation added to CSI amplitude.
- pub csi_noise_std: f64,
- /// Fractional thrust coefficient variation: ±motor_thrust_variation.
- pub motor_thrust_variation: f64,
- /// Mean packet loss percentage [0–100].
- pub packet_loss_pct: f64,
- /// Maximum additional MAVLink latency injected, ms.
- pub extra_latency_max_ms: u64,
-}
-
-impl Default for DomainRandomizationConfig {
- fn default() -> Self {
- Self {
- wind_max_ms: 6.0,
- csi_noise_std: 0.05,
- motor_thrust_variation: 0.10,
- packet_loss_pct: 15.0,
- extra_latency_max_ms: 100,
- }
- }
-}
-
-impl TrainingConfig {
- /// Quick 10-episode demo run — suitable for CI smoke tests.
- pub fn quick_demo() -> Self {
- Self {
- mode: TrainingMode::Demo,
- num_drones: 4,
- num_episodes: 10,
- max_steps_per_episode: 200,
- ..Default::default()
- }
- }
-
- /// Full training preset with aggressive domain randomisation.
- pub fn full_training() -> Self {
- Self {
- num_episodes: 5000,
- max_steps_per_episode: 5000,
- domain_rand: DomainRandomizationConfig {
- wind_max_ms: 12.0,
- csi_noise_std: 0.1,
- motor_thrust_variation: 0.15,
- packet_loss_pct: 30.0,
- extra_latency_max_ms: 200,
- },
- ..Default::default()
- }
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- #[test]
- fn quick_demo_has_fewer_episodes() {
- let quick = TrainingConfig::quick_demo();
- let full = TrainingConfig::full_training();
- assert!(quick.num_episodes < full.num_episodes);
- assert_eq!(quick.mode, TrainingMode::Demo);
- }
-
- #[test]
- fn full_training_has_larger_domain_rand() {
- let full = TrainingConfig::full_training();
- let def = DomainRandomizationConfig::default();
- assert!(full.domain_rand.wind_max_ms > def.wind_max_ms);
- assert!(full.domain_rand.packet_loss_pct > def.packet_loss_pct);
- }
-}
diff --git a/v2/crates/ruview-swarm/src/marl/training_loop.rs b/v2/crates/ruview-swarm/src/marl/training_loop.rs
deleted file mode 100644
index 98692275..00000000
--- a/v2/crates/ruview-swarm/src/marl/training_loop.rs
+++ /dev/null
@@ -1,277 +0,0 @@
-//! Minimal MAPPO training loop — PPO policy gradient update on CPU.
-//!
-//! Production training uses Gazebo/PX4 SITL or the Demo environment.
-//! This module provides the update step itself, independent of the environment.
-
-use super::{
- actor::{ActorAction, MappoActor},
- observation::LocalObservation,
-};
-
-/// A single (observation, action, reward, next_observation, done) transition.
-#[derive(Debug, Clone)]
-pub struct Transition {
- pub obs: LocalObservation,
- pub action: ActorAction,
- pub reward: f32,
- pub next_obs: LocalObservation,
- pub done: bool,
-}
-
-/// Replay buffer for PPO — stores a fixed number of transitions per update.
-pub struct ReplayBuffer {
- pub transitions: Vec,
- pub capacity: usize,
-}
-
-impl ReplayBuffer {
- pub fn new(capacity: usize) -> Self {
- Self { transitions: Vec::with_capacity(capacity), capacity }
- }
-
- pub fn push(&mut self, t: Transition) {
- if self.transitions.len() >= self.capacity {
- self.transitions.remove(0);
- }
- self.transitions.push(t);
- }
-
- pub fn is_full(&self) -> bool {
- self.transitions.len() >= self.capacity
- }
-
- pub fn len(&self) -> usize { self.transitions.len() }
- pub fn is_empty(&self) -> bool { self.transitions.is_empty() }
-
- /// Compute discounted returns for all transitions (GAE-λ simplified to MC return).
- pub fn compute_returns(&self, gamma: f32) -> Vec {
- let n = self.transitions.len();
- let mut returns = vec![0.0f32; n];
- let mut running = 0.0f32;
- for i in (0..n).rev() {
- running = self.transitions[i].reward
- + gamma * running * (!self.transitions[i].done as i32 as f32);
- returns[i] = running;
- }
- returns
- }
-}
-
-/// PPO hyperparameters.
-#[derive(Debug, Clone)]
-pub struct PpoConfig {
- pub lr: f32,
- pub clip_epsilon: f32,
- pub gamma: f32,
- pub gae_lambda: f32,
- pub entropy_coeff: f32,
- pub epochs: usize,
-}
-
-impl Default for PpoConfig {
- fn default() -> Self {
- Self {
- lr: 3e-4,
- clip_epsilon: 0.2,
- gamma: 0.99,
- gae_lambda: 0.95,
- entropy_coeff: 0.01,
- epochs: 10,
- }
- }
-}
-
-/// Statistics from one PPO update step.
-#[derive(Debug, Clone, Default)]
-pub struct UpdateStats {
- pub mean_return: f32,
- pub policy_loss: f32,
- pub entropy: f32,
- pub updates: usize,
-}
-
-/// Compute mean return from a buffer.
-pub fn compute_mean_return(buffer: &ReplayBuffer, gamma: f32) -> f32 {
- let returns = buffer.compute_returns(gamma);
- if returns.is_empty() { return 0.0; }
- returns.iter().sum::() / returns.len() as f32
-}
-
-/// Simplified PPO policy gradient update.
-///
-/// In production this would use autodiff; here we use a finite-difference
-/// approximation for the pure-Rust MLP actor (no autograd required for demo).
-/// The production path should use Candle or burn for full gradient computation.
-///
-/// Returns update statistics.
-pub fn ppo_update(
- actor: &mut MappoActor,
- buffer: &ReplayBuffer,
- config: &PpoConfig,
-) -> UpdateStats {
- if buffer.is_empty() {
- return UpdateStats::default();
- }
-
- let returns = buffer.compute_returns(config.gamma);
- let mean_return = returns.iter().sum::() / returns.len() as f32;
-
- // Normalise returns
- let std_return = {
- let var = returns.iter()
- .map(|r| (r - mean_return).powi(2))
- .sum::() / returns.len() as f32;
- var.sqrt().max(1e-8)
- };
- let advantages: Vec = returns.iter()
- .map(|r| (r - mean_return) / std_return)
- .collect();
-
- // Finite-difference pseudo-gradient update on output layer bias
- // (production code would use autograd; this is a demo approximation)
- let fd_eps = config.lr * 0.01;
- let mut total_loss = 0.0f32;
-
- for (transition, advantage) in buffer.transitions.iter().zip(advantages.iter()) {
- let predicted = actor.forward(&transition.obs);
-
- // Log-prob proxy: use tanh(delta_heading) as action probability proxy
- let log_prob = (predicted.delta_heading_rad + 1e-8).abs().ln();
- let loss = -log_prob * advantage;
- total_loss += loss;
-
- // Nudge: update a single scalar in the direction of advantage
- // (This is a placeholder — real PPO needs full backprop)
- let _ = fd_eps * advantage; // consume value; real update would modify weights
- }
-
- let policy_loss = total_loss / buffer.len() as f32;
- // Entropy: uniform action distribution maximises entropy; proxy here
- let entropy = config.entropy_coeff * 0.5;
-
- UpdateStats {
- mean_return,
- policy_loss,
- entropy,
- updates: config.epochs,
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
- use crate::marl::{actor::ActorConfig, observation::LocalObservation};
-
- fn make_transition(reward: f32) -> Transition {
- Transition {
- obs: LocalObservation::zeros(),
- action: ActorAction {
- delta_heading_rad: 0.1,
- delta_altitude_m: 0.0,
- speed_ms: 4.0,
- trigger_csi_scan: false,
- },
- reward,
- next_obs: LocalObservation::zeros(),
- done: false,
- }
- }
-
- #[test]
- fn test_buffer_capacity() {
- let mut buf = ReplayBuffer::new(5);
- for i in 0..8 {
- buf.push(make_transition(i as f32));
- }
- assert_eq!(buf.len(), 5, "buffer should cap at capacity");
- }
-
- #[test]
- fn test_returns_monotone_positive() {
- let mut buf = ReplayBuffer::new(4);
- for _ in 0..4 { buf.push(make_transition(1.0)); }
- let returns = buf.compute_returns(0.99);
- // Each return should be >= 1.0 (positive reward accumulates)
- for r in &returns {
- assert!(*r >= 1.0, "all returns should be >= 1.0 with positive rewards");
- }
- // Returns should be non-decreasing from right to left
- for i in 0..returns.len() - 1 {
- assert!(returns[i] >= returns[i + 1],
- "earlier returns should be higher (more future reward)");
- }
- }
-
- #[test]
- fn test_ppo_update_produces_stats() {
- let mut actor = MappoActor::random_init(ActorConfig::default());
- let mut buf = ReplayBuffer::new(20);
- for i in 0..20 {
- buf.push(make_transition(if i % 2 == 0 { 10.0 } else { -2.0 }));
- }
- let stats = ppo_update(&mut actor, &buf, &PpoConfig::default());
- assert_ne!(stats.mean_return, 0.0, "mean return should be computed");
- assert_eq!(stats.updates, PpoConfig::default().epochs);
- }
-
- #[test]
- fn test_empty_buffer_no_crash() {
- let mut actor = MappoActor::random_init(ActorConfig::default());
- let buf = ReplayBuffer::new(20);
- let stats = ppo_update(&mut actor, &buf, &PpoConfig::default());
- assert_eq!(stats.mean_return, 0.0);
- assert_eq!(stats.updates, 0);
- }
-
- #[test]
- fn test_marl_convergence_improves_mean_return() {
- use rand::Rng;
-
- let mut actor = MappoActor::random_init(ActorConfig::default());
- let ppo_cfg = PpoConfig { lr: 1e-3, ..PpoConfig::default() };
- let mut rng = rand::thread_rng();
-
- // Collect transitions with varying rewards (simulate improvement trajectory)
- let mut buf = ReplayBuffer::new(64);
- for step in 0..64 {
- // Simulate improving rewards: early steps low reward, later steps higher
- let reward = if step < 32 {
- rng.gen_range(-5.0f32..-1.0)
- } else {
- rng.gen_range(1.0..15.0)
- };
- buf.push(Transition {
- obs: LocalObservation::zeros(),
- action: ActorAction {
- delta_heading_rad: 0.1,
- delta_altitude_m: 0.0,
- speed_ms: 5.0,
- trigger_csi_scan: true,
- },
- reward,
- next_obs: LocalObservation::zeros(),
- done: step == 63,
- });
- }
-
- // Run PPO update
- let stats = ppo_update(&mut actor, &buf, &ppo_cfg);
-
- // The mean return should reflect the mixed-reward trajectory
- assert!(stats.updates > 0, "PPO should have run updates");
- assert!(
- stats.mean_return.is_finite(),
- "mean return should be finite: {}",
- stats.mean_return
- );
- // With 32 negative + 32 positive rewards, mean should be non-zero
- assert!(
- stats.mean_return != 0.0,
- "mean return should be non-zero with varied rewards"
- );
-
- // Run multiple update cycles and verify stats are stable
- let stats2 = ppo_update(&mut actor, &buf, &ppo_cfg);
- assert!(stats2.mean_return.is_finite());
- }
-}
diff --git a/v2/crates/ruview-swarm/src/orchestrator/mod.rs b/v2/crates/ruview-swarm/src/orchestrator/mod.rs
deleted file mode 100644
index c06d3332..00000000
--- a/v2/crates/ruview-swarm/src/orchestrator/mod.rs
+++ /dev/null
@@ -1,415 +0,0 @@
-//! SwarmOrchestrator — wires together all swarm subsystems for a complete swarm node.
-//!
-//! Each physical drone runs one SwarmOrchestrator instance. In demo/sim mode it
-//! runs N orchestrators in one process to simulate a full swarm.
-
-use crate::{
- config::SwarmConfig,
- failsafe::{FailSafeMachine, FailSafeState},
- sensing::{
- multiview::MultiViewFusion,
- payload::{CsiPayloadPipeline, PayloadConfig},
- },
- planning::{
- coverage::CoverageStrategy,
- probability_grid::ProbabilityGrid,
- },
- types::{CsiDetection, DroneState, NodeId, Position3D, Velocity3D},
-};
-use std::collections::HashMap;
-
-/// The complete per-drone swarm coordinator.
-///
-/// In production: backed by live CSI payload and PX4 flight controller.
-/// In demo/sim: backed by synthetic CSI and simulated state.
-pub struct SwarmOrchestrator {
- pub node_id: NodeId,
- pub config: SwarmConfig,
- pub state: DroneState,
- pub failsafe: FailSafeMachine,
- pub coverage: CoverageStrategy,
- pub probability_grid: ProbabilityGrid,
- pub csi_pipeline: CsiPayloadPipeline,
- pub fusion: MultiViewFusion,
- /// Latest known positions of swarm peers.
- pub peer_states: HashMap,
- /// Detections received from peers (last cycle).
- pub peer_detections: Vec,
- /// Accumulated mission statistics.
- pub stats: MissionStats,
- /// Optional Ruflo backend for AgentDB, AIDefence, and SONA intelligence.
- /// When None (default), all Ruflo calls are no-ops — existing behaviour preserved.
- #[cfg(feature = "ruflo")]
- pub ruflo: Option>,
- /// Active trajectory ID issued by the Ruflo intelligence hooks.
- #[cfg(feature = "ruflo")]
- pub trajectory_id: Option,
-}
-
-/// Accumulated metrics for one mission run.
-#[derive(Debug, Clone, Default)]
-pub struct MissionStats {
- pub cells_covered: u32,
- pub victims_confirmed: u32,
- pub collision_events: u32,
- pub steps: u64,
- pub elapsed_secs: f64,
-}
-
-impl SwarmOrchestrator {
- /// Create a new orchestrator in demo mode (synthetic CSI).
- pub fn new_demo(
- node_id: NodeId,
- config: SwarmConfig,
- start_position: Position3D,
- victims: Vec,
- ) -> Self {
- let grid_w = (config.mission.area_width_m / config.mission.grid_resolution_m).ceil() as u32;
- let grid_h = (config.mission.area_height_m / config.mission.grid_resolution_m).ceil() as u32;
- let probability_grid =
- ProbabilityGrid::new(grid_w, grid_h, config.mission.grid_resolution_m);
-
- let noise_std = config.demo.as_ref().map(|d| d.csi_noise_std).unwrap_or(0.05);
- let detection_range = config.planning.csi_scan_width_m;
- let convergence_threshold = config.planning.convergence_threshold;
-
- let csi_pipeline = CsiPayloadPipeline::new_synthetic(
- node_id,
- PayloadConfig {
- scan_freq_hz: 10.0,
- detection_range_m: detection_range,
- confidence_threshold: 0.5,
- esp32_baud_rate: 921_600,
- },
- victims,
- noise_std,
- node_id.0 as u64,
- );
-
- let state = DroneState {
- id: node_id,
- position: start_position,
- velocity: Velocity3D::default(),
- heading_rad: 0.0,
- altitude_agl_m: config.planning.flight_altitude_m,
- battery_pct: 100.0,
- link_quality: 1.0,
- timestamp_ms: 0,
- };
-
- Self {
- node_id,
- config: config.clone(),
- state,
- failsafe: FailSafeMachine::new(),
- coverage: CoverageStrategy::new(convergence_threshold),
- probability_grid,
- csi_pipeline,
- fusion: MultiViewFusion::default(),
- peer_states: HashMap::new(),
- peer_detections: Vec::new(),
- stats: MissionStats::default(),
- #[cfg(feature = "ruflo")]
- ruflo: None,
- #[cfg(feature = "ruflo")]
- trajectory_id: None,
- }
- }
-
- /// Process one simulation step (dt_secs: time elapsed since last step).
- /// Returns the current fail-safe state after evaluation.
- pub async fn step(&mut self, dt_secs: f64, link_alive: bool) -> FailSafeState {
- self.stats.steps += 1;
- self.stats.elapsed_secs += dt_secs;
-
- // 1. Drain stale peer detections from previous cycle.
- self.peer_detections.clear();
-
- // 2. Evaluate fail-safe state machine.
- let nearest_dist = self.nearest_peer_distance();
- let fs_state = self.failsafe.tick(&self.state, link_alive, nearest_dist);
-
- if fs_state != FailSafeState::Nominal && fs_state != FailSafeState::LowBatteryWarn {
- return fs_state; // safety takes over; skip mission logic
- }
-
- // 3. CSI scan at current position.
- let current_pos = self.state.position;
- if let Some(detection) = self.csi_pipeline.scan(¤t_pos).await {
- if detection.confidence >= self.csi_pipeline.config.confidence_threshold {
- if let Some(victim_pos) = detection.victim_position {
- let cell = self.pos_to_cell(&victim_pos);
- self.probability_grid.update_bayesian(cell, detection.confidence, true);
- }
- }
- }
-
- // 4. Mark current cell as scanned.
- let cur_cell = self.pos_to_cell(¤t_pos);
- let was_new = self.probability_grid.mark_scanned(cur_cell);
- if was_new {
- self.stats.cells_covered += 1;
- }
-
- // 5. Update coverage phase based on grid state.
- self.coverage.phase_transition(&self.probability_grid);
-
- // 6. Move toward next waypoint (proportional navigation for simulation).
- if let Some(target) = self.coverage.next_target(&self.state, &self.probability_grid) {
- self.move_toward(target, dt_secs);
- }
-
- // 7. Simple battery drain: 1% per 30 s at full speed.
- self.state.battery_pct -= (dt_secs / 30.0) as f32;
- self.state.battery_pct = self.state.battery_pct.max(0.0);
- self.state.timestamp_ms += (dt_secs * 1_000.0) as u64;
-
- fs_state
- }
-
- /// Multi-drone CSI fusion at the cluster-head level.
- /// Returns a fused detection if enough viewpoints agree.
- pub fn fuse_detections(
- &self,
- all_detections: &[CsiDetection],
- all_positions: &[(NodeId, Position3D)],
- ) -> Option {
- self.fusion.fuse(all_detections, all_positions)
- }
-
- /// Accept an incoming peer state update (called by the swarm comm layer).
- pub fn receive_peer_state(&mut self, peer: DroneState) {
- self.peer_states.insert(peer.id, peer);
- }
-
- /// Accept an incoming CSI detection from a peer.
- pub fn receive_peer_detection(&mut self, det: CsiDetection) {
- self.peer_detections.push(det);
- }
-
- /// Attach a Ruflo backend for AgentDB pattern learning, AIDefence, and SONA.
- ///
- /// Call after `new_demo()`:
- /// ```ignore
- /// let orch = SwarmOrchestrator::new_demo(...)
- /// .with_ruflo(Box::new(MockRufloBackend::new()));
- /// ```
- #[cfg(feature = "ruflo")]
- pub fn with_ruflo(mut self, backend: Box) -> Self {
- self.ruflo = Some(backend);
- self
- }
-
- /// Start a Ruflo intelligence trajectory for this mission node.
- ///
- /// Call before the mission loop begins. If no backend is attached this is a no-op.
- #[cfg(feature = "ruflo")]
- pub async fn start_trajectory(&mut self, mission_desc: &str) {
- if let Some(ruflo) = &self.ruflo {
- match ruflo.trajectory_start(mission_desc, "swarm-specialist").await {
- Ok(tid) => self.trajectory_id = Some(tid),
- Err(e) => tracing::warn!("trajectory_start failed: {}", e),
- }
- }
- }
-
- /// End the Ruflo trajectory and persist the mission summary in AgentDB.
- ///
- /// Stores both a searchable memory entry and a pattern-learned description.
- /// If no backend is attached this is a no-op.
- #[cfg(feature = "ruflo")]
- pub async fn finish_trajectory(&mut self, success: bool, mission_key: &str) {
- if let Some(ruflo) = &self.ruflo {
- let tid = self.trajectory_id.take();
- if let Some(tid) = &tid {
- let _ = ruflo.trajectory_end(tid, success, None).await;
- }
- // Build and serialise mission summary.
- let summary = crate::ruflo::MissionSummary::from_stats(
- &self.stats,
- &self.config.mission.profile,
- 1, // single drone; caller sets correct count via separate API if needed
- self.config.mission.area_width_m,
- self.config.mission.area_height_m,
- 0, // caller sets victims_total; 0 = unknown
- self.probability_grid.coverage_pct(),
- );
- if let Ok(json) = serde_json::to_string(&summary) {
- let _ = ruflo.store_mission(mission_key, &json, "swarm-missions").await;
- }
- let _ = ruflo.store_pattern(
- &summary.to_pattern_description(),
- summary.pattern_type(),
- summary.pattern_confidence(),
- ).await;
- }
- }
-
- /// AIDefence-checked variant of `receive_peer_detection`.
- ///
- /// Returns `true` and enqueues the detection if it passes the safety check.
- /// Returns `false` (and drops the detection) if AIDefence flags it as unsafe.
- /// Falls back to `true` (accept) if the Ruflo backend is not attached or the
- /// check itself errors (fail-open to avoid blocking legitimate traffic).
- #[cfg(feature = "ruflo")]
- pub async fn receive_peer_detection_checked(&mut self, det: CsiDetection) -> bool {
- if let Some(ruflo) = &self.ruflo {
- // Serialise the detection to a string for AIDefence inspection.
- let repr = format!(
- "drone_id={:?} confidence={:.3} victim={:?}",
- det.drone_id, det.confidence, det.victim_position
- );
- match ruflo.mavlink_is_safe(&repr).await {
- Ok(false) => {
- tracing::warn!(
- "aidefence rejected peer detection from {:?}",
- det.drone_id
- );
- return false;
- }
- Err(e) => tracing::debug!("aidefence check failed (proceeding): {}", e),
- _ => {}
- }
- }
- self.receive_peer_detection(det);
- true
- }
-
- /// Returns true when the mission is considered complete.
- pub fn is_mission_complete(&self) -> bool {
- self.probability_grid.coverage_pct() > 0.95
- }
-
- // ──────────────────────── private helpers ────────────────────────
-
- /// Distance to the nearest peer drone (f64::MAX if no peers).
- fn nearest_peer_distance(&self) -> f64 {
- self.peer_states
- .values()
- .map(|p| self.state.position.distance_to(&p.position))
- .fold(f64::MAX, f64::min)
- }
-
- /// Convert a world position to grid cell indices, clamped to grid bounds.
- fn pos_to_cell(&self, pos: &Position3D) -> (u32, u32) {
- let r = self.config.mission.grid_resolution_m;
- let w = (self.config.mission.area_width_m / r) as u32;
- let h = (self.config.mission.area_height_m / r) as u32;
- let xi = (pos.x / r).max(0.0) as u32;
- let yi = (pos.y / r).max(0.0) as u32;
- (xi.min(w.saturating_sub(1)), yi.min(h.saturating_sub(1)))
- }
-
- /// Simple proportional navigation: steer toward target at max planning speed.
- fn move_toward(&mut self, target: Position3D, dt_secs: f64) {
- let dx = target.x - self.state.position.x;
- let dy = target.y - self.state.position.y;
- let dist = (dx * dx + dy * dy).sqrt();
-
- if dist < 0.5 {
- self.state.velocity = Velocity3D::default();
- return;
- }
-
- let speed = self.config.planning.max_speed_ms.min(dist / dt_secs);
- let vx = (dx / dist) * speed;
- let vy = (dy / dist) * speed;
-
- self.state.position.x += vx * dt_secs;
- self.state.position.y += vy * dt_secs;
- self.state.velocity = Velocity3D { vx, vy, vz: 0.0 };
- self.state.heading_rad = vy.atan2(vx);
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- fn demo_orchestrator(node_id: u32, victims: Vec) -> SwarmOrchestrator {
- let cfg = SwarmConfig::demo_default();
- SwarmOrchestrator::new_demo(
- NodeId(node_id),
- cfg,
- Position3D { x: 10.0 * node_id as f64, y: 0.0, z: -30.0 },
- victims,
- )
- }
-
- #[tokio::test]
- async fn test_single_orchestrator_step() {
- let mut orch =
- demo_orchestrator(0, vec![Position3D { x: 50.0, y: 50.0, z: 0.0 }]);
- let state = orch.step(0.1, true).await;
- assert_eq!(state, FailSafeState::Nominal);
- assert_eq!(orch.stats.steps, 1);
- }
-
- #[tokio::test]
- async fn test_failsafe_triggers_on_link_loss() {
- let mut orch = demo_orchestrator(0, vec![]);
- // Lower the hold threshold so it trips well within a sub-second test run.
- orch.failsafe.link_loss_hold_secs = 0.001;
- orch.failsafe.link_loss_rth_secs = 0.1;
-
- // One tick to start the link-loss timer, then sleep briefly so the
- // real-time elapsed exceeds the tiny hold threshold.
- orch.step(0.1, false).await;
- std::thread::sleep(std::time::Duration::from_millis(5));
-
- let state = orch.step(0.1, false).await;
- assert_ne!(state, FailSafeState::Nominal, "link loss should trigger failsafe");
- }
-
- #[tokio::test]
- async fn test_multi_drone_coverage() {
- let victims = vec![Position3D { x: 50.0, y: 50.0, z: 0.0 }];
- let mut drones: Vec =
- (0..4).map(|i| demo_orchestrator(i, victims.clone())).collect();
-
- // 50 steps × 0.1 s dt = 5 simulated seconds
- for _ in 0..50 {
- for drone in &mut drones {
- drone.step(0.1, true).await;
- }
- }
-
- let total_cells: u32 = drones.iter().map(|d| d.stats.cells_covered).sum();
- assert!(total_cells > 0, "drones should have covered some cells");
-
- let elapsed = drones[0].stats.elapsed_secs;
- assert!((elapsed - 5.0).abs() < 0.01, "elapsed should be ~5 s, got {elapsed}");
- }
-
- #[tokio::test]
- async fn test_peer_state_exchange() {
- let mut orch0 = demo_orchestrator(0, vec![]);
- let mut orch1 = demo_orchestrator(1, vec![]);
-
- orch0.step(0.1, true).await;
- orch1.step(0.1, true).await;
-
- // Exchange states
- orch0.receive_peer_state(orch1.state.clone());
- orch1.receive_peer_state(orch0.state.clone());
-
- assert!(
- orch0.peer_states.contains_key(&NodeId(1)),
- "orch0 should know about orch1"
- );
- }
-
- #[tokio::test]
- async fn test_mission_complete_after_full_coverage() {
- let mut orch = demo_orchestrator(0, vec![]);
- // Manually mark every cell scanned.
- let w = orch.probability_grid.width;
- let h = orch.probability_grid.height;
- for y in 0..h {
- for x in 0..w {
- orch.probability_grid.mark_scanned((x, y));
- }
- }
- assert!(orch.is_mission_complete(), "should be complete at 100% coverage");
- }
-}
diff --git a/v2/crates/ruview-swarm/src/planning/coverage.rs b/v2/crates/ruview-swarm/src/planning/coverage.rs
deleted file mode 100644
index f068ef14..00000000
--- a/v2/crates/ruview-swarm/src/planning/coverage.rs
+++ /dev/null
@@ -1,119 +0,0 @@
-//! Coverage strategy: systematic sweep → probabilistic pursuit → convergence.
-
-use crate::types::{DroneState, NodeId, Position3D};
-use super::probability_grid::ProbabilityGrid;
-use std::collections::HashMap;
-
-/// Phase of the coverage mission.
-#[derive(Debug, Clone)]
-pub enum Phase {
- /// Systematic boustrophedon sweep of the mission area.
- Systematic,
- /// Probabilistic pursuit: drones head toward high-P cells.
- ProbabilisticPursuit,
- /// Convergence on confirmed detections by the listed drones.
- Convergence(Vec),
-}
-
-/// Coverage strategy tracking phase and cell assignments.
-pub struct CoverageStrategy {
- pub phase: Phase,
- /// Assigned cell per drone.
- pub assignments: HashMap,
- pub convergence_threshold: f32,
-}
-
-impl CoverageStrategy {
- pub fn new(convergence_threshold: f32) -> Self {
- Self {
- phase: Phase::Systematic,
- assignments: HashMap::new(),
- convergence_threshold,
- }
- }
-
- /// Compute the next waypoint for a drone given the current grid.
- pub fn next_waypoint(
- &self,
- node_id: NodeId,
- state: &DroneState,
- grid: &ProbabilityGrid,
- flight_altitude_m: f64,
- ) -> Position3D {
- if let Phase::Convergence(_) = &self.phase {
- if let Some(&(cx, cy)) = self.assignments.get(&node_id) {
- return Position3D {
- x: cx as f64 * grid.cell_size_m,
- y: cy as f64 * grid.cell_size_m,
- z: -flight_altitude_m,
- };
- }
- }
-
- // Default: head toward the highest-priority unscanned cell.
- if let Some((cx, cy)) = grid.highest_priority_unscanned() {
- Position3D {
- x: cx as f64 * grid.cell_size_m,
- y: cy as f64 * grid.cell_size_m,
- z: -flight_altitude_m,
- }
- } else {
- state.position
- }
- }
-
- /// Return the next navigation target position for an orchestrator step.
- ///
- /// - Systematic phase: next unscanned boustrophedon cell.
- /// - ProbabilisticPursuit: highest-priority unscanned cell.
- /// - Convergence: highest-priority unscanned cell (refine around detections).
- pub fn next_target(&self, state: &DroneState, grid: &ProbabilityGrid) -> Option {
- let r = grid.cell_size_m;
- match &self.phase {
- Phase::Systematic => {
- grid.next_systematic_cell(state).map(|(cx, cy)| Position3D {
- x: cx as f64 * r + r / 2.0,
- y: cy as f64 * r + r / 2.0,
- z: state.position.z,
- })
- }
- Phase::ProbabilisticPursuit | Phase::Convergence(_) => {
- grid.highest_priority_unscanned().map(|(cx, cy)| Position3D {
- x: cx as f64 * r + r / 2.0,
- y: cy as f64 * r + r / 2.0,
- z: state.position.z,
- })
- }
- }
- }
-
- /// Transition to next phase based on grid state, guarded by a threshold.
- pub fn phase_transition_with_threshold(
- &mut self,
- grid: &ProbabilityGrid,
- _threshold: f32,
- ) {
- self.phase_transition(grid);
- }
-
- /// Transition to next phase based on grid state.
- pub fn phase_transition(&mut self, grid: &ProbabilityGrid) {
- let max_p = grid
- .cells
- .iter()
- .flat_map(|row| row.iter())
- .map(|c| c.victim_probability)
- .fold(0.0_f32, f32::max);
-
- self.phase = match &self.phase {
- Phase::Systematic if max_p >= self.convergence_threshold => {
- Phase::ProbabilisticPursuit
- }
- Phase::ProbabilisticPursuit if max_p >= 0.9 => {
- Phase::Convergence(vec![])
- }
- other => other.clone(),
- };
- }
-}
-
diff --git a/v2/crates/ruview-swarm/src/planning/mod.rs b/v2/crates/ruview-swarm/src/planning/mod.rs
deleted file mode 100644
index 5ff2a322..00000000
--- a/v2/crates/ruview-swarm/src/planning/mod.rs
+++ /dev/null
@@ -1,12 +0,0 @@
-//! Mission planning: coverage, probability grid, RRT-APF path planning.
-
-pub mod rrt_apf;
-pub mod coverage;
-pub mod probability_grid;
-pub mod pheromone;
-pub mod patterns;
-
-pub use rrt_apf::{RrtApfPlanner, Waypoint};
-pub use coverage::{CoverageStrategy, Phase};
-pub use probability_grid::ProbabilityGrid;
-pub use patterns::{FlightPattern, PatternContext};
diff --git a/v2/crates/ruview-swarm/src/planning/patterns.rs b/v2/crates/ruview-swarm/src/planning/patterns.rs
deleted file mode 100644
index 26e774e6..00000000
--- a/v2/crates/ruview-swarm/src/planning/patterns.rs
+++ /dev/null
@@ -1,428 +0,0 @@
-//! Flight / coverage-optimization patterns for swarm area search.
-//!
-//! Different strategies trade off coverage completeness, time, and robustness:
-//! - Boustrophedon: systematic lawnmower; complete but drones overlap if unpartitioned
-//! - PartitionedLawnmower: area split into per-drone strips → no overlap, ~Nx faster coverage
-//! - Spiral: outward spiral from a seed; good for centred search (last-known-position SAR)
-//! - Pheromone: stigmergic — steer away from recently-visited cells; robust to dropout
-//! - PotentialField: repelled by visited cells + peers, attracted to unscanned frontier
-//! - LevyFlight: heavy-tailed random walk; good exploration when target location unknown
-
-use crate::types::{NodeId, Position3D};
-
-#[derive(Debug, Clone, Copy, PartialEq, Eq, Default)]
-pub enum FlightPattern {
- Boustrophedon,
- #[default]
- PartitionedLawnmower,
- Spiral,
- Pheromone,
- PotentialField,
- LevyFlight,
-}
-
-impl FlightPattern {
- // Intentional inherent infallible parser (returns Self, not Result); shipped API.
- #[allow(clippy::should_implement_trait)]
- pub fn from_str(s: &str) -> Self {
- match s.to_lowercase().as_str() {
- "boustrophedon" | "lawnmower" => FlightPattern::Boustrophedon,
- "partitioned" | "partitioned_lawnmower" => FlightPattern::PartitionedLawnmower,
- "spiral" => FlightPattern::Spiral,
- "pheromone" | "stigmergic" => FlightPattern::Pheromone,
- "potential" | "potential_field" => FlightPattern::PotentialField,
- "levy" | "levyflight" | "levy_flight" => FlightPattern::LevyFlight,
- _ => FlightPattern::default(),
- }
- }
-
- pub fn name(&self) -> &'static str {
- match self {
- FlightPattern::Boustrophedon => "boustrophedon",
- FlightPattern::PartitionedLawnmower => "partitioned_lawnmower",
- FlightPattern::Spiral => "spiral",
- FlightPattern::Pheromone => "pheromone",
- FlightPattern::PotentialField => "potential_field",
- FlightPattern::LevyFlight => "levy_flight",
- }
- }
-
- /// All pattern variants, for enumeration / UI selection.
- pub fn all() -> [FlightPattern; 6] {
- [
- FlightPattern::Boustrophedon,
- FlightPattern::PartitionedLawnmower,
- FlightPattern::Spiral,
- FlightPattern::Pheromone,
- FlightPattern::PotentialField,
- FlightPattern::LevyFlight,
- ]
- }
-}
-
-/// Inputs for computing the next waypoint under a pattern.
-pub struct PatternContext<'a> {
- pub drone_id: NodeId,
- pub swarm_size: usize,
- pub current: Position3D,
- pub area_w: f64,
- pub area_h: f64,
- pub altitude_z: f64, // flight z (negative NED)
- pub scan_width_m: f64, // strip spacing
- pub step: u64, // tick counter (for deterministic pseudo-random patterns)
- pub visited: &'a [Position3D], // recently visited cell centres (for pheromone/potential)
- pub peers: &'a [Position3D], // peer positions (for potential-field repulsion)
-}
-
-impl FlightPattern {
- /// Compute the next target position for a drone under this pattern.
- pub fn next_target(&self, ctx: &PatternContext) -> Position3D {
- match self {
- FlightPattern::Boustrophedon => boustrophedon(ctx),
- FlightPattern::PartitionedLawnmower => partitioned_lawnmower(ctx),
- FlightPattern::Spiral => spiral(ctx),
- FlightPattern::Pheromone => pheromone(ctx),
- FlightPattern::PotentialField => potential_field(ctx),
- FlightPattern::LevyFlight => levy_flight(ctx),
- }
- }
-}
-
-/// Clamp a candidate (x, y) to the area bounds and lift it to the flight altitude.
-fn clamp_to_area(x: f64, y: f64, ctx: &PatternContext) -> Position3D {
- Position3D {
- x: x.clamp(0.0, ctx.area_w),
- y: y.clamp(0.0, ctx.area_h),
- z: ctx.altitude_z,
- }
-}
-
-/// Serpentine waypoint within a rectangular sub-region.
-///
-/// Walks rows of height `scan_width_m`; on each row sweeps left→right or
-/// right→left depending on the row parity, advancing one `scan_width_m`
-/// segment per `step`.
-fn serpentine_in_region(
- x0: f64,
- x1: f64,
- y0: f64,
- y1: f64,
- scan_width_m: f64,
- step: u64,
-) -> (f64, f64) {
- let strip_w = (x1 - x0).max(scan_width_m);
- let height = (y1 - y0).max(scan_width_m);
-
- // Number of horizontal segments per row before stepping to the next row.
- let cols = ((strip_w / scan_width_m).ceil() as u64).max(1);
- // Number of rows in this region.
- let rows = ((height / scan_width_m).ceil() as u64).max(1);
- let total = cols * rows;
- let s = step % total;
-
- let row = s / cols;
- let col = s % cols;
-
- // Centre of the current row band.
- let y = y0 + (row as f64 + 0.5) * scan_width_m;
- let y = y.min(y1);
-
- // Serpentine: even rows L→R, odd rows R→L.
- let along = if row.is_multiple_of(2) { col } else { cols - 1 - col };
- let x = x0 + (along as f64 + 0.5) * scan_width_m;
- let x = x.min(x1);
-
- (x, y)
-}
-
-/// Classic full-area serpentine lawnmower (drones may overlap — baseline).
-fn boustrophedon(ctx: &PatternContext) -> Position3D {
- let (x, y) = serpentine_in_region(
- 0.0,
- ctx.area_w,
- 0.0,
- ctx.area_h,
- ctx.scan_width_m,
- ctx.step,
- );
- clamp_to_area(x, y, ctx)
-}
-
-/// Partitioned lawnmower: split `area_w` into `swarm_size` vertical strips;
-/// drone `i` lawnmowers ONLY within strip `[i*w/n, (i+1)*w/n]`.
-///
-/// This is the clustering fix: each drone covers a disjoint band, so total
-/// coverage scales ~linearly with swarm size instead of all drones tracing
-/// the same path.
-fn partitioned_lawnmower(ctx: &PatternContext) -> Position3D {
- let n = ctx.swarm_size.max(1);
- let i = (ctx.drone_id.0 as usize) % n;
- let strip_w = ctx.area_w / n as f64;
- let x0 = i as f64 * strip_w;
- let x1 = x0 + strip_w;
-
- let (x, y) =
- serpentine_in_region(x0, x1, 0.0, ctx.area_h, ctx.scan_width_m, ctx.step);
- clamp_to_area(x, y, ctx)
-}
-
-/// Outward Archimedean spiral from the area centre; radius grows with step.
-fn spiral(ctx: &PatternContext) -> Position3D {
- let cx = ctx.area_w / 2.0;
- let cy = ctx.area_h / 2.0;
-
- // Angular step keeps successive waypoints roughly `scan_width_m` apart.
- let theta = ctx.step as f64 * 0.6;
- // Archimedean spiral r = b * theta; b chosen so each turn adds scan_width_m.
- let b = ctx.scan_width_m / (2.0 * std::f64::consts::PI);
- let r = b * theta;
-
- let x = cx + r * theta.cos();
- let y = cy + r * theta.sin();
- clamp_to_area(x, y, ctx)
-}
-
-/// Stigmergic: sample candidate headings, step toward the least-visited one.
-fn pheromone(ctx: &PatternContext) -> Position3D {
- let step_len = ctx.scan_width_m.max(1.0);
- // Deterministic base heading offset per drone so they diverge.
- let base = ctx.drone_id.0 as f64 * (std::f64::consts::PI / 3.0);
-
- let n_candidates = 8;
- let mut best: Option<(f64, f64, f64)> = None; // (score, x, y); lower score = less visited
- for k in 0..n_candidates {
- let theta = base + (k as f64) * (2.0 * std::f64::consts::PI / n_candidates as f64);
- let cx = ctx.current.x + step_len * theta.cos();
- let cy = ctx.current.y + step_len * theta.sin();
- let cx = cx.clamp(0.0, ctx.area_w);
- let cy = cy.clamp(0.0, ctx.area_h);
-
- // Penalty = sum of inverse-distance to recently-visited cell centres.
- let mut visit_pressure = 0.0;
- for v in ctx.visited {
- let d = (cx - v.x).hypot(cy - v.y);
- visit_pressure += 1.0 / (1.0 + d);
- }
- if best.as_ref().is_none_or(|(bs, _, _)| visit_pressure < *bs) {
- best = Some((visit_pressure, cx, cy));
- }
- }
-
- let (_, x, y) = best.unwrap_or((0.0, ctx.current.x, ctx.current.y));
- clamp_to_area(x, y, ctx)
-}
-
-/// Potential field: repelled by visited cells + peers, attracted to the
-/// nearest unscanned frontier; step in the resultant direction.
-fn potential_field(ctx: &PatternContext) -> Position3D {
- let mut fx = 0.0;
- let mut fy = 0.0;
-
- // Repulsion from recently-visited cells.
- for v in ctx.visited {
- let dx = ctx.current.x - v.x;
- let dy = ctx.current.y - v.y;
- let d2 = dx * dx + dy * dy + 1.0;
- let mag = 1.0 / d2;
- fx += dx / d2.sqrt() * mag;
- fy += dy / d2.sqrt() * mag;
- }
-
- // Repulsion from peers (collision / overlap avoidance).
- for p in ctx.peers {
- let dx = ctx.current.x - p.x;
- let dy = ctx.current.y - p.y;
- let d2 = dx * dx + dy * dy + 1.0;
- let mag = 2.0 / d2; // peers repel more strongly than stale trail
- fx += dx / d2.sqrt() * mag;
- fy += dy / d2.sqrt() * mag;
- }
-
- // Attraction toward the nearest unscanned frontier point. Sample a grid of
- // candidate area points; pick the one with greatest distance to any visited
- // cell (i.e. the least-explored region) and pull toward it.
- let mut frontier: Option<(f64, f64, f64)> = None; // (openness, x, y)
- let samples = 5;
- for ix in 0..=samples {
- for iy in 0..=samples {
- let px = ctx.area_w * ix as f64 / samples as f64;
- let py = ctx.area_h * iy as f64 / samples as f64;
- let mut nearest = f64::INFINITY;
- for v in ctx.visited {
- let d = (px - v.x).hypot(py - v.y);
- if d < nearest {
- nearest = d;
- }
- }
- if !nearest.is_finite() {
- nearest = (px - ctx.current.x).hypot(py - ctx.current.y);
- }
- if frontier.as_ref().is_none_or(|(o, _, _)| nearest > *o) {
- frontier = Some((nearest, px, py));
- }
- }
- }
- if let Some((_, gx, gy)) = frontier {
- let dx = gx - ctx.current.x;
- let dy = gy - ctx.current.y;
- let d = (dx * dx + dy * dy).sqrt().max(1e-6);
- fx += dx / d * 1.5; // attraction gain
- fy += dy / d * 1.5;
- }
-
- let fmag = (fx * fx + fy * fy).sqrt();
- let step_len = ctx.scan_width_m.max(1.0);
- let (x, y) = if fmag > 1e-9 {
- (
- ctx.current.x + fx / fmag * step_len,
- ctx.current.y + fy / fmag * step_len,
- )
- } else {
- (ctx.current.x, ctx.current.y)
- };
- clamp_to_area(x, y, ctx)
-}
-
-/// Deterministic pseudo-random heavy-tailed step (Lévy flight). Most steps are
-/// short; occasional long jumps. Seeded from drone_id + step via an LCG so the
-/// trajectory is reproducible.
-fn levy_flight(ctx: &PatternContext) -> Position3D {
- // Linear congruential generator (Numerical Recipes constants).
- let seed = (ctx.drone_id.0 as u64)
- .wrapping_mul(0x9E37_79B9_7F4A_7C15)
- .wrapping_add(ctx.step.wrapping_mul(0x2545_F491_4F6C_DD1D));
- let r1 = lcg(seed);
- let r2 = lcg(r1);
-
- let u_angle = (r1 >> 11) as f64 / (1u64 << 53) as f64; // [0,1)
- let u_len = ((r2 >> 11) as f64 / (1u64 << 53) as f64).max(1e-6); // (0,1]
-
- let theta = u_angle * 2.0 * std::f64::consts::PI;
- // Heavy-tailed step length: inverse power-law (Pareto-like), exponent ~1.5.
- let step_len = ctx.scan_width_m.max(1.0) * u_len.powf(-1.0 / 1.5);
- // Cap to the area diagonal so a single jump can't shoot arbitrarily far.
- let max_jump = (ctx.area_w * ctx.area_w + ctx.area_h * ctx.area_h).sqrt();
- let step_len = step_len.min(max_jump);
-
- let x = ctx.current.x + step_len * theta.cos();
- let y = ctx.current.y + step_len * theta.sin();
- clamp_to_area(x, y, ctx)
-}
-
-#[inline]
-fn lcg(state: u64) -> u64 {
- state
- .wrapping_mul(6364136223846793005)
- .wrapping_add(1442695040888963407)
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- fn ctx<'a>(
- drone_id: u32,
- swarm_size: usize,
- step: u64,
- current: Position3D,
- visited: &'a [Position3D],
- peers: &'a [Position3D],
- ) -> PatternContext<'a> {
- PatternContext {
- drone_id: NodeId(drone_id),
- swarm_size,
- current,
- area_w: 100.0,
- area_h: 80.0,
- altitude_z: -20.0,
- scan_width_m: 5.0,
- step,
- visited,
- peers,
- }
- }
-
- #[test]
- fn test_partitioned_strips_disjoint() {
- let empty: [Position3D; 0] = [];
- // Two drones, swarm of 2: drone 0 owns left half, drone 1 the right half.
- let mut d0_xs = Vec::new();
- let mut d1_xs = Vec::new();
- for s in 0..40u64 {
- let c0 = ctx(0, 2, s, Position3D::zero(), &empty, &empty);
- let c1 = ctx(1, 2, s, Position3D::zero(), &empty, &empty);
- d0_xs.push(FlightPattern::PartitionedLawnmower.next_target(&c0).x);
- d1_xs.push(FlightPattern::PartitionedLawnmower.next_target(&c1).x);
- }
- let mid = 100.0 / 2.0;
- // Drone 0 stays strictly in the left half, drone 1 strictly in the right.
- assert!(d0_xs.iter().all(|&x| x <= mid), "drone 0 left of midline");
- assert!(d1_xs.iter().all(|&x| x >= mid), "drone 1 right of midline");
- // And they never share an x position (disjoint strips → no overlap).
- for &a in &d0_xs {
- for &b in &d1_xs {
- assert!(a < b || (a <= mid && b >= mid), "strips overlap: {a} vs {b}");
- }
- }
- }
-
- #[test]
- fn test_all_patterns_in_bounds() {
- let visited = [
- Position3D { x: 10.0, y: 10.0, z: -20.0 },
- Position3D { x: 50.0, y: 40.0, z: -20.0 },
- ];
- let peers = [Position3D { x: 30.0, y: 20.0, z: -20.0 }];
- for pat in FlightPattern::all() {
- let mut current = Position3D { x: 25.0, y: 25.0, z: -20.0 };
- for s in 0..20u64 {
- let c = ctx(1, 4, s, current, &visited, &peers);
- let t = pat.next_target(&c);
- assert!(
- t.x >= 0.0 && t.x <= 100.0,
- "{} x out of bounds at step {s}: {}",
- pat.name(),
- t.x
- );
- assert!(
- t.y >= 0.0 && t.y <= 80.0,
- "{} y out of bounds at step {s}: {}",
- pat.name(),
- t.y
- );
- assert_eq!(t.z, -20.0, "{} altitude wrong", pat.name());
- current = t;
- }
- }
- }
-
- #[test]
- fn test_pattern_from_str_roundtrip() {
- for pat in FlightPattern::all() {
- assert_eq!(
- FlightPattern::from_str(pat.name()),
- pat,
- "roundtrip failed for {}",
- pat.name()
- );
- }
- }
-
- #[test]
- fn test_spiral_radius_grows() {
- let empty: [Position3D; 0] = [];
- let centre_x = 100.0 / 2.0;
- let centre_y = 80.0 / 2.0;
- let dist = |s: u64| {
- let c = ctx(0, 1, s, Position3D::zero(), &empty, &empty);
- let t = FlightPattern::Spiral.next_target(&c);
- ((t.x - centre_x).powi(2) + (t.y - centre_y).powi(2)).sqrt()
- };
- let near = dist(1);
- let far = dist(50);
- assert!(
- far > near,
- "spiral radius should grow: step1={near}, step50={far}"
- );
- }
-}
diff --git a/v2/crates/ruview-swarm/src/planning/pheromone.rs b/v2/crates/ruview-swarm/src/planning/pheromone.rs
deleted file mode 100644
index 6c9ddbc3..00000000
--- a/v2/crates/ruview-swarm/src/planning/pheromone.rs
+++ /dev/null
@@ -1,22 +0,0 @@
-//! Stigmergic pheromone evaporation for coverage tracking.
-
-use crate::types::GridCell;
-
-/// Evaporate pheromones across all cells.
-/// `rate`: fraction decayed per tick (e.g. 0.01 = 1% per tick).
-pub fn evaporate(cells: &mut [Vec], rate: f32) {
- for row in cells.iter_mut() {
- for cell in row.iter_mut() {
- cell.pheromone = (cell.pheromone * (1.0 - rate)).max(0.0);
- }
- }
-}
-
-/// Deposit pheromone at a cell (clamp to 1.0).
-pub fn deposit(cells: &mut [Vec], x: u32, y: u32, amount: f32) {
- if let Some(row) = cells.get_mut(y as usize) {
- if let Some(cell) = row.get_mut(x as usize) {
- cell.pheromone = (cell.pheromone + amount).min(1.0);
- }
- }
-}
diff --git a/v2/crates/ruview-swarm/src/planning/probability_grid.rs b/v2/crates/ruview-swarm/src/planning/probability_grid.rs
deleted file mode 100644
index 5a44e483..00000000
--- a/v2/crates/ruview-swarm/src/planning/probability_grid.rs
+++ /dev/null
@@ -1,153 +0,0 @@
-//! Bayesian probability grid for victim localization.
-
-use crate::types::GridCell;
-
-/// 2-D grid tracking posterior victim probability per cell.
-pub struct ProbabilityGrid {
- pub cells: Vec>,
- pub cell_size_m: f64,
- pub width: u32,
- pub height: u32,
-}
-
-impl ProbabilityGrid {
- pub fn new(width: u32, height: u32, cell_size_m: f64) -> Self {
- let cells = (0..height)
- .map(|y| {
- (0..width)
- .map(|x| GridCell {
- x_idx: x,
- y_idx: y,
- victim_probability: 0.5, // uninformative prior
- pheromone: 0.0,
- last_scanned_ms: 0,
- })
- .collect()
- })
- .collect();
- Self { cells, cell_size_m, width, height }
- }
-
- /// Bayesian update: P(victim | detection) or P(victim | no detection).
- pub fn update_bayesian(&mut self, cell: (u32, u32), confidence: f32, detected: bool) {
- let (cx, cy) = cell;
- if cx >= self.width || cy >= self.height {
- return;
- }
- let c = &mut self.cells[cy as usize][cx as usize];
- let prior = c.victim_probability as f64;
- // Likelihood ratio update
- let likelihood = if detected {
- confidence as f64
- } else {
- 1.0 - confidence as f64
- };
- let denom = likelihood * prior + (1.0 - likelihood) * (1.0 - prior);
- c.victim_probability = if denom > 1e-9 {
- (likelihood * prior / denom) as f32
- } else {
- prior as f32
- };
- c.pheromone = (c.pheromone + 0.1).min(1.0);
- }
-
- /// Returns the cell (x, y) with highest expected value: P * (1 - scanned_weight).
- pub fn highest_priority_unscanned(&self) -> Option<(u32, u32)> {
- let now_approx: u64 = 0; // caller should pass current time; use 0 for simplicity
- let _ = now_approx;
- let mut best: Option<((u32, u32), f32)> = None;
- for row in &self.cells {
- for cell in row {
- let scanned_weight = if cell.last_scanned_ms > 0 { cell.pheromone } else { 0.0 };
- let score = cell.victim_probability * (1.0 - scanned_weight);
- if best.as_ref().is_none_or(|(_, bs)| score > *bs) {
- best = Some(((cell.x_idx, cell.y_idx), score));
- }
- }
- }
- best.map(|(pos, _)| pos)
- }
-
- /// Mark a cell as scanned. Returns true if this is the first scan of this cell.
- pub fn mark_scanned(&mut self, cell: (u32, u32)) -> bool {
- let (cx, cy) = cell;
- if cx >= self.width || cy >= self.height {
- return false;
- }
- let c = &mut self.cells[cy as usize][cx as usize];
- if c.last_scanned_ms == 0 {
- c.last_scanned_ms = 1; // mark as visited
- true
- } else {
- false
- }
- }
-
- /// Fraction of cells that have been scanned at least once.
- pub fn coverage_pct(&self) -> f64 {
- let total: usize = self.cells.iter().flatten().count();
- let scanned: usize = self.cells.iter().flatten().filter(|c| c.last_scanned_ms > 0).count();
- if total == 0 { 1.0 } else { scanned as f64 / total as f64 }
- }
-
- /// Return the next cell for systematic boustrophedon sweep (row-by-row, unscanned first).
- pub fn next_systematic_cell(&self, _state: &crate::types::DroneState) -> Option<(u32, u32)> {
- // Walk rows in order; within each row alternate direction based on row parity.
- for yi in 0..self.height {
- let x_iter: Box> = if yi % 2 == 0 {
- Box::new(0..self.width)
- } else {
- Box::new((0..self.width).rev())
- };
- for xi in x_iter {
- if self.cells[yi as usize][xi as usize].last_scanned_ms == 0 {
- return Some((xi, yi));
- }
- }
- }
- None
- }
-
- /// Merge another grid's probabilities using weighted average.
- pub fn apply_gossip_update(&mut self, remote: &ProbabilityGrid) {
- let h = self.height.min(remote.height) as usize;
- let w = self.width.min(remote.width) as usize;
- for y in 0..h {
- for x in 0..w {
- let local = &mut self.cells[y][x];
- let r = remote.cells[y][x].victim_probability;
- local.victim_probability = (local.victim_probability + r) / 2.0;
- }
- }
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- #[test]
- fn test_bayesian_update_increases_probability() {
- let mut grid = ProbabilityGrid::new(10, 10, 2.0);
- grid.update_bayesian((5, 5), 0.9, true);
- assert!(grid.cells[5][5].victim_probability > 0.5);
- }
-
- #[test]
- fn test_bayesian_update_decreases_probability() {
- let mut grid = ProbabilityGrid::new(10, 10, 2.0);
- grid.update_bayesian((5, 5), 0.9, false);
- assert!(grid.cells[5][5].victim_probability < 0.5);
- }
-
- #[test]
- fn test_highest_priority_returns_cell() {
- let mut grid = ProbabilityGrid::new(5, 5, 2.0);
- // Boost one cell
- grid.cells[2][3].victim_probability = 0.99;
- grid.cells[2][3].pheromone = 0.0;
- let best = grid.highest_priority_unscanned();
- assert!(best.is_some());
- assert_eq!(best.unwrap(), (3, 2));
- }
-}
diff --git a/v2/crates/ruview-swarm/src/planning/rrt_apf.rs b/v2/crates/ruview-swarm/src/planning/rrt_apf.rs
deleted file mode 100644
index 9a4d55de..00000000
--- a/v2/crates/ruview-swarm/src/planning/rrt_apf.rs
+++ /dev/null
@@ -1,177 +0,0 @@
-//! RRT-APF hybrid path planner: Rapidly-exploring Random Trees with
-//! Artificial Potential Field obstacle repulsion.
-
-use crate::types::Position3D;
-use rand::Rng;
-
-/// A planned waypoint with an associated target speed.
-#[derive(Debug, Clone)]
-pub struct Waypoint {
- pub position: Position3D,
- pub speed_ms: f64,
-}
-
-/// RRT-APF path planner.
-pub struct RrtApfPlanner {
- pub obstacle_cells: Vec,
- pub apf_repulsion_dist: f64,
- pub step_size_m: f64,
-}
-
-impl RrtApfPlanner {
- pub fn new(apf_repulsion_dist: f64) -> Self {
- Self {
- obstacle_cells: Vec::new(),
- apf_repulsion_dist,
- step_size_m: 2.0,
- }
- }
-
- /// Compute the APF repulsion gradient at `pos` from all nearby obstacles.
- pub fn apf_force(&self, pos: &Position3D, neighbors: &[Position3D]) -> (f64, f64, f64) {
- let mut fx = 0.0_f64;
- let mut fy = 0.0_f64;
- let mut fz = 0.0_f64;
- for obs in self.obstacle_cells.iter().chain(neighbors.iter()) {
- let dist = pos.distance_to(obs);
- if dist < self.apf_repulsion_dist && dist > 1e-6 {
- let strength = (self.apf_repulsion_dist - dist) / (dist * dist);
- fx += strength * (pos.x - obs.x);
- fy += strength * (pos.y - obs.y);
- fz += strength * (pos.z - obs.z);
- }
- }
- (fx, fy, fz)
- }
-
- /// Plan a path from `start` to `goal` using RRT* with APF bias.
- pub fn plan(
- &self,
- start: Position3D,
- goal: Position3D,
- max_iter: usize,
- rng: &mut impl Rng,
- ) -> Vec {
- let mut tree: Vec<(Position3D, usize)> = vec![(start, 0)];
- let goal_dist_thresh = self.step_size_m * 1.5;
-
- for _ in 0..max_iter {
- // Sample random point (bias 10% toward goal)
- let sample = if rng.gen::() < 0.1 {
- goal
- } else {
- let range = 200.0_f64;
- Position3D {
- x: start.x + (rng.gen::() - 0.5) * range,
- y: start.y + (rng.gen::() - 0.5) * range,
- z: start.z,
- }
- };
-
- // Find nearest node in tree
- let (nearest_idx, nearest_pos) = tree
- .iter()
- .enumerate()
- .min_by(|(_, (a, _)), (_, (b, _))| {
- a.distance_to(&sample)
- .partial_cmp(&b.distance_to(&sample))
- .unwrap_or(std::cmp::Ordering::Equal)
- })
- .map(|(i, (p, _))| (i, *p))
- .unwrap_or((0, start));
-
- // Step toward sample, then apply APF
- let dist_to_sample = nearest_pos.distance_to(&sample);
- if dist_to_sample < 1e-9 {
- continue;
- }
- let scale = self.step_size_m / dist_to_sample;
- let mut new_pos = Position3D {
- x: nearest_pos.x + (sample.x - nearest_pos.x) * scale,
- y: nearest_pos.y + (sample.y - nearest_pos.y) * scale,
- z: nearest_pos.z + (sample.z - nearest_pos.z) * scale,
- };
-
- // Apply APF correction
- let (fx, fy, fz) = self.apf_force(&new_pos, &[]);
- let apf_scale = 0.3;
- new_pos.x += fx * apf_scale;
- new_pos.y += fy * apf_scale;
- new_pos.z += fz * apf_scale;
-
- tree.push((new_pos, nearest_idx));
-
- if new_pos.distance_to(&goal) <= goal_dist_thresh {
- // Trace path back to root
- let mut path = Vec::new();
- let mut current_idx = tree.len() - 1;
- while current_idx != 0 {
- let (pos, parent) = tree[current_idx];
- path.push(Waypoint { position: pos, speed_ms: 5.0 });
- current_idx = parent;
- }
- path.push(Waypoint { position: start, speed_ms: 5.0 });
- path.reverse();
- path.push(Waypoint { position: goal, speed_ms: 2.0 });
- return path;
- }
- }
-
- // Fallback: direct line
- vec![
- Waypoint { position: start, speed_ms: 5.0 },
- Waypoint { position: goal, speed_ms: 5.0 },
- ]
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- #[test]
- fn test_plan_returns_at_least_two_waypoints() {
- let planner = RrtApfPlanner::new(3.0);
- let start = Position3D { x: 0.0, y: 0.0, z: -30.0 };
- let goal = Position3D { x: 50.0, y: 50.0, z: -30.0 };
- let mut rng = rand::thread_rng();
- let path = planner.plan(start, goal, 500, &mut rng);
- assert!(path.len() >= 2);
- }
-
- #[test]
- fn test_apf_force_pushes_away() {
- let planner = RrtApfPlanner {
- obstacle_cells: vec![Position3D { x: 1.0, y: 0.0, z: 0.0 }],
- apf_repulsion_dist: 5.0,
- step_size_m: 2.0,
- };
- let pos = Position3D { x: 0.0, y: 0.0, z: 0.0 };
- let (fx, _, _) = planner.apf_force(&pos, &[]);
- assert!(fx < 0.0); // pushed away from x=1 obstacle
- }
-
- #[test]
- fn test_plan_reaches_goal() {
- let planner = RrtApfPlanner::new(3.0);
- let start = Position3D { x: 0.0, y: 0.0, z: -30.0 };
- let goal = Position3D { x: 50.0, y: 50.0, z: -30.0 };
- let mut rng = rand::thread_rng();
- let path = planner.plan(start, goal, 500, &mut rng);
- let last = path.last().unwrap();
- // The RRT either reaches goal directly or the fallback end is the goal itself.
- assert!(last.position.distance_to(&goal) < 10.0, "path should end near goal");
- }
-
- #[test]
- fn test_apf_repulsion_nonzero_near_obstacle() {
- let planner = RrtApfPlanner {
- obstacle_cells: vec![Position3D { x: 3.0, y: 0.0, z: 0.0 }],
- apf_repulsion_dist: 5.0,
- step_size_m: 2.0,
- };
- let pos = Position3D { x: 0.0, y: 0.0, z: 0.0 };
- let (fx, _, _) = planner.apf_force(&pos, &[]);
- assert!(fx < 0.0, "repulsion should push away from obstacle (negative x)");
- }
-}
diff --git a/v2/crates/ruview-swarm/src/ruflo/backend.rs b/v2/crates/ruview-swarm/src/ruflo/backend.rs
deleted file mode 100644
index f124351a..00000000
--- a/v2/crates/ruview-swarm/src/ruflo/backend.rs
+++ /dev/null
@@ -1,69 +0,0 @@
-//! RufloBackend trait and shared types.
-use async_trait::async_trait;
-
-/// Error type for Ruflo backend operations.
-#[derive(Debug, thiserror::Error)]
-pub enum RufloError {
- #[error("network error: {0}")]
- Network(String),
- #[error("tool error: {0}")]
- Tool(String),
- #[error("serialization error: {0}")]
- Serialize(String),
-}
-
-/// A past mission retrieved from AgentDB memory.
-#[derive(Debug, Clone, serde::Deserialize, Default)]
-pub struct MissionMemoryEntry {
- pub key: String,
- pub value: String, // JSON-encoded mission summary
- pub score: f32,
-}
-
-/// A coordination pattern retrieved from AgentDB pattern store.
-#[derive(Debug, Clone, serde::Deserialize, Default)]
-pub struct PatternEntry {
- pub pattern: String,
- pub pattern_type: String,
- pub confidence: f32,
- pub score: f32,
-}
-
-/// Result of an AIDefence MAVLink message scan.
-#[derive(Debug, Clone)]
-pub struct MavlinkScanResult {
- pub safe: bool,
- pub threats: Vec,
-}
-
-/// Core Ruflo capability trait.
-///
-/// Two implementations:
-/// - `HttpRufloBackend` (feature=ruflo): calls the claude-flow daemon at localhost:3000
-/// - `MockRufloBackend`: in-memory mock for testing (always available)
-#[async_trait]
-pub trait RufloBackend: Send + Sync {
- // ── MissionMemory (claude-flow: memory_store / memory_search) ────
- async fn store_mission(&self, key: &str, summary: &str, namespace: &str)
- -> Result<(), RufloError>;
- async fn search_missions(&self, query: &str, limit: usize, namespace: &str)
- -> Result, RufloError>;
-
- // ── PatternLearner (agentdb_pattern-store / agentdb_pattern-search) ─
- async fn store_pattern(&self, pattern: &str, pattern_type: &str, confidence: f32)
- -> Result<(), RufloError>;
- async fn search_patterns(&self, query: &str, top_k: usize, min_confidence: f32)
- -> Result, RufloError>;
-
- // ── MavlinkDefence (aidefence_is_safe / aidefence_scan) ──────────
- async fn mavlink_is_safe(&self, message_repr: &str) -> Result;
- async fn mavlink_scan(&self, message_repr: &str) -> Result;
-
- // ── IntelligenceHooks (hooks_intelligence_trajectory-*) ──────────
- async fn trajectory_start(&self, task: &str, agent: &str)
- -> Result; // returns trajectoryId
- async fn trajectory_step(&self, trajectory_id: &str, action: &str, result: &str, quality: f32)
- -> Result<(), RufloError>;
- async fn trajectory_end(&self, trajectory_id: &str, success: bool, feedback: Option<&str>)
- -> Result<(), RufloError>;
-}
diff --git a/v2/crates/ruview-swarm/src/ruflo/http_backend.rs b/v2/crates/ruview-swarm/src/ruflo/http_backend.rs
deleted file mode 100644
index 7a934518..00000000
--- a/v2/crates/ruview-swarm/src/ruflo/http_backend.rs
+++ /dev/null
@@ -1,173 +0,0 @@
-//! HTTP backend that calls the claude-flow daemon via JSON-RPC 2.0.
-//! Default endpoint: http://localhost:3000/rpc
-//!
-//! Start the daemon with: npx @claude-flow/cli@latest daemon start
-
-use async_trait::async_trait;
-use std::sync::atomic::{AtomicU64, Ordering};
-use std::time::Duration;
-use super::backend::*;
-
-/// Per-request timeout applied to every JSON-RPC call.
-/// A dead or slow daemon must not stall swarm operation loops.
-const REQUEST_TIMEOUT: Duration = Duration::from_secs(5);
-
-pub struct HttpRufloBackend {
- client: reqwest::Client,
- base_url: String,
- request_id: AtomicU64,
-}
-
-impl HttpRufloBackend {
- pub fn new(base_url: &str) -> Self {
- let client = reqwest::Client::builder()
- .timeout(REQUEST_TIMEOUT)
- .build()
- .expect("failed to build reqwest client");
- Self {
- client,
- base_url: base_url.trim_end_matches('/').to_string(),
- request_id: AtomicU64::new(1),
- }
- }
-
- pub fn localhost() -> Self { Self::new("http://localhost:3000") }
-
- async fn call_tool(
- &self,
- tool: &str,
- args: serde_json::Value,
- ) -> Result {
- let id = self.request_id.fetch_add(1, Ordering::SeqCst);
- let body = serde_json::json!({
- "jsonrpc": "2.0",
- "method": "tools/call",
- "id": id,
- "params": { "name": tool, "arguments": args }
- });
-
- let resp = self.client
- .post(format!("{}/rpc", self.base_url))
- .json(&body)
- .send()
- .await
- .map_err(|e| RufloError::Network(e.to_string()))?;
-
- let json: serde_json::Value = resp.json().await
- .map_err(|e| RufloError::Serialize(e.to_string()))?;
-
- if let Some(err) = json.get("error") {
- return Err(RufloError::Tool(err.to_string()));
- }
-
- Ok(json["result"].clone())
- }
-}
-
-#[async_trait]
-impl RufloBackend for HttpRufloBackend {
- async fn store_mission(&self, key: &str, value: &str, namespace: &str)
- -> Result<(), RufloError>
- {
- self.call_tool("memory_store", serde_json::json!({
- "key": key, "value": value, "namespace": namespace
- })).await?;
- Ok(())
- }
-
- async fn search_missions(&self, query: &str, limit: usize, namespace: &str)
- -> Result, RufloError>
- {
- let result = self.call_tool("memory_search", serde_json::json!({
- "query": query, "namespace": namespace, "limit": limit
- })).await?;
- let entries: Vec = serde_json::from_value(result)
- .unwrap_or_default();
- Ok(entries)
- }
-
- async fn store_pattern(&self, pattern: &str, pattern_type: &str, confidence: f32)
- -> Result<(), RufloError>
- {
- self.call_tool("agentdb_pattern-store", serde_json::json!({
- "pattern": pattern, "type": pattern_type, "confidence": confidence
- })).await?;
- Ok(())
- }
-
- async fn search_patterns(&self, query: &str, top_k: usize, min_confidence: f32)
- -> Result, RufloError>
- {
- let result = self.call_tool("agentdb_pattern-search", serde_json::json!({
- "query": query, "topK": top_k, "minConfidence": min_confidence
- })).await?;
- let entries: Vec = serde_json::from_value(
- result["results"].clone()
- ).unwrap_or_default();
- Ok(entries)
- }
-
- async fn mavlink_is_safe(&self, message_repr: &str) -> Result {
- let result = self.call_tool("aidefence_is_safe", serde_json::json!({
- "input": message_repr
- })).await?;
- Ok(result["safe"].as_bool().unwrap_or(true))
- }
-
- async fn mavlink_scan(&self, message_repr: &str) -> Result {
- let result = self.call_tool("aidefence_scan", serde_json::json!({
- "input": message_repr, "quick": false
- })).await?;
- let safe = result["safe"].as_bool().unwrap_or(true);
- let threats: Vec = result["threats"]
- .as_array()
- .map(|a| a.iter().filter_map(|v| v["type"].as_str().map(String::from)).collect())
- .unwrap_or_default();
- Ok(MavlinkScanResult { safe, threats })
- }
-
- async fn trajectory_start(&self, task: &str, agent: &str)
- -> Result
- {
- let result = self.call_tool("hooks_intelligence_trajectory-start", serde_json::json!({
- "task": task, "agent": agent
- })).await?;
- Ok(result["trajectoryId"]
- .as_str()
- .unwrap_or("unknown-traj")
- .to_string())
- }
-
- async fn trajectory_step(
- &self,
- trajectory_id: &str,
- action: &str,
- result_str: &str,
- quality: f32,
- ) -> Result<(), RufloError> {
- self.call_tool("hooks_intelligence_trajectory-step", serde_json::json!({
- "trajectoryId": trajectory_id,
- "action": action,
- "result": result_str,
- "quality": quality
- })).await?;
- Ok(())
- }
-
- async fn trajectory_end(
- &self,
- trajectory_id: &str,
- success: bool,
- feedback: Option<&str>,
- ) -> Result<(), RufloError> {
- let mut args = serde_json::json!({
- "trajectoryId": trajectory_id,
- "success": success
- });
- if let Some(fb) = feedback {
- args["feedback"] = fb.into();
- }
- self.call_tool("hooks_intelligence_trajectory-end", args).await?;
- Ok(())
- }
-}
diff --git a/v2/crates/ruview-swarm/src/ruflo/mission_summary.rs b/v2/crates/ruview-swarm/src/ruflo/mission_summary.rs
deleted file mode 100644
index ed351de6..00000000
--- a/v2/crates/ruview-swarm/src/ruflo/mission_summary.rs
+++ /dev/null
@@ -1,125 +0,0 @@
-//! Serializable mission summary stored in AgentDB memory after each completed mission.
-use serde::{Deserialize, Serialize};
-use crate::orchestrator::MissionStats;
-
-/// Serializable summary of a completed mission stored in AgentDB.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct MissionSummary {
- pub mission_profile: String,
- pub num_drones: usize,
- pub area_width_m: f64,
- pub area_height_m: f64,
- pub victims_total: usize,
- pub victims_confirmed: u32,
- pub cells_covered: u32,
- pub coverage_pct: f64,
- pub elapsed_secs: f64,
- pub collision_events: u32,
- pub localization_error_m: Option,
-}
-
-impl MissionSummary {
- pub fn from_stats(
- stats: &MissionStats,
- profile: &str,
- num_drones: usize,
- area_width: f64,
- area_height: f64,
- victims_total: usize,
- coverage_pct: f64,
- ) -> Self {
- Self {
- mission_profile: profile.to_string(),
- num_drones,
- area_width_m: area_width,
- area_height_m: area_height,
- victims_total,
- victims_confirmed: stats.victims_confirmed,
- cells_covered: stats.cells_covered,
- coverage_pct,
- elapsed_secs: stats.elapsed_secs,
- collision_events: stats.collision_events,
- localization_error_m: None,
- }
- }
-
- /// Pattern description for AgentDB pattern-store — human-readable.
- pub fn to_pattern_description(&self) -> String {
- format!(
- "{} mission: {} drones over {}x{}m, {} victims confirmed in {:.1}s, {:.0}% coverage, {} collisions",
- self.mission_profile,
- self.num_drones,
- self.area_width_m as u32,
- self.area_height_m as u32,
- self.victims_confirmed,
- self.elapsed_secs,
- self.coverage_pct * 100.0,
- self.collision_events,
- )
- }
-
- /// Pattern type tag for AgentDB.
- pub fn pattern_type(&self) -> &str {
- match self.mission_profile.as_str() {
- "sar" => "sar-mission",
- "inspection" => "inspection-mission",
- "mine" => "mine-mission",
- _ => "swarm-mission",
- }
- }
-
- /// Confidence score (0-1) for AgentDB based on mission outcomes.
- pub fn pattern_confidence(&self) -> f32 {
- let victim_score = if self.victims_total > 0 {
- self.victims_confirmed as f32 / self.victims_total as f32
- } else {
- 0.5
- };
- let coverage_score = self.coverage_pct as f32;
- let collision_penalty = (self.collision_events as f32 * 0.1).min(0.5);
- ((victim_score * 0.5 + coverage_score * 0.5) - collision_penalty).clamp(0.0, 1.0)
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- fn make_stats(victims_confirmed: u32, cells_covered: u32, collision_events: u32) -> MissionStats {
- MissionStats {
- cells_covered,
- victims_confirmed,
- collision_events,
- steps: 100,
- elapsed_secs: 30.0,
- }
- }
-
- #[test]
- fn test_pattern_type_tags() {
- let stats = make_stats(2, 80, 0);
- let s = MissionSummary::from_stats(&stats, "sar", 4, 400.0, 400.0, 3, 0.85);
- assert_eq!(s.pattern_type(), "sar-mission");
-
- let s2 = MissionSummary::from_stats(&stats, "custom", 2, 200.0, 200.0, 0, 0.5);
- assert_eq!(s2.pattern_type(), "swarm-mission");
- }
-
- #[test]
- fn test_pattern_confidence_penalises_collisions() {
- let no_collisions = make_stats(3, 80, 0);
- let with_collisions = make_stats(3, 80, 4);
- let s_good = MissionSummary::from_stats(&no_collisions, "sar", 4, 400.0, 400.0, 3, 0.9);
- let s_bad = MissionSummary::from_stats(&with_collisions, "sar", 4, 400.0, 400.0, 3, 0.9);
- assert!(s_good.pattern_confidence() > s_bad.pattern_confidence());
- }
-
- #[test]
- fn test_to_pattern_description_contains_profile() {
- let stats = make_stats(1, 50, 0);
- let s = MissionSummary::from_stats(&stats, "inspection", 2, 100.0, 100.0, 1, 0.75);
- let desc = s.to_pattern_description();
- assert!(desc.contains("inspection"), "description should include profile: {desc}");
- assert!(desc.contains("2 drones"), "description should include drone count: {desc}");
- }
-}
diff --git a/v2/crates/ruview-swarm/src/ruflo/mock_backend.rs b/v2/crates/ruview-swarm/src/ruflo/mock_backend.rs
deleted file mode 100644
index 6a70c13b..00000000
--- a/v2/crates/ruview-swarm/src/ruflo/mock_backend.rs
+++ /dev/null
@@ -1,158 +0,0 @@
-//! In-memory mock RufloBackend for testing — no network, zero latency.
-use async_trait::async_trait;
-use std::sync::{Arc, Mutex};
-use super::backend::*;
-
-/// Configurable mock. All writes go to in-memory vecs; searches return stored items.
-pub struct MockRufloBackend {
- pub missions: Arc>>, // (key, value)
- pub patterns: Arc>>, // (pattern, type, confidence)
- pub scan_safe: bool, // set false to simulate a detected threat
- pub traj_ids: Arc>>,
-}
-
-impl Default for MockRufloBackend {
- fn default() -> Self {
- Self {
- missions: Arc::new(Mutex::new(Vec::new())),
- patterns: Arc::new(Mutex::new(Vec::new())),
- scan_safe: true,
- traj_ids: Arc::new(Mutex::new(Vec::new())),
- }
- }
-}
-
-impl MockRufloBackend {
- pub fn new() -> Self { Self::default() }
-
- /// Pre-load a past mission for search to return.
- pub fn seed_mission(&self, key: &str, value: &str) {
- self.missions.lock().unwrap().push((key.to_string(), value.to_string()));
- }
-
- /// Pre-load a pattern for search to return.
- pub fn seed_pattern(&self, pattern: &str, ptype: &str, confidence: f32) {
- self.patterns.lock().unwrap().push((pattern.to_string(), ptype.to_string(), confidence));
- }
-
- /// Configure the scanner to reject the next message.
- pub fn reject_next(self) -> Self { Self { scan_safe: false, ..self } }
-}
-
-#[async_trait]
-impl RufloBackend for MockRufloBackend {
- async fn store_mission(&self, key: &str, value: &str, _ns: &str) -> Result<(), RufloError> {
- self.missions.lock().unwrap().push((key.to_string(), value.to_string()));
- Ok(())
- }
-
- async fn search_missions(&self, query: &str, limit: usize, _ns: &str)
- -> Result, RufloError>
- {
- let missions = self.missions.lock().unwrap();
- Ok(missions.iter().take(limit).map(|(k, v)| MissionMemoryEntry {
- key: k.clone(),
- value: v.clone(),
- score: if v.contains(query) { 0.9 } else { 0.5 },
- }).collect())
- }
-
- async fn store_pattern(&self, pattern: &str, ptype: &str, confidence: f32)
- -> Result<(), RufloError>
- {
- self.patterns.lock().unwrap().push((pattern.to_string(), ptype.to_string(), confidence));
- Ok(())
- }
-
- async fn search_patterns(&self, _query: &str, top_k: usize, min_conf: f32)
- -> Result, RufloError>
- {
- let patterns = self.patterns.lock().unwrap();
- Ok(patterns.iter()
- .filter(|(_, _, c)| *c >= min_conf)
- .take(top_k)
- .map(|(p, t, c)| PatternEntry {
- pattern: p.clone(),
- pattern_type: t.clone(),
- confidence: *c,
- score: *c,
- })
- .collect())
- }
-
- async fn mavlink_is_safe(&self, _msg: &str) -> Result {
- Ok(self.scan_safe)
- }
-
- async fn mavlink_scan(&self, _msg: &str) -> Result {
- Ok(MavlinkScanResult {
- safe: self.scan_safe,
- threats: if self.scan_safe {
- vec![]
- } else {
- vec!["suspicious_coordinates".into()]
- },
- })
- }
-
- async fn trajectory_start(&self, task: &str, _agent: &str)
- -> Result
- {
- let id = format!("mock-traj-{}", task.len()); // deterministic for testing
- self.traj_ids.lock().unwrap().push(id.clone());
- Ok(id)
- }
-
- async fn trajectory_step(&self, _id: &str, _act: &str, _res: &str, _q: f32)
- -> Result<(), RufloError> { Ok(()) }
-
- async fn trajectory_end(&self, _id: &str, _ok: bool, _fb: Option<&str>)
- -> Result<(), RufloError> { Ok(()) }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- #[tokio::test]
- async fn test_mock_store_and_search_mission() {
- let mock = MockRufloBackend::new();
- mock.store_mission("m1", r#"{"victims":2}"#, "swarm-missions").await.unwrap();
- let results = mock.search_missions("victims", 5, "swarm-missions").await.unwrap();
- assert_eq!(results.len(), 1);
- assert_eq!(results[0].key, "m1");
- assert!(results[0].score > 0.5, "keyword match should score high");
- }
-
- #[tokio::test]
- async fn test_mock_pattern_lifecycle() {
- let mock = MockRufloBackend::new();
- mock.store_pattern("approach from 3 angles when P > 0.7", "sar-trajectory", 0.9).await.unwrap();
- let results = mock.search_patterns("SAR convergence", 5, 0.5).await.unwrap();
- assert_eq!(results.len(), 1);
- assert_eq!(results[0].confidence, 0.9);
- }
-
- #[tokio::test]
- async fn test_mock_mavlink_defence_safe() {
- let mock = MockRufloBackend::new();
- assert!(mock.mavlink_is_safe(r#"{"drone_id":1,"confidence":0.8}"#).await.unwrap());
- }
-
- #[tokio::test]
- async fn test_mock_mavlink_defence_rejected() {
- let mock = MockRufloBackend { scan_safe: false, ..Default::default() };
- let scan = mock.mavlink_scan("SUSPICIOUS MESSAGE").await.unwrap();
- assert!(!scan.safe);
- assert!(!scan.threats.is_empty());
- }
-
- #[tokio::test]
- async fn test_mock_trajectory_lifecycle() {
- let mock = MockRufloBackend::new();
- let tid = mock.trajectory_start("SAR 400x400", "swarm-specialist").await.unwrap();
- mock.trajectory_step(&tid, "scan (5,3)", "prob=0.6", 0.7).await.unwrap();
- mock.trajectory_end(&tid, true, Some("victim found")).await.unwrap();
- assert!(!mock.traj_ids.lock().unwrap().is_empty());
- }
-}
diff --git a/v2/crates/ruview-swarm/src/ruflo/mod.rs b/v2/crates/ruview-swarm/src/ruflo/mod.rs
deleted file mode 100644
index c1e7dc41..00000000
--- a/v2/crates/ruview-swarm/src/ruflo/mod.rs
+++ /dev/null
@@ -1,22 +0,0 @@
-//! Ruflo AI-agent capabilities integration.
-//!
-//! Integrates the claude-flow daemon's AgentDB, AIDefence, and SONA intelligence
-//! hooks into the ruview-swarm orchestrator via a trait-based backend.
-//!
-//! Feature gate: `ruflo`. The `RufloBackend` trait and `MockRufloBackend` are always
-//! compiled so tests can use them without enabling the `ruflo` feature. Only
-//! `HttpRufloBackend` (which requires `reqwest` + `serde_json`) is gated.
-
-pub mod backend;
-pub mod mock_backend;
-pub mod mission_summary;
-
-#[cfg(feature = "ruflo")]
-pub mod http_backend;
-
-pub use backend::{RufloBackend, RufloError, MissionMemoryEntry, PatternEntry, MavlinkScanResult};
-pub use mock_backend::MockRufloBackend;
-pub use mission_summary::MissionSummary;
-
-#[cfg(feature = "ruflo")]
-pub use http_backend::HttpRufloBackend;
diff --git a/v2/crates/ruview-swarm/src/security/antijamming.rs b/v2/crates/ruview-swarm/src/security/antijamming.rs
deleted file mode 100644
index e79060b4..00000000
--- a/v2/crates/ruview-swarm/src/security/antijamming.rs
+++ /dev/null
@@ -1,208 +0,0 @@
-//! FHSS (Frequency Hopping Spread Spectrum) anti-jamming interface.
-//!
-//! Provides frequency hop sequence generation and cognitive radio-inspired
-//! adaptive frequency/power selection for drone swarm communication links.
-
-use serde::{Deserialize, Serialize};
-
-/// FHSS configuration for a swarm communication link.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct FhssConfig {
- /// Hop rate in hops-per-second (typical: 100–200).
- pub hop_rate_hz: f64,
- /// Available frequency channels in MHz.
- pub channels_mhz: Vec,
- /// Minimum RSSI (dBm) before triggering channel switch.
- pub rssi_threshold_dbm: f32,
- /// Number of consecutive poor-RSSI samples before switching.
- pub jamming_detect_window: usize,
-}
-
-impl Default for FhssConfig {
- fn default() -> Self {
- // 900 MHz ISM band: 902–928 MHz, 50 channels at 512 kHz spacing
- let channels: Vec = (0..50).map(|i| 902.0 + i as f64 * 0.512).collect();
- Self {
- hop_rate_hz: 200.0,
- channels_mhz: channels,
- rssi_threshold_dbm: -85.0,
- jamming_detect_window: 5,
- }
- }
-}
-
-/// State of the FHSS radio at one node.
-pub struct FhssRadio {
- pub config: FhssConfig,
- /// Current hop sequence position.
- hop_index: usize,
- /// Rolling RSSI history (most recent last).
- rssi_history: Vec,
- /// Elapsed time since last hop (ms).
- elapsed_ms: f64,
- /// Node ID seed for unique hop sequence (XOR with hop_index for non-collision).
- node_seed: u32,
- /// Number of jammer-evasion channel jumps taken.
- pub evasion_count: u64,
-}
-
-impl FhssRadio {
- pub fn new(node_seed: u32, config: FhssConfig) -> Self {
- Self {
- config,
- hop_index: 0,
- rssi_history: Vec::new(),
- elapsed_ms: 0.0,
- node_seed,
- evasion_count: 0,
- }
- }
-
- /// Returns the current active channel frequency in MHz.
- ///
- /// `FhssConfig` is `Deserialize`, so `channels_mhz` can arrive empty from a
- /// malformed or hostile config. An empty channel list would make `% n`
- /// (n = 0) panic with a divide-by-zero. Guard it and return a benign `0.0`
- /// sentinel instead of crashing the radio task (DoS-resistance).
- pub fn current_channel_mhz(&self) -> f64 {
- let n = self.config.channels_mhz.len();
- if n == 0 {
- return 0.0;
- }
- // XOR node seed into hop index so each node uses a different offset
- let idx = (self.hop_index ^ (self.node_seed as usize)) % n;
- self.config.channels_mhz[idx]
- }
-
- /// Advance the hop sequence by one step (call at hop_rate_hz).
- pub fn next_hop(&mut self) {
- let n = self.config.channels_mhz.len();
- if n == 0 {
- return; // no channels configured — nothing to hop (avoid `% 0` panic)
- }
- self.hop_index = (self.hop_index + 1) % n;
- }
-
- /// Update with latest RSSI measurement. Drives jamming detection.
- pub fn observe_rssi(&mut self, rssi_dbm: f32) {
- self.rssi_history.push(rssi_dbm);
- if self.rssi_history.len() > self.config.jamming_detect_window {
- self.rssi_history.remove(0);
- }
- }
-
- /// Returns true if jamming is detected (all recent RSSI samples below threshold).
- pub fn jamming_detected(&self) -> bool {
- if self.rssi_history.len() < self.config.jamming_detect_window {
- return false;
- }
- self.rssi_history.iter().all(|&r| r < self.config.rssi_threshold_dbm)
- }
-
- /// Evasive hop: jump ahead by a pseudo-random offset to escape jammer.
- /// Uses a simple LCG seeded by node_seed + evasion_count for determinism.
- pub fn evasive_hop(&mut self) {
- let lcg_a: u64 = 6364136223846793005;
- let lcg_c: u64 = 1442695040888963407;
- // Use wrapping arithmetic to avoid overflow in debug builds
- let seed = (self.node_seed as u64)
- .wrapping_mul(lcg_a)
- .wrapping_add(self.evasion_count)
- .wrapping_add(lcg_c);
- let len = self.config.channels_mhz.len();
- if len == 0 {
- return; // no channels configured — avoid `% 0` panic
- }
- let n = len as u64;
- let offset = (seed % n / 4 + 3) as usize;
- self.hop_index = (self.hop_index + offset) % len;
- self.evasion_count += 1;
- self.rssi_history.clear();
- }
-
- /// Tick the radio by dt_ms milliseconds. Handles automatic hopping.
- ///
- /// Multiple hops may fire within a single tick if dt_ms > hop_interval_ms.
- pub fn tick(&mut self, dt_ms: f64) {
- self.elapsed_ms += dt_ms;
- let hop_interval_ms = 1000.0 / self.config.hop_rate_hz;
- while self.elapsed_ms >= hop_interval_ms {
- self.elapsed_ms -= hop_interval_ms;
- self.next_hop();
- }
- if self.jamming_detected() {
- self.evasive_hop();
- }
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- #[test]
- fn test_different_nodes_different_channels() {
- let cfg = FhssConfig::default();
- let r0 = FhssRadio::new(0, cfg.clone());
- let r1 = FhssRadio::new(7, cfg);
- // Nodes with different seeds should use different channels at hop 0
- assert_ne!(r0.current_channel_mhz(), r1.current_channel_mhz(),
- "different nodes should use different initial channels");
- }
-
- #[test]
- fn test_jamming_detection() {
- let cfg = FhssConfig { jamming_detect_window: 3, rssi_threshold_dbm: -85.0, ..Default::default() };
- let mut radio = FhssRadio::new(0, cfg);
- // Feed 3 below-threshold RSSI values
- radio.observe_rssi(-90.0);
- radio.observe_rssi(-92.0);
- assert!(!radio.jamming_detected(), "need full window");
- radio.observe_rssi(-91.0);
- assert!(radio.jamming_detected());
- }
-
- #[test]
- fn test_evasive_hop_changes_channel() {
- let cfg = FhssConfig::default();
- let mut radio = FhssRadio::new(42, cfg);
- let before = radio.current_channel_mhz();
- radio.evasive_hop();
- let after = radio.current_channel_mhz();
- assert_ne!(before, after, "evasive hop should change channel");
- }
-
- #[test]
- fn test_tick_advances_hop() {
- let cfg = FhssConfig { hop_rate_hz: 1000.0, ..Default::default() }; // 1 hop/ms
- let mut radio = FhssRadio::new(0, cfg);
- let initial_idx = radio.hop_index;
- radio.tick(2.0); // 2 ms = 2 hops
- assert_eq!(radio.hop_index, (initial_idx + 2) % 50);
- }
-
- /// Security/DoS: an empty `channels_mhz` (deserialized from a malformed or
- /// hostile config) must not panic with a `% 0` divide-by-zero. Fails on old
- /// code, where `next_hop`/`current_channel_mhz`/`evasive_hop`/`tick` all do
- /// modulo / index by `channels_mhz.len()`.
- #[test]
- fn test_empty_channels_does_not_panic() {
- let cfg = FhssConfig { channels_mhz: vec![], jamming_detect_window: 1, ..Default::default() };
- let mut radio = FhssRadio::new(7, cfg);
- // None of these may panic.
- let _ = radio.current_channel_mhz();
- radio.next_hop();
- radio.observe_rssi(-99.0); // window=1 → jamming_detected() true → evasive_hop()
- radio.tick(100.0);
- radio.evasive_hop();
- assert_eq!(radio.current_channel_mhz(), 0.0, "empty channel list returns sentinel");
- }
-
- #[test]
- fn test_channel_in_valid_range() {
- let cfg = FhssConfig::default();
- let radio = FhssRadio::new(99, cfg.clone());
- let ch = radio.current_channel_mhz();
- assert!(ch >= 902.0 && ch <= 928.0, "channel {} out of ISM band", ch);
- }
-}
diff --git a/v2/crates/ruview-swarm/src/security/geofence.rs b/v2/crates/ruview-swarm/src/security/geofence.rs
deleted file mode 100644
index 47599ea0..00000000
--- a/v2/crates/ruview-swarm/src/security/geofence.rs
+++ /dev/null
@@ -1,184 +0,0 @@
-//! Geofence: polygon boundary with hard/soft margins.
-
-use crate::types::Position3D;
-use serde::{Deserialize, Serialize};
-
-/// Polygon geofence with altitude bounds.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct Geofence {
- /// Polygon vertices (x, y) in local NED metres.
- pub boundary: Vec<(f64, f64)>,
- pub min_altitude_m: f64,
- pub max_altitude_m: f64,
- /// Hard margin: triggers RTH immediately.
- pub hard_margin_m: f64,
- /// Soft margin: triggers warning + speed reduction.
- pub soft_margin_m: f64,
-}
-
-/// Result of a geofence check.
-#[derive(Debug, Clone, PartialEq)]
-pub enum GeofenceResult {
- Safe,
- SoftWarning { distance_to_boundary_m: f64 },
- HardBreach,
-}
-
-impl Geofence {
- /// Check a position against this geofence.
- pub fn check(&self, pos: &Position3D) -> GeofenceResult {
- // Fail CLOSED on a non-finite position. A NaN/Inf component (from a
- // corrupt GPS/EKF estimate or a forged position) makes every subsequent
- // comparison false: `NaN < min || NaN > max` is `false`, so the altitude
- // breach is skipped, and a NaN altitude with otherwise-valid x/y would
- // return `Safe` — a silent geofence bypass on a flight-safety boundary.
- // Treat any non-finite coordinate as a hard breach.
- if !pos.x.is_finite() || !pos.y.is_finite() || !pos.z.is_finite() {
- return GeofenceResult::HardBreach;
- }
-
- let altitude_m = -pos.z; // NED: negative z = altitude above ground
-
- // Altitude check
- if altitude_m < self.min_altitude_m || altitude_m > self.max_altitude_m {
- return GeofenceResult::HardBreach;
- }
-
- let inside = self.point_in_polygon(pos.x, pos.y);
- let dist = self.distance_to_boundary(pos.x, pos.y);
-
- if !inside {
- return GeofenceResult::HardBreach;
- }
-
- if dist <= self.hard_margin_m {
- GeofenceResult::HardBreach
- } else if dist <= self.soft_margin_m {
- GeofenceResult::SoftWarning { distance_to_boundary_m: dist }
- } else {
- GeofenceResult::Safe
- }
- }
-
- /// Ray-casting algorithm: even number of crossings = outside.
- fn point_in_polygon(&self, x: f64, y: f64) -> bool {
- let n = self.boundary.len();
- if n < 3 {
- return false;
- }
- let mut inside = false;
- let mut j = n - 1;
- for i in 0..n {
- let (xi, yi) = self.boundary[i];
- let (xj, yj) = self.boundary[j];
- if ((yi > y) != (yj > y)) && (x < (xj - xi) * (y - yi) / (yj - yi) + xi) {
- inside = !inside;
- }
- j = i;
- }
- inside
- }
-
- /// Minimum distance from (x, y) to any boundary edge.
- fn distance_to_boundary(&self, x: f64, y: f64) -> f64 {
- let n = self.boundary.len();
- if n == 0 {
- return f64::INFINITY;
- }
- let mut min_dist = f64::INFINITY;
- let mut j = n - 1;
- for i in 0..n {
- let (ax, ay) = self.boundary[j];
- let (bx, by) = self.boundary[i];
- let dist = point_to_segment_dist(x, y, ax, ay, bx, by);
- if dist < min_dist {
- min_dist = dist;
- }
- j = i;
- }
- min_dist
- }
-}
-
-fn point_to_segment_dist(px: f64, py: f64, ax: f64, ay: f64, bx: f64, by: f64) -> f64 {
- let dx = bx - ax;
- let dy = by - ay;
- let len_sq = dx * dx + dy * dy;
- if len_sq < 1e-12 {
- return ((px - ax).powi(2) + (py - ay).powi(2)).sqrt();
- }
- let t = ((px - ax) * dx + (py - ay) * dy) / len_sq;
- let t = t.clamp(0.0, 1.0);
- let cx = ax + t * dx;
- let cy = ay + t * dy;
- ((px - cx).powi(2) + (py - cy).powi(2)).sqrt()
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- fn square_fence() -> Geofence {
- Geofence {
- boundary: vec![(0.0, 0.0), (100.0, 0.0), (100.0, 100.0), (0.0, 100.0)],
- min_altitude_m: 0.0,
- max_altitude_m: 120.0,
- hard_margin_m: 10.0,
- soft_margin_m: 25.0,
- }
- }
-
- #[test]
- fn test_centre_is_safe() {
- let f = square_fence();
- let pos = Position3D { x: 50.0, y: 50.0, z: -30.0 };
- assert_eq!(f.check(&pos), GeofenceResult::Safe);
- }
-
- #[test]
- fn test_outside_is_hard_breach() {
- let f = square_fence();
- let pos = Position3D { x: 150.0, y: 50.0, z: -30.0 };
- assert_eq!(f.check(&pos), GeofenceResult::HardBreach);
- }
-
- #[test]
- fn test_near_edge_is_soft_warning() {
- let f = square_fence();
- // 15m from boundary → beyond hard (10m) but within soft (25m)
- let pos = Position3D { x: 15.0, y: 50.0, z: -30.0 };
- assert!(matches!(f.check(&pos), GeofenceResult::SoftWarning { .. }));
- }
-
- #[test]
- fn test_altitude_breach() {
- let f = square_fence();
- let pos = Position3D { x: 50.0, y: 50.0, z: -200.0 }; // 200m altitude
- assert_eq!(f.check(&pos), GeofenceResult::HardBreach);
- }
-
- /// Security: a NaN altitude with an otherwise in-bounds x/y must fail closed
- /// to HardBreach. Fails on old code where `NaN < min || NaN > max` is `false`,
- /// the altitude check is skipped, and the point-in-polygon path returns Safe —
- /// a silent geofence bypass.
- #[test]
- fn test_nan_altitude_fails_closed() {
- let f = square_fence();
- let pos = Position3D { x: 50.0, y: 50.0, z: f64::NAN };
- assert_eq!(f.check(&pos), GeofenceResult::HardBreach);
- }
-
- /// Security: NaN/Inf horizontal coordinates must also fail closed.
- #[test]
- fn test_nonfinite_horizontal_fails_closed() {
- let f = square_fence();
- assert_eq!(
- f.check(&Position3D { x: f64::NAN, y: 50.0, z: -30.0 }),
- GeofenceResult::HardBreach
- );
- assert_eq!(
- f.check(&Position3D { x: 50.0, y: f64::INFINITY, z: -30.0 }),
- GeofenceResult::HardBreach
- );
- }
-}
diff --git a/v2/crates/ruview-swarm/src/security/mavlink_signing.rs b/v2/crates/ruview-swarm/src/security/mavlink_signing.rs
deleted file mode 100644
index ef442cc2..00000000
--- a/v2/crates/ruview-swarm/src/security/mavlink_signing.rs
+++ /dev/null
@@ -1,100 +0,0 @@
-//! MAVLink v2 HMAC-SHA256 link-level signing.
-
-use hmac::{Hmac, Mac};
-use sha2::Sha256;
-use std::sync::atomic::{AtomicU64, Ordering};
-
-type HmacSha256 = Hmac;
-
-/// Signs and verifies MAVLink v2 messages using HMAC-SHA256.
-pub struct MavlinkSigner {
- key: [u8; 32],
- link_id: u8,
- timestamp: AtomicU64,
-}
-
-impl MavlinkSigner {
- pub fn new(key: [u8; 32], link_id: u8) -> Self {
- Self {
- key,
- link_id,
- timestamp: AtomicU64::new(1),
- }
- }
-
- /// Advance and return a monotonic 48-bit timestamp (units: 10 µs since epoch).
- fn next_timestamp(&self) -> u64 {
- self.timestamp.fetch_add(1, Ordering::SeqCst)
- }
-
- /// Compute the 6-byte MAVLink v2 signature.
- /// Signature = first 6 bytes of HMAC-SHA256(key, link_id || timestamp_6bytes || message_bytes)
- pub fn sign(&self, message_bytes: &[u8]) -> [u8; 6] {
- let ts = self.next_timestamp();
- let ts_bytes = ts.to_le_bytes(); // 8 bytes, MAVLink uses 6 but we include all for simplicity
-
- let mut mac = HmacSha256::new_from_slice(&self.key)
- .expect("HMAC accepts any key length");
- mac.update(&[self.link_id]);
- mac.update(&ts_bytes[..6]);
- mac.update(message_bytes);
-
- let result = mac.finalize().into_bytes();
- let mut sig = [0u8; 6];
- sig.copy_from_slice(&result[..6]);
- sig
- }
-
- /// Verify that `signature` is valid for `message_bytes`.
- /// This implementation re-computes against all recent timestamps within a
- /// small window (for demo/test). Production code should maintain a timestamp
- /// window per link_id.
- pub fn verify(&self, message_bytes: &[u8], signature: &[u8; 6]) -> bool {
- let current_ts = self.timestamp.load(Ordering::SeqCst);
- // Check ±32 timestamps to handle reordering in tests
- let start = current_ts.saturating_sub(32);
- for ts in start..=current_ts + 1 {
- let ts_bytes = ts.to_le_bytes();
- let mut mac = HmacSha256::new_from_slice(&self.key)
- .expect("HMAC accepts any key length");
- mac.update(&[self.link_id]);
- mac.update(&ts_bytes[..6]);
- mac.update(message_bytes);
- let result = mac.finalize().into_bytes();
- if &result[..6] == signature.as_ref() {
- return true;
- }
- }
- false
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- #[test]
- fn test_sign_produces_6_bytes() {
- let signer = MavlinkSigner::new([0xABu8; 32], 0);
- let sig = signer.sign(b"heartbeat");
- assert_eq!(sig.len(), 6);
- }
-
- #[test]
- fn test_verify_correct_signature() {
- let signer = MavlinkSigner::new([0x42u8; 32], 1);
- let msg = b"test_message";
- let sig = signer.sign(msg);
- assert!(signer.verify(msg, &sig));
- }
-
- #[test]
- fn test_verify_wrong_key_fails() {
- let signer1 = MavlinkSigner::new([0x01u8; 32], 1);
- let signer2 = MavlinkSigner::new([0x02u8; 32], 1);
- let msg = b"test_message";
- let sig = signer1.sign(msg);
- // signer2 has a different key — can't verify signer1's sig
- assert!(!signer2.verify(msg, &sig));
- }
-}
diff --git a/v2/crates/ruview-swarm/src/security/mod.rs b/v2/crates/ruview-swarm/src/security/mod.rs
deleted file mode 100644
index 8da45ff0..00000000
--- a/v2/crates/ruview-swarm/src/security/mod.rs
+++ /dev/null
@@ -1,13 +0,0 @@
-//! Security: MAVLink signing, UWB anti-spoofing, geofencing, Remote ID, FHSS anti-jamming.
-
-pub mod mavlink_signing;
-pub mod uwb_antispoofing;
-pub mod geofence;
-pub mod remote_id;
-pub mod antijamming;
-
-pub use mavlink_signing::MavlinkSigner;
-pub use uwb_antispoofing::UwbAntiSpoofing;
-pub use geofence::{Geofence, GeofenceResult};
-pub use remote_id::RemoteIdBroadcast;
-pub use antijamming::{FhssConfig, FhssRadio};
diff --git a/v2/crates/ruview-swarm/src/security/remote_id.rs b/v2/crates/ruview-swarm/src/security/remote_id.rs
deleted file mode 100644
index a4289222..00000000
--- a/v2/crates/ruview-swarm/src/security/remote_id.rs
+++ /dev/null
@@ -1,143 +0,0 @@
-//! ASTM F3411 Remote ID — **Basic ID message only** (ADR-159 §A3).
-//!
-//! Only the Basic ID message (`encode_basic_id`) is implemented. The
-//! Location/Vector message is **not** encoded yet because the drone position is
-//! tracked in a local NED frame (north/east metres relative to a takeoff datum),
-//! and a compliant Location/Vector message requires WGS84 latitude/longitude.
-//! Broadcasting NED metres in lat/lon fields would emit physically-impossible
-//! coordinates (e.g. "latitude = 12.4 metres"), so we deliberately keep the
-//! drone position in honest `drone_north_m` / `drone_east_m` fields until a real
-//! local-tangent-plane NED→WGS84 transform (with an operator datum) lands. See
-//! the `ACCEPTED-FUTURE` note in ADR-159 §A3.
-
-use crate::types::DroneState;
-use serde::{Deserialize, Serialize};
-
-/// Remote ID broadcast state for one drone.
-///
-/// Drone position is stored as **NED metres** (`drone_north_m` / `drone_east_m`)
-/// relative to the operator/takeoff datum — *not* WGS84 lat/lon — because no
-/// datum-anchored geodetic transform is wired yet. The operator position is true
-/// WGS84 (it comes from the operator's GNSS, not the local frame).
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct RemoteIdBroadcast {
- pub uas_id: [u8; 20], // 20-byte UAS ID (ANSI/CTA-2063-A)
- /// Operator latitude (WGS84 degrees) — real geodetic position.
- pub operator_lat: f64,
- /// Operator longitude (WGS84 degrees) — real geodetic position.
- pub operator_lon: f64,
- /// Drone north offset in **metres** from the operator/takeoff datum (NED x).
- /// NOT a latitude. See module docs — Location/Vector encoding is deferred
- /// until a real NED→WGS84 transform exists.
- pub drone_north_m: f64,
- /// Drone east offset in **metres** from the operator/takeoff datum (NED y).
- /// NOT a longitude.
- pub drone_east_m: f64,
- pub altitude_msl_m: f32,
- pub speed_ms: f32,
- pub heading_deg: f32,
- pub timestamp_ms: u64,
- pub emergency_status: bool,
-}
-
-impl RemoteIdBroadcast {
- pub fn new(uas_id: [u8; 20]) -> Self {
- Self {
- uas_id,
- operator_lat: 0.0,
- operator_lon: 0.0,
- drone_north_m: 0.0,
- drone_east_m: 0.0,
- altitude_msl_m: 0.0,
- speed_ms: 0.0,
- heading_deg: 0.0,
- timestamp_ms: 0,
- emergency_status: false,
- }
- }
-
- /// Update from a drone state and operator position.
- ///
- /// The drone position is stored as honest NED metres — we do **not** fake a
- /// lat/lon from a local-frame offset. The operator position is true WGS84.
- pub fn update(&mut self, state: &DroneState, operator_pos: (f64, f64)) {
- // NED metres, stored as-is in metre-typed fields (no fabricated geodetic
- // coordinates). A future Location/Vector encoder must transform these
- // through a datum-anchored NED→WGS84 projection before broadcast.
- self.drone_north_m = state.position.x; // NED x = north offset, metres
- self.drone_east_m = state.position.y; // NED y = east offset, metres
- self.altitude_msl_m = state.altitude_agl_m as f32;
- self.speed_ms = state.velocity.magnitude() as f32;
- self.heading_deg = state.heading_rad.to_degrees() as f32;
- self.timestamp_ms = state.timestamp_ms;
- self.operator_lat = operator_pos.0;
- self.operator_lon = operator_pos.1;
- }
-
- /// Encode a 25-byte ASTM F3411 Basic ID message.
- /// Format: [message_type(1)] [id_type(1)] [uas_id(20)] [reserved(3)]
- pub fn encode_basic_id(&self) -> [u8; 25] {
- let mut buf = [0u8; 25];
- buf[0] = 0x00; // Message type: Basic ID
- buf[1] = 0x01; // ID type: Serial Number
- buf[2..22].copy_from_slice(&self.uas_id);
- // bytes 22-24: reserved
- buf
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- #[test]
- fn test_encode_basic_id_length() {
- let rid = RemoteIdBroadcast::new([0x41u8; 20]);
- let buf = rid.encode_basic_id();
- assert_eq!(buf.len(), 25);
- assert_eq!(buf[1], 0x01); // ID type: serial number
- }
-
- #[test]
- fn test_uas_id_in_encoded_buffer() {
- let mut id = [0u8; 20];
- id[0] = 0xFF;
- let rid = RemoteIdBroadcast::new(id);
- let buf = rid.encode_basic_id();
- assert_eq!(buf[2], 0xFF);
- }
-
- /// ADR-159 §A3 — a known NED offset must land in honest **metre** fields,
- /// never in WGS84 lat/lon fields (which would broadcast physically-impossible
- /// coordinates like "latitude = 37.5 m"). Fails on old code, where the same
- /// values were stored into `drone_lat`/`drone_lon`.
- #[test]
- fn test_ned_offset_stored_as_metres_not_latlon() {
- use crate::types::{DroneState, NodeId, Position3D};
-
- let mut state = DroneState::default_at_origin(NodeId(7));
- // 37.5 m north, -12.0 m east of the takeoff datum.
- state.position = Position3D {
- x: 37.5,
- y: -12.0,
- z: 5.0,
- };
- let mut rid = RemoteIdBroadcast::new([0x41u8; 20]);
- // Operator at a real WGS84 fix (San Francisco-ish).
- rid.update(&state, (37.7749, -122.4194));
-
- // Drone offset is honest NED metres.
- assert_eq!(rid.drone_north_m, 37.5);
- assert_eq!(rid.drone_east_m, -12.0);
-
- // Operator position is the real geodetic fix and is plausibly a lat/lon.
- assert!((-90.0..=90.0).contains(&rid.operator_lat));
- assert!((-180.0..=180.0).contains(&rid.operator_lon));
- assert!((rid.operator_lat - 37.7749).abs() < 1e-9);
-
- // The drone NED metres would have been an out-of-range "latitude" only
- // if a value happened to exceed 90 — but the contract is the field name
- // itself: these are metres, not degrees. A future Location/Vector
- // encoder must project them through a real NED→WGS84 transform.
- }
-}
diff --git a/v2/crates/ruview-swarm/src/security/uwb_antispoofing.rs b/v2/crates/ruview-swarm/src/security/uwb_antispoofing.rs
deleted file mode 100644
index d623a3c1..00000000
--- a/v2/crates/ruview-swarm/src/security/uwb_antispoofing.rs
+++ /dev/null
@@ -1,82 +0,0 @@
-//! UWB-based GPS anti-spoofing: cross-validates GPS position against UWB ranging.
-
-use crate::types::{NodeId, Position3D};
-
-/// Cross-validates GPS against UWB ranging to neighbours.
-pub struct UwbAntiSpoofing {
- /// Tolerance for GPS vs UWB distance discrepancy, metres.
- pub tolerance_m: f64,
- /// Minimum number of UWB neighbours required for a valid cross-check.
- pub min_neighbors: usize,
-}
-
-impl UwbAntiSpoofing {
- pub fn new(tolerance_m: f64, min_neighbors: usize) -> Self {
- Self { tolerance_m, min_neighbors }
- }
-
- /// Returns `true` if the GPS position is consistent with UWB ranging data.
- pub fn is_gps_valid(
- &self,
- gps_position: &Position3D,
- uwb_ranges: &[(NodeId, f64)],
- neighbor_gps: &[(NodeId, Position3D)],
- ) -> bool {
- if uwb_ranges.len() < self.min_neighbors {
- // Not enough UWB anchors to validate — allow through with warning
- return true;
- }
-
- let validated_count = uwb_ranges
- .iter()
- .filter_map(|(id, uwb_dist)| {
- neighbor_gps
- .iter()
- .find(|(nid, _)| nid == id)
- .map(|(_, ngps)| {
- let gps_dist = gps_position.distance_to(ngps);
- (gps_dist - uwb_dist).abs() <= self.tolerance_m
- })
- })
- .filter(|&ok| ok)
- .count();
-
- // Require majority of ranges to be consistent
- validated_count * 2 >= uwb_ranges.len()
- }
-}
-
-impl Default for UwbAntiSpoofing {
- fn default() -> Self {
- Self::new(2.0, 2)
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- #[test]
- fn test_consistent_gps_valid() {
- let anti = UwbAntiSpoofing::new(2.0, 2);
- let gps = Position3D { x: 0.0, y: 0.0, z: 0.0 };
- let n1_pos = Position3D { x: 10.0, y: 0.0, z: 0.0 };
- let n2_pos = Position3D { x: 0.0, y: 10.0, z: 0.0 };
- let uwb_ranges = vec![(NodeId(1), 10.0), (NodeId(2), 10.0)];
- let neighbor_gps = vec![(NodeId(1), n1_pos), (NodeId(2), n2_pos)];
- assert!(anti.is_gps_valid(&gps, &uwb_ranges, &neighbor_gps));
- }
-
- #[test]
- fn test_spoofed_gps_invalid() {
- let anti = UwbAntiSpoofing::new(2.0, 2);
- // GPS claims (0,0) but UWB says drone is 50m from both neighbours
- let gps = Position3D { x: 0.0, y: 0.0, z: 0.0 };
- let n1_pos = Position3D { x: 10.0, y: 0.0, z: 0.0 };
- let n2_pos = Position3D { x: 0.0, y: 10.0, z: 0.0 };
- // UWB reports 50m but GPS only shows 10m — spoof detected
- let uwb_ranges = vec![(NodeId(1), 50.0), (NodeId(2), 50.0)];
- let neighbor_gps = vec![(NodeId(1), n1_pos), (NodeId(2), n2_pos)];
- assert!(!anti.is_gps_valid(&gps, &uwb_ranges, &neighbor_gps));
- }
-}
diff --git a/v2/crates/ruview-swarm/src/sensing/mod.rs b/v2/crates/ruview-swarm/src/sensing/mod.rs
deleted file mode 100644
index 07f5f1c6..00000000
--- a/v2/crates/ruview-swarm/src/sensing/mod.rs
+++ /dev/null
@@ -1,7 +0,0 @@
-pub mod payload;
-pub mod multiview;
-pub mod occworld_bridge;
-
-pub use payload::{CsiPayloadPipeline, PayloadConfig};
-pub use multiview::{MultiViewFusion, FusedDetection};
-pub use occworld_bridge::{OccWorldBridge, OccupancyPrior, VoxelCell};
diff --git a/v2/crates/ruview-swarm/src/sensing/multiview.rs b/v2/crates/ruview-swarm/src/sensing/multiview.rs
deleted file mode 100644
index daa7f95b..00000000
--- a/v2/crates/ruview-swarm/src/sensing/multiview.rs
+++ /dev/null
@@ -1,237 +0,0 @@
-use crate::types::{NodeId, Position3D, CsiDetection};
-
-/// A fused detection result from multiple drone viewpoints.
-#[derive(Debug, Clone)]
-pub struct FusedDetection {
- pub confidence: f32,
- pub estimated_position: Position3D,
- pub contributing_drones: Vec,
- /// Localization uncertainty ellipse (std dev in metres).
- pub uncertainty_m: f64,
-}
-
-/// Geometric diversity metric (Cramer-Rao bound proxy).
-/// More diverse viewpoints -> lower bound -> better localization.
-fn geometric_diversity_index(positions: &[Position3D]) -> f64 {
- if positions.len() < 2 {
- return 0.0;
- }
- // Compute average pairwise angular separation
- let n = positions.len();
- let centroid = Position3D {
- x: positions.iter().map(|p| p.x).sum::() / n as f64,
- y: positions.iter().map(|p| p.y).sum::() / n as f64,
- z: positions.iter().map(|p| p.z).sum::() / n as f64,
- };
-
- let mut total_angle = 0.0_f64;
- let mut pairs = 0;
- for i in 0..n {
- for j in (i + 1)..n {
- let a = (positions[i].x - centroid.x, positions[i].y - centroid.y);
- let b = (positions[j].x - centroid.x, positions[j].y - centroid.y);
- let dot = a.0 * b.0 + a.1 * b.1;
- let mag_a = (a.0 * a.0 + a.1 * a.1).sqrt().max(1e-9);
- let mag_b = (b.0 * b.0 + b.1 * b.1).sqrt().max(1e-9);
- let cos_angle = (dot / (mag_a * mag_b)).clamp(-1.0, 1.0);
- total_angle += cos_angle.acos();
- pairs += 1;
- }
- }
-
- if pairs > 0 { total_angle / pairs as f64 } else { 0.0 }
-}
-
-/// Multi-drone CSI fusion via confidence-weighted position averaging with geometric bias.
-pub struct MultiViewFusion {
- /// Minimum number of independent viewpoints required to produce a fused result.
- pub min_viewpoints: usize,
- /// Minimum confidence of individual detections to include in fusion.
- pub min_confidence: f32,
-}
-
-impl Default for MultiViewFusion {
- fn default() -> Self {
- Self { min_viewpoints: 2, min_confidence: 0.5 }
- }
-}
-
-impl MultiViewFusion {
- /// Fuse multiple CSI detections from different drone viewpoints.
- /// Returns None if fewer than min_viewpoints pass the confidence threshold.
- pub fn fuse(
- &self,
- detections: &[CsiDetection],
- drone_positions: &[(NodeId, Position3D)],
- ) -> Option {
- // Filter by confidence and require a FINITE estimated position.
- //
- // A peer detection (received via `receive_peer_detection`) carries f32/f64
- // fields that can deserialize to NaN/Inf. A NaN `victim_position` passes
- // `is_some()` and would propagate through the confidence-weighted average
- // into the fused position — dispatching a NaN "confirmed victim" location
- // to the swarm. A NaN `confidence` is already rejected by `>= min_confidence`
- // (NaN comparisons are false), but we make that explicit and also require
- // the victim position components to be finite. Fail CLOSED: drop poisoned
- // detections rather than fusing them.
- let valid: Vec<(&CsiDetection, &Position3D)> = detections
- .iter()
- .filter(|d| {
- d.confidence.is_finite()
- && d.confidence >= self.min_confidence
- && d.victim_position
- .map(|p| p.x.is_finite() && p.y.is_finite() && p.z.is_finite())
- .unwrap_or(false)
- })
- .filter_map(|d| {
- let drone_pos = drone_positions
- .iter()
- .find(|(id, _)| *id == d.drone_id)
- .map(|(_, p)| p)?;
- Some((d, drone_pos))
- })
- .collect();
-
- if valid.len() < self.min_viewpoints {
- return None;
- }
-
- // Compute geometric diversity index for uncertainty estimate
- let drone_pos_list: Vec = valid.iter().map(|(_, p)| **p).collect();
- let gdi = geometric_diversity_index(&drone_pos_list);
-
- // Weighted average of victim position estimates
- let total_weight: f32 = valid.iter().map(|(d, _)| d.confidence).sum();
- let mut fused_x = 0.0_f64;
- let mut fused_y = 0.0_f64;
- let mut fused_z = 0.0_f64;
- let mut fused_conf = 0.0_f32;
-
- for (det, _) in &valid {
- let w = det.confidence / total_weight;
- let vp = det.victim_position.unwrap();
- fused_x += w as f64 * vp.x;
- fused_y += w as f64 * vp.y;
- fused_z += w as f64 * vp.z;
- fused_conf += w * det.confidence;
- }
-
- // Uncertainty shrinks with geometric diversity and number of viewpoints:
- // baseline 5 m (single drone) -> scales down by sqrt(n) and gdi factor
- let base_uncertainty_m = 5.0;
- let n = valid.len() as f64;
- let gdi_factor = (1.0 + gdi / std::f64::consts::PI).clamp(1.0, 2.0);
- let uncertainty_m = base_uncertainty_m / (n.sqrt() * gdi_factor);
-
- Some(FusedDetection {
- confidence: fused_conf,
- estimated_position: Position3D { x: fused_x, y: fused_y, z: fused_z },
- contributing_drones: valid.iter().map(|(d, _)| d.drone_id).collect(),
- uncertainty_m,
- })
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- #[test]
- fn test_fusion_single_view_insufficient() {
- let fusion = MultiViewFusion { min_viewpoints: 2, min_confidence: 0.5 };
- let det = CsiDetection {
- drone_id: NodeId(0),
- confidence: 0.9,
- victim_position: Some(Position3D { x: 10.0, y: 5.0, z: 0.0 }),
- timestamp_ms: 0,
- };
- let result = fusion.fuse(&[det], &[(NodeId(0), Position3D::zero())]);
- assert!(result.is_none(), "single viewpoint should not produce fusion");
- }
-
- #[test]
- fn test_fusion_three_views() {
- let fusion = MultiViewFusion::default();
- let victim = Position3D { x: 50.0, y: 50.0, z: 0.0 };
- let detections = vec![
- CsiDetection {
- drone_id: NodeId(0),
- confidence: 0.85,
- victim_position: Some(Position3D { x: 51.0, y: 49.0, z: 0.0 }),
- timestamp_ms: 0,
- },
- CsiDetection {
- drone_id: NodeId(1),
- confidence: 0.78,
- victim_position: Some(Position3D { x: 49.0, y: 51.0, z: 0.0 }),
- timestamp_ms: 0,
- },
- CsiDetection {
- drone_id: NodeId(2),
- confidence: 0.92,
- victim_position: Some(Position3D { x: 50.0, y: 50.0, z: 0.0 }),
- timestamp_ms: 0,
- },
- ];
- let positions = vec![
- (NodeId(0), Position3D { x: 0.0, y: 0.0, z: -30.0 }),
- (NodeId(1), Position3D { x: 100.0, y: 0.0, z: -30.0 }),
- (NodeId(2), Position3D { x: 50.0, y: 86.6, z: -30.0 }), // equilateral triangle
- ];
-
- let result = fusion.fuse(&detections, &positions).unwrap();
- let err = result.estimated_position.distance_to(&victim);
- assert!(
- err < 3.0,
- "fusion error {} m should be < 3 m for 3 equilateral viewpoints",
- err
- );
- assert!(
- result.uncertainty_m < 5.0,
- "uncertainty {} should be < 5 m single-drone baseline",
- result.uncertainty_m
- );
- }
-
- /// Security: a detection with a NaN victim position (poisoned peer report)
- /// must be dropped, not fused. Fails on old code where the NaN propagates
- /// into the confidence-weighted average and the fused position is NaN.
- #[test]
- fn test_nan_victim_position_dropped_from_fusion() {
- let fusion = MultiViewFusion { min_viewpoints: 2, min_confidence: 0.5 };
- let detections = vec![
- CsiDetection {
- drone_id: NodeId(0),
- confidence: 0.9,
- victim_position: Some(Position3D { x: 50.0, y: 50.0, z: 0.0 }),
- timestamp_ms: 0,
- },
- CsiDetection {
- drone_id: NodeId(1),
- confidence: 0.9,
- victim_position: Some(Position3D { x: f64::NAN, y: 50.0, z: 0.0 }),
- timestamp_ms: 0,
- },
- CsiDetection {
- drone_id: NodeId(2),
- confidence: 0.9,
- victim_position: Some(Position3D { x: 50.0, y: 50.0, z: 0.0 }),
- timestamp_ms: 0,
- },
- ];
- let positions = vec![
- (NodeId(0), Position3D { x: 0.0, y: 0.0, z: -30.0 }),
- (NodeId(1), Position3D { x: 100.0, y: 0.0, z: -30.0 }),
- (NodeId(2), Position3D { x: 50.0, y: 86.6, z: -30.0 }),
- ];
- // Two finite viewpoints remain → still fuses, but the result must be finite.
- let result = fusion.fuse(&detections, &positions).unwrap();
- assert!(
- result.estimated_position.x.is_finite()
- && result.estimated_position.y.is_finite()
- && result.estimated_position.z.is_finite(),
- "fused position must be finite when a NaN detection is present"
- );
- assert!(!result.contributing_drones.contains(&NodeId(1)), "NaN detection must be excluded");
- }
-}
diff --git a/v2/crates/ruview-swarm/src/sensing/occworld_bridge.rs b/v2/crates/ruview-swarm/src/sensing/occworld_bridge.rs
deleted file mode 100644
index 9af48486..00000000
--- a/v2/crates/ruview-swarm/src/sensing/occworld_bridge.rs
+++ /dev/null
@@ -1,146 +0,0 @@
-//! Bridge between OccWorld Python subprocess (ADR-147) and the Rust swarm planner.
-use crate::types::Position3D;
-use std::path::PathBuf;
-
-/// A 3-D occupancy grid cell.
-#[derive(Debug, Clone, Copy, serde::Serialize, serde::Deserialize)]
-pub struct VoxelCell {
- pub x: f32,
- pub y: f32,
- pub z: f32,
- pub occupancy: f32, // 0.0 = free, 1.0 = occupied
- pub semantic_class: u8, // 0=free, 1=wall, 2=floor, 3=person, 4=furniture
-}
-
-/// Occupancy prior produced by OccWorld inference (ADR-147).
-pub struct OccupancyPrior {
- pub voxels: Vec,
- pub resolution_m: f32,
- pub origin: (f32, f32, f32),
- pub timestamp_ms: u64,
-}
-
-impl OccupancyPrior {
- /// Extract free-space cells (occupancy < threshold) at a given altitude band.
- /// Used by RRT* as valid sampling space.
- pub fn free_cells_at_altitude(&self, target_z: f32, band_m: f32, threshold: f32) -> Vec<(f32, f32)> {
- self.voxels
- .iter()
- .filter(|v| v.occupancy < threshold && (v.z - target_z).abs() < band_m)
- .map(|v| (v.x, v.y))
- .collect()
- }
-
- /// Extract occupied cells (walls, debris). Used as obstacles for path planning.
- pub fn obstacle_cells(&self, threshold: f32) -> Vec {
- self.voxels
- .iter()
- .filter(|v| v.occupancy >= threshold)
- .map(|v| Position3D { x: v.x as f64, y: v.y as f64, z: v.z as f64 })
- .collect()
- }
-
- /// Cells where a person voxel is predicted (semantic_class == 3).
- /// Initializes the Bayesian probability grid with a prior.
- pub fn person_cells(&self) -> Vec {
- self.voxels
- .iter()
- .filter(|v| v.semantic_class == 3)
- .map(|v| Position3D { x: v.x as f64, y: v.y as f64, z: v.z as f64 })
- .collect()
- }
-
- /// Generate a synthetic 20 × 20 × 3 m room prior for demo mode.
- ///
- /// The room has wall voxels on the perimeter and free-space voxels in the
- /// interior, at the requested voxel resolution.
- pub fn synthetic_room(resolution_m: f32) -> Self {
- let mut voxels = Vec::new();
- let room = 20.0f32;
- let steps = (room / resolution_m) as i32;
- for xi in 0..steps {
- for yi in 0..steps {
- for zi in 0..15i32 { // 3 m height (15 × 0.2 m slices)
- let x = xi as f32 * resolution_m - room / 2.0;
- let y = yi as f32 * resolution_m - room / 2.0;
- let z = zi as f32 * resolution_m;
- let is_wall = xi == 0 || xi == steps - 1 || yi == 0 || yi == steps - 1;
- voxels.push(VoxelCell {
- x,
- y,
- z,
- occupancy: if is_wall { 1.0 } else { 0.0 },
- semantic_class: if is_wall { 1 } else if zi == 0 { 2 } else { 0 },
- });
- }
- }
- }
- OccupancyPrior { voxels, resolution_m, origin: (0.0, 0.0, 0.0), timestamp_ms: 0 }
- }
-}
-
-/// Bridge to the OccWorld Python subprocess (ADR-147).
-/// Provides 3-D occupancy priors for the RRT* path planner and the Bayesian
-/// victim-probability grid. In demo mode, returns a synthetic room prior.
-pub struct OccWorldBridge {
- /// Path to the OccWorld Python script.
- pub script_path: PathBuf,
- /// Cache of the last inference result.
- last_prior: Option,
-}
-
-impl Default for OccWorldBridge {
- fn default() -> Self {
- Self { script_path: PathBuf::from("occworld_infer.py"), last_prior: None }
- }
-}
-
-impl OccWorldBridge {
- pub fn new(script_path: PathBuf) -> Self {
- Self { script_path, last_prior: None }
- }
-
- /// Run a demo-mode inference using the synthetic room prior.
- /// No subprocess is spawned; the result is immediately available.
- pub async fn infer_demo(&mut self) -> &OccupancyPrior {
- self.last_prior = Some(OccupancyPrior::synthetic_room(0.2));
- self.last_prior.as_ref().unwrap()
- }
-
- /// Run OccWorld inference and return the occupancy prior.
- /// In demo mode: returns a synthetic prior with configurable obstacles.
- pub async fn infer(&mut self, demo_mode: bool) -> crate::SwarmResult<&OccupancyPrior> {
- if demo_mode {
- self.last_prior = Some(OccupancyPrior::synthetic_room(0.2));
- } else {
- // Production: spawn Python subprocess, read JSON output.
- // let output = tokio::process::Command::new("python3")
- // .arg(&self.script_path)
- // .arg("--mode=infer")
- // .output().await?;
- // parse JSON output into OccupancyPrior.
- // Fallback to synthetic for now until subprocess integration is complete.
- self.last_prior = Some(OccupancyPrior::synthetic_room(0.2));
- }
- Ok(self.last_prior.as_ref().unwrap())
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- #[test]
- fn test_synthetic_room_has_walls() {
- let prior = OccupancyPrior::synthetic_room(0.5);
- let obstacles = prior.obstacle_cells(0.5);
- assert!(!obstacles.is_empty(), "room should have wall voxels");
- }
-
- #[test]
- fn test_free_cells_at_altitude() {
- let prior = OccupancyPrior::synthetic_room(0.5);
- let free = prior.free_cells_at_altitude(1.5, 0.5, 0.5);
- assert!(!free.is_empty(), "room interior should have free cells");
- }
-}
diff --git a/v2/crates/ruview-swarm/src/sensing/payload.rs b/v2/crates/ruview-swarm/src/sensing/payload.rs
deleted file mode 100644
index 8806d8bb..00000000
--- a/v2/crates/ruview-swarm/src/sensing/payload.rs
+++ /dev/null
@@ -1,137 +0,0 @@
-use crate::types::{NodeId, Position3D, CsiDetection};
-
-/// Configuration for the onboard CSI sensing payload.
-#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
-pub struct PayloadConfig {
- pub scan_freq_hz: f64, // 10.0 nominal, 20.0 during Phase 3 convergence
- pub detection_range_m: f64, // ~28.0 m (Wi2SAR validated)
- pub confidence_threshold: f32, // minimum confidence to report detection (0.6)
- pub esp32_baud_rate: u32, // 921600
-}
-
-impl Default for PayloadConfig {
- fn default() -> Self {
- Self {
- scan_freq_hz: 10.0,
- detection_range_m: 28.0,
- confidence_threshold: 0.6,
- esp32_baud_rate: 921600,
- }
- }
-}
-
-/// Represents the CSI sensing payload pipeline running on the drone's companion compute.
-/// In production: reads from ESP32-S3 via serial TDM; runs CIR (ADR-134) -> RF encoder (ADR-146).
-/// In demo/sim mode: generates synthetic detections.
-pub struct CsiPayloadPipeline {
- pub node_id: NodeId,
- pub config: PayloadConfig,
- mode: PipelineMode,
-}
-
-// Fields in Live and Replay variants are unused until the serial/file backends are wired up.
-#[allow(dead_code)]
-enum PipelineMode {
- /// Live pipeline: reads from serial port.
- Live { port_path: String },
- /// Demo/simulation mode: synthetic CSI generation.
- Synthetic {
- victim_positions: Vec,
- noise_std: f64,
- rng_seed: u64,
- },
- /// Replay mode: reads from recorded CSI file.
- Replay { file_path: String, loop_replay: bool },
-}
-
-impl CsiPayloadPipeline {
- pub fn new_live(node_id: NodeId, config: PayloadConfig, port: &str) -> Self {
- Self { node_id, config, mode: PipelineMode::Live { port_path: port.to_string() } }
- }
-
- pub fn new_synthetic(
- node_id: NodeId,
- config: PayloadConfig,
- victims: Vec,
- noise_std: f64,
- seed: u64,
- ) -> Self {
- Self {
- node_id,
- config,
- mode: PipelineMode::Synthetic {
- victim_positions: victims,
- noise_std,
- rng_seed: seed,
- },
- }
- }
-
- pub fn new_replay(node_id: NodeId, config: PayloadConfig, path: &str, loop_replay: bool) -> Self {
- Self {
- node_id,
- config,
- mode: PipelineMode::Replay {
- file_path: path.to_string(),
- loop_replay,
- },
- }
- }
-
- /// Scan the current position and return a detection report (if any).
- pub async fn scan(&self, drone_pos: &Position3D) -> Option {
- match &self.mode {
- PipelineMode::Synthetic { victim_positions, noise_std, rng_seed } => {
- self.synthetic_scan(drone_pos, victim_positions, *noise_std, *rng_seed)
- }
- PipelineMode::Live { .. } => {
- // Production: would read from serial port, run CIR+RF encoder pipeline
- // For now: return None (requires hardware)
- None
- }
- PipelineMode::Replay { .. } => {
- // Production: would read from recorded file
- None
- }
- }
- }
-
- fn synthetic_scan(
- &self,
- drone_pos: &Position3D,
- victims: &[Position3D],
- noise_std: f64,
- _seed: u64,
- ) -> Option {
- use rand::Rng;
- let mut rng = rand::thread_rng();
-
- for victim in victims {
- let dist = drone_pos.distance_to(victim);
- if dist < self.config.detection_range_m {
- let base_confidence = (-dist / self.config.detection_range_m).exp();
- let noise: f64 = rng.gen_range(-noise_std..noise_std);
- let confidence = (base_confidence + noise).clamp(0.0, 1.0) as f32;
-
- if confidence >= self.config.confidence_threshold {
- let pos_noise_x: f64 = rng.gen_range(-noise_std * 5.0..noise_std * 5.0);
- let pos_noise_y: f64 = rng.gen_range(-noise_std * 5.0..noise_std * 5.0);
- return Some(CsiDetection {
- drone_id: self.node_id,
- confidence,
- victim_position: Some(Position3D {
- x: victim.x + pos_noise_x,
- y: victim.y + pos_noise_y,
- z: victim.z,
- }),
- timestamp_ms: std::time::SystemTime::now()
- .duration_since(std::time::UNIX_EPOCH)
- .map(|d| d.as_millis() as u64)
- .unwrap_or(0),
- });
- }
- }
- }
- None
- }
-}
diff --git a/v2/crates/ruview-swarm/src/topology/gossip.rs b/v2/crates/ruview-swarm/src/topology/gossip.rs
deleted file mode 100644
index 350f2154..00000000
--- a/v2/crates/ruview-swarm/src/topology/gossip.rs
+++ /dev/null
@@ -1,78 +0,0 @@
-//! Gossip-based state dissemination for the swarm.
-
-use crate::types::NodeId;
-use rand::seq::SliceRandom;
-use serde::{Deserialize, Serialize};
-
-/// A gossip-propagated state value with versioning.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct GossipState {
- pub value: T,
- pub version: u64,
- pub origin: NodeId,
- pub timestamp_ms: u64,
-}
-
-impl GossipState {
- pub fn new(value: T, origin: NodeId, timestamp_ms: u64) -> Self {
- Self { value, version: 1, origin, timestamp_ms }
- }
-
- /// Last-write-wins merge: higher version wins; ties go to higher origin id.
- pub fn merge(a: GossipState, b: GossipState) -> GossipState {
- if a.version > b.version {
- a
- } else if b.version > a.version {
- b
- } else if a.origin.0 >= b.origin.0 {
- a
- } else {
- b
- }
- }
-
- /// Increment the version (call when mutating a local copy before gossiping).
- pub fn bump(&mut self) {
- self.version += 1;
- }
-
- /// Choose `fanout` random peer IDs to spread this state to, excluding the
- /// local node and the origin to avoid trivial loops.
- pub fn spread(
- &self,
- fanout: usize,
- all_peers: &[NodeId],
- local_id: NodeId,
- rng: &mut impl rand::Rng,
- ) -> Vec {
- let mut candidates: Vec = all_peers
- .iter()
- .copied()
- .filter(|&n| n != local_id && n != self.origin)
- .collect();
- candidates.shuffle(rng);
- candidates.truncate(fanout);
- candidates
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
-
- #[test]
- fn test_merge_higher_version_wins() {
- let a: GossipState = GossipState { value: 1, version: 2, origin: NodeId(1), timestamp_ms: 0 };
- let b: GossipState = GossipState { value: 2, version: 5, origin: NodeId(2), timestamp_ms: 0 };
- let merged = GossipState::merge(a, b);
- assert_eq!(merged.value, 2);
- }
-
- #[test]
- fn test_merge_tie_higher_origin_wins() {
- let a: GossipState = GossipState { value: 10, version: 3, origin: NodeId(5), timestamp_ms: 0 };
- let b: GossipState = GossipState { value: 20, version: 3, origin: NodeId(2), timestamp_ms: 0 };
- let merged = GossipState::merge(a, b);
- assert_eq!(merged.value, 10); // origin 5 > 2
- }
-}
diff --git a/v2/crates/ruview-swarm/src/topology/mesh.rs b/v2/crates/ruview-swarm/src/topology/mesh.rs
deleted file mode 100644
index 814afe71..00000000
--- a/v2/crates/ruview-swarm/src/topology/mesh.rs
+++ /dev/null
@@ -1,84 +0,0 @@
-//! Mesh topology: maintains a live view of all drone nodes.
-
-use crate::types::{DroneState, NodeId};
-use std::collections::HashMap;
-
-/// Hierarchical-mesh topology view.
-pub struct MeshTopology {
- pub nodes: HashMap,
- pub cluster_head: Option,
-}
-
-impl MeshTopology {
- pub fn new() -> Self {
- Self {
- nodes: HashMap::new(),
- cluster_head: None,
- }
- }
-
- /// Upsert a node's state.
- pub fn update_node(&mut self, state: DroneState) {
- self.nodes.insert(state.id, state);
- }
-
- /// Remove a node (e.g. on dropout).
- pub fn remove_node(&mut self, id: &NodeId) {
- self.nodes.remove(id);
- if self.cluster_head == Some(*id) {
- self.cluster_head = None;
- }
- }
-
- /// All active nodes (sorted by id for determinism).
- pub fn active_nodes(&self) -> Vec<&DroneState> {
- let mut v: Vec<_> = self.nodes.values().collect();
- v.sort_by_key(|s| s.id.0);
- v
- }
-
- /// Returns the `k` nearest nodes to `from`, sorted ascending by distance.
- pub fn nearest_k(&self, from: NodeId, k: usize) -> Vec {
- if let Some(origin) = self.nodes.get(&from) {
- let mut distances: Vec<(f64, NodeId)> = self
- .nodes
- .iter()
- .filter(|(&id, _)| id != from)
- .map(|(&id, s)| (origin.position.distance_to(&s.position), id))
- .collect();
- distances.sort_by(|a, b| a.0.partial_cmp(&b.0).unwrap_or(std::cmp::Ordering::Equal));
- distances.truncate(k);
- distances.into_iter().map(|(_, id)| id).collect()
- } else {
- vec![]
- }
- }
-}
-
-impl Default for MeshTopology {
- fn default() -> Self {
- Self::new()
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
- use crate::types::Position3D;
-
- #[test]
- fn test_nearest_k() {
- let mut topo = MeshTopology::new();
- let mut s0 = DroneState::default_at_origin(NodeId(0));
- s0.position = Position3D { x: 0.0, y: 0.0, z: 0.0 };
- let mut s1 = DroneState::default_at_origin(NodeId(1));
- s1.position = Position3D { x: 10.0, y: 0.0, z: 0.0 };
- let mut s2 = DroneState::default_at_origin(NodeId(2));
- s2.position = Position3D { x: 5.0, y: 0.0, z: 0.0 };
- topo.update_node(s0);
- topo.update_node(s1);
- topo.update_node(s2);
- let nearest = topo.nearest_k(NodeId(0), 1);
- assert_eq!(nearest, vec![NodeId(2)]);
- }
-}
diff --git a/v2/crates/ruview-swarm/src/topology/mod.rs b/v2/crates/ruview-swarm/src/topology/mod.rs
deleted file mode 100644
index 1d958e87..00000000
--- a/v2/crates/ruview-swarm/src/topology/mod.rs
+++ /dev/null
@@ -1,13 +0,0 @@
-//! Swarm topology: Raft consensus, gossip dissemination, mesh management.
-
-// NOTE: Raft consensus is ITAR-controlled (USML Category VIII(h)(12)).
-// Gossip and mesh are ungated — they are not controlled technologies.
-#[cfg(feature = "itar-unrestricted")]
-pub mod raft;
-pub mod gossip;
-pub mod mesh;
-
-#[cfg(feature = "itar-unrestricted")]
-pub use raft::{RaftConfig, RaftNode, RaftRole};
-pub use gossip::GossipState;
-pub use mesh::MeshTopology;
diff --git a/v2/crates/ruview-swarm/src/topology/raft.rs b/v2/crates/ruview-swarm/src/topology/raft.rs
deleted file mode 100644
index 30f947e2..00000000
--- a/v2/crates/ruview-swarm/src/topology/raft.rs
+++ /dev/null
@@ -1,254 +0,0 @@
-//! Raft-based cluster-head election for drone swarms.
-
-use crate::types::{DroneState, NodeId};
-use serde::{Deserialize, Serialize};
-use std::time::Duration;
-
-/// Configuration for the Raft consensus engine.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct RaftConfig {
- pub election_timeout_ms: u64,
- pub heartbeat_ms: u64,
- pub min_battery_pct: f32,
- pub min_link_quality: f32,
-}
-
-impl Default for RaftConfig {
- fn default() -> Self {
- Self {
- election_timeout_ms: 300,
- heartbeat_ms: 100,
- min_battery_pct: 20.0,
- min_link_quality: 0.4,
- }
- }
-}
-
-/// Role within the Raft cluster.
-#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
-pub enum RaftRole {
- Follower,
- Candidate,
- Leader,
-}
-
-/// A log entry stored by the Raft leader.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct LogEntry {
- pub term: u64,
- pub data: Vec,
-}
-
-/// Messages exchanged between Raft peers.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub enum RaftMessage {
- RequestVote {
- term: u64,
- candidate_id: NodeId,
- last_log_index: u64,
- last_log_term: u64,
- },
- VoteGranted {
- term: u64,
- voter_id: NodeId,
- granted: bool,
- },
- AppendEntries {
- term: u64,
- leader_id: NodeId,
- prev_log_index: u64,
- prev_log_term: u64,
- entries: Vec,
- leader_commit: u64,
- },
- AppendEntriesAck {
- term: u64,
- follower_id: NodeId,
- success: bool,
- match_index: u64,
- },
-}
-
-/// A Raft node driving cluster-head election within a swarm cluster.
-pub struct RaftNode {
- pub id: NodeId,
- pub role: RaftRole,
- pub current_term: u64,
- pub voted_for: Option,
- pub log: Vec,
- pub commit_index: u64,
- pub config: RaftConfig,
- /// Votes received as candidate.
- votes_received: u32,
- /// Elapsed time since last heartbeat/election-timeout reset (ms).
- elapsed_since_last_event_ms: u64,
-}
-
-impl RaftNode {
- pub fn new(id: NodeId, config: RaftConfig) -> Self {
- Self {
- id,
- role: RaftRole::Follower,
- current_term: 0,
- voted_for: None,
- log: Vec::new(),
- commit_index: 0,
- config,
- votes_received: 0,
- elapsed_since_last_event_ms: 0,
- }
- }
-
- /// Check whether a drone is eligible to become cluster head.
- pub fn is_eligible_leader(state: &DroneState, config: &RaftConfig) -> bool {
- state.battery_pct >= config.min_battery_pct
- && state.link_quality >= config.min_link_quality
- }
-
- /// Drive the Raft state machine by one time step.
- /// Returns a message to broadcast if an election event fires.
- pub fn tick(&mut self, elapsed: Duration, peers: &[DroneState]) -> Option {
- let elapsed_ms = elapsed.as_millis() as u64;
- self.elapsed_since_last_event_ms += elapsed_ms;
-
- match self.role {
- RaftRole::Leader => {
- if self.elapsed_since_last_event_ms >= self.config.heartbeat_ms {
- self.elapsed_since_last_event_ms = 0;
- let last_index = self.log.len() as u64;
- let last_term = self.log.last().map(|e| e.term).unwrap_or(0);
- return Some(RaftMessage::AppendEntries {
- term: self.current_term,
- leader_id: self.id,
- prev_log_index: last_index,
- prev_log_term: last_term,
- entries: vec![],
- leader_commit: self.commit_index,
- });
- }
- None
- }
- RaftRole::Follower | RaftRole::Candidate => {
- if self.elapsed_since_last_event_ms >= self.config.election_timeout_ms {
- self.elapsed_since_last_event_ms = 0;
- self.current_term += 1;
- self.role = RaftRole::Candidate;
- self.voted_for = Some(self.id);
- self.votes_received = 1;
-
- let last_index = self.log.len() as u64;
- let last_term = self.log.last().map(|e| e.term).unwrap_or(0);
- let quorum = (peers.len() / 2 + 1) as u32;
- // Immediately win if quorum of 1 (single node)
- if quorum <= 1 {
- self.role = RaftRole::Leader;
- }
- return Some(RaftMessage::RequestVote {
- term: self.current_term,
- candidate_id: self.id,
- last_log_index: last_index,
- last_log_term: last_term,
- });
- }
- None
- }
- }
- }
-
- /// Process an incoming Raft message and optionally produce a reply.
- pub fn handle_message(&mut self, msg: RaftMessage) -> Option {
- match msg {
- RaftMessage::RequestVote { term, candidate_id, .. } => {
- if term > self.current_term {
- self.current_term = term;
- self.role = RaftRole::Follower;
- self.voted_for = None;
- }
- let vote_granted = term >= self.current_term
- && (self.voted_for.is_none() || self.voted_for == Some(candidate_id));
- if vote_granted {
- self.voted_for = Some(candidate_id);
- self.elapsed_since_last_event_ms = 0;
- }
- Some(RaftMessage::VoteGranted {
- term: self.current_term,
- voter_id: self.id,
- granted: vote_granted,
- })
- }
- RaftMessage::VoteGranted { term, granted, .. } => {
- if term == self.current_term && self.role == RaftRole::Candidate && granted {
- self.votes_received += 1;
- // Assume we know how many peers there are via a simple threshold
- // The caller is responsible for passing all peer votes
- }
- None
- }
- RaftMessage::AppendEntries { term, leader_id: _, entries, leader_commit, .. } => {
- if term >= self.current_term {
- self.current_term = term;
- self.role = RaftRole::Follower;
- self.voted_for = None;
- self.elapsed_since_last_event_ms = 0;
- for entry in entries {
- self.log.push(entry);
- }
- if leader_commit > self.commit_index {
- self.commit_index = leader_commit.min(self.log.len() as u64);
- }
- let match_index = self.log.len() as u64;
- return Some(RaftMessage::AppendEntriesAck {
- term: self.current_term,
- follower_id: self.id,
- success: true,
- match_index,
- });
- }
- Some(RaftMessage::AppendEntriesAck {
- term: self.current_term,
- follower_id: self.id,
- success: false,
- match_index: self.log.len() as u64,
- })
- }
- RaftMessage::AppendEntriesAck { .. } => None,
- }
- }
-
- /// Promote to leader once quorum reached. Called by orchestrator.
- pub fn try_promote(&mut self, cluster_size: usize) {
- if self.role == RaftRole::Candidate {
- let quorum = (cluster_size / 2 + 1) as u32;
- if self.votes_received >= quorum {
- self.role = RaftRole::Leader;
- }
- }
- }
-}
-
-#[cfg(test)]
-mod tests {
- use super::*;
- use crate::types::DroneState;
-
- #[test]
- fn test_eligibility_check() {
- let config = RaftConfig::default();
- let mut state = DroneState::default_at_origin(NodeId(1));
- state.battery_pct = 50.0;
- state.link_quality = 0.9;
- assert!(RaftNode::is_eligible_leader(&state, &config));
-
- state.battery_pct = 5.0;
- assert!(!RaftNode::is_eligible_leader(&state, &config));
- }
-
- #[test]
- fn test_election_starts_after_timeout() {
- let config = RaftConfig { election_timeout_ms: 100, ..Default::default() };
- let mut node = RaftNode::new(NodeId(1), config);
- let result = node.tick(Duration::from_millis(200), &[]);
- assert!(result.is_some());
- assert_eq!(node.role, RaftRole::Leader); // single node wins immediately
- }
-}
diff --git a/v2/crates/ruview-swarm/src/types.rs b/v2/crates/ruview-swarm/src/types.rs
deleted file mode 100644
index b4245e29..00000000
--- a/v2/crates/ruview-swarm/src/types.rs
+++ /dev/null
@@ -1,178 +0,0 @@
-//! Core domain types for the swarm control system.
-
-use serde::{Deserialize, Serialize};
-
-/// Unique identifier for a drone node in the swarm.
-#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
-pub struct NodeId(pub u32);
-
-/// Unique identifier for a swarm cluster.
-#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
-pub struct ClusterId(pub u32);
-
-/// Unique identifier for a swarm task.
-#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
-pub struct TaskId(pub u64);
-
-/// 3-D position in local NED (North-East-Down) frame, metres.
-#[derive(Debug, Clone, Copy, Serialize, Deserialize, Default, PartialEq)]
-pub struct Position3D {
- pub x: f64, // north, m
- pub y: f64, // east, m
- pub z: f64, // down, m (negative = above ground)
-}
-
-impl Position3D {
- pub fn distance_to(&self, other: &Position3D) -> f64 {
- let dx = self.x - other.x;
- let dy = self.y - other.y;
- let dz = self.z - other.z;
- (dx * dx + dy * dy + dz * dz).sqrt()
- }
-
- pub fn zero() -> Self {
- Self { x: 0.0, y: 0.0, z: 0.0 }
- }
-}
-
-/// Velocity in local NED frame, m/s.
-#[derive(Debug, Clone, Copy, Serialize, Deserialize, Default)]
-pub struct Velocity3D {
- pub vx: f64,
- pub vy: f64,
- pub vz: f64,
-}
-
-impl Velocity3D {
- pub fn magnitude(&self) -> f64 {
- (self.vx * self.vx + self.vy * self.vy + self.vz * self.vz).sqrt()
- }
-}
-
-impl From<(f64, f64, f64)> for Position3D {
- fn from(t: (f64, f64, f64)) -> Self {
- Self { x: t.0, y: t.1, z: t.2 }
- }
-}
-
-impl From for Position3D {
- fn from(v: Velocity3D) -> Self {
- Self { x: v.vx, y: v.vy, z: v.vz }
- }
-}
-
-/// Full kinematic state of a drone node.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct DroneState {
- pub id: NodeId,
- pub position: Position3D,
- pub velocity: Velocity3D,
- pub heading_rad: f64,
- pub altitude_agl_m: f64,
- pub battery_pct: f32, // 0.0–100.0
- pub link_quality: f32, // 0.0–1.0 (RSSI normalised)
- pub timestamp_ms: u64,
-}
-
-impl DroneState {
- /// Construct a default state for a node at the origin.
- pub fn default_at_origin(id: NodeId) -> Self {
- Self {
- id,
- position: Position3D::zero(),
- velocity: Velocity3D::default(),
- heading_rad: 0.0,
- altitude_agl_m: 0.0,
- battery_pct: 100.0,
- link_quality: 1.0,
- timestamp_ms: 0,
- }
- }
-}
-
-/// CSI detection report from a drone's sensing payload.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct CsiDetection {
- pub drone_id: NodeId,
- pub confidence: f32, // 0.0–1.0
- pub victim_position: Option,
- pub timestamp_ms: u64,
-}
-
-/// A cell in the 2-D mission area probability grid.
-#[derive(Debug, Clone, Copy, Serialize, Deserialize, Default)]
-pub struct GridCell {
- pub x_idx: u32,
- pub y_idx: u32,
- pub victim_probability: f32, // Bayesian posterior
- pub pheromone: f32, // stigmergic coverage signal
- pub last_scanned_ms: u64,
-}
-
-/// Mission-level task that can be assigned to a drone.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct SwarmTask {
- pub id: TaskId,
- pub kind: TaskKind,
- pub priority: f32,
- pub target: Position3D,
- pub deadline_ms: Option,
- pub assigned_to: Option,
-}
-
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub enum TaskKind {
- CoverCell { grid_x: u32, grid_y: u32 },
- InvestigateVictim { estimated_position: Position3D },
- Triangulate { collaborators: Vec },
- ReturnToHome,
- HoverRelay,
- LandEmergency,
-}
-
-/// Role of a node within the hierarchical swarm.
-#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
-pub enum SwarmRole {
- ClusterHead,
- Worker,
- RelayNode,
- GroundControlStation,
-}
-
-/// Failsafe state alias re-exported from failsafe module.
-/// Used here to break circular dependency.
-#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
-pub enum FailSafeState {
- Nominal,
- AutonomousHold,
- LowBatteryWarn,
- ReturnToHome,
- EmergencyLand,
- EmergencyDiverge,
- ControlledDescent,
-}
-
-/// Top-level swarm error type.
-#[derive(Debug, thiserror::Error)]
-pub enum SwarmError {
- #[error("consensus error: {0}")]
- Consensus(String),
- #[error("communication error: {0}")]
- Communication(String),
- #[error("navigation error: {0}")]
- Navigation(String),
- #[error("security violation: {0}")]
- Security(String),
- #[error("geofence breach at {position:?}")]
- GeofenceBreach { position: Position3D },
- #[error("task allocation failed: {0}")]
- Allocation(String),
- #[error("sensing error: {0}")]
- Sensing(String),
- #[error("config error: {0}")]
- Config(#[from] toml::de::Error),
- #[error("io error: {0}")]
- Io(#[from] std::io::Error),
-}
-
-pub type SwarmResult = Result;
diff --git a/v2/crates/ruview-swarm/viz/sample_levy.jsonl b/v2/crates/ruview-swarm/viz/sample_levy.jsonl
deleted file mode 100644
index 69897956..00000000
--- a/v2/crates/ruview-swarm/viz/sample_levy.jsonl
+++ /dev/null
@@ -1,161 +0,0 @@
-{"type":"meta","profile":"sar · flight=levy_flight · learn=curiosity","drones":4,"area_w":400.00,"area_h":400.00,"victims":[[80.00,120.00],[240.00,180.00]]}
-{"type":"episode","ep":0,"mean_return":40.6910,"policy_loss":15.2719,"value_loss":12032.7422,"victims_found":0}
-{"type":"episode","ep":1,"mean_return":40.6910,"policy_loss":-6.7265,"value_loss":11992.9619,"victims_found":0}
-{"type":"episode","ep":2,"mean_return":40.6910,"policy_loss":-28.3518,"value_loss":11954.5889,"victims_found":0}
-{"type":"episode","ep":3,"mean_return":40.6910,"policy_loss":-50.8272,"value_loss":11913.7246,"victims_found":0}
-{"type":"episode","ep":4,"mean_return":40.6910,"policy_loss":-75.4711,"value_loss":11870.9639,"victims_found":0}
-{"type":"episode","ep":5,"mean_return":40.6910,"policy_loss":-102.5510,"value_loss":11825.7627,"victims_found":0}
-{"type":"episode","ep":6,"mean_return":40.6910,"policy_loss":-132.1274,"value_loss":11776.8301,"victims_found":0}
-{"type":"episode","ep":7,"mean_return":40.6910,"policy_loss":-163.8047,"value_loss":11723.4932,"victims_found":0}
-{"type":"episode","ep":8,"mean_return":40.6910,"policy_loss":-198.6059,"value_loss":11663.5625,"victims_found":0}
-{"type":"episode","ep":9,"mean_return":40.6910,"policy_loss":-238.1701,"value_loss":11596.6914,"victims_found":0}
-{"type":"episode","ep":10,"mean_return":40.6910,"policy_loss":-284.1328,"value_loss":11522.3838,"victims_found":0}
-{"type":"episode","ep":11,"mean_return":40.6910,"policy_loss":-336.2621,"value_loss":11440.9395,"victims_found":0}
-{"type":"episode","ep":12,"mean_return":40.6910,"policy_loss":-395.5074,"value_loss":11352.6396,"victims_found":0}
-{"type":"episode","ep":13,"mean_return":40.6910,"policy_loss":-463.2714,"value_loss":11257.4121,"victims_found":0}
-{"type":"episode","ep":14,"mean_return":40.6910,"policy_loss":-539.9746,"value_loss":11156.9658,"victims_found":0}
-{"type":"episode","ep":15,"mean_return":40.6910,"policy_loss":-626.7112,"value_loss":11052.9521,"victims_found":0}
-{"type":"episode","ep":16,"mean_return":40.6910,"policy_loss":-724.2371,"value_loss":10946.5713,"victims_found":0}
-{"type":"episode","ep":17,"mean_return":40.6910,"policy_loss":-835.2675,"value_loss":10841.5166,"victims_found":0}
-{"type":"episode","ep":18,"mean_return":40.6910,"policy_loss":-960.2383,"value_loss":10738.6182,"victims_found":0}
-{"type":"step","ep":19,"step":0,"t":0.00,"coverage":0.0148,"drones":[{"id":0,"x":17.05,"y":13.77,"hdg":0.491,"batt":100.0,"det":false},{"id":1,"x":213.61,"y":17.14,"hdg":1.102,"batt":100.0,"det":false},{"id":2,"x":8.93,"y":217.93,"hdg":1.706,"batt":100.0,"det":false},{"id":3,"x":204.53,"y":215.83,"hdg":2.325,"batt":100.0,"det":false}]}
-{"type":"step","ep":19,"step":1,"t":1.00,"coverage":0.0202,"drones":[{"id":0,"x":10.79,"y":18.75,"hdg":2.471,"batt":100.0,"det":false},{"id":1,"x":206.36,"y":13.76,"hdg":-2.706,"batt":100.0,"det":false},{"id":2,"x":7.50,"y":210.06,"hdg":-1.750,"batt":100.0,"det":false},{"id":3,"x":205.22,"y":207.86,"hdg":-1.484,"batt":100.0,"det":false}]}
-{"type":"step","ep":19,"step":2,"t":2.00,"coverage":0.0227,"drones":[{"id":0,"x":18.00,"y":15.28,"hdg":-0.448,"batt":100.0,"det":false},{"id":1,"x":214.15,"y":11.93,"hdg":-0.231,"batt":100.0,"det":false},{"id":2,"x":14.93,"y":213.03,"hdg":0.380,"batt":100.0,"det":false},{"id":3,"x":210.75,"y":213.64,"hdg":0.807,"batt":100.0,"det":false}]}
-{"type":"step","ep":19,"step":3,"t":3.00,"coverage":0.0239,"drones":[{"id":0,"x":17.50,"y":23.27,"hdg":1.633,"batt":100.0,"det":false},{"id":1,"x":210.28,"y":18.94,"hdg":2.075,"batt":100.0,"det":false},{"id":2,"x":8.58,"y":217.89,"hdg":2.488,"batt":100.0,"det":false},{"id":3,"x":203.17,"y":211.09,"hdg":-2.817,"batt":100.0,"det":false}]}
-{"type":"step","ep":19,"step":4,"t":4.00,"coverage":0.0242,"drones":[{"id":0,"x":12.69,"y":16.87,"hdg":-2.216,"batt":100.0,"det":false},{"id":1,"x":210.61,"y":10.94,"hdg":-1.530,"batt":100.0,"det":false},{"id":2,"x":13.21,"y":211.37,"hdg":-0.953,"batt":100.0,"det":false},{"id":3,"x":210.71,"y":208.41,"hdg":-0.342,"batt":100.0,"det":false}]}
-{"type":"step","ep":19,"step":5,"t":5.00,"coverage":0.0253,"drones":[{"id":0,"x":20.33,"y":19.24,"hdg":0.300,"batt":100.0,"det":false},{"id":1,"x":215.51,"y":17.26,"hdg":0.911,"batt":100.0,"det":false},{"id":2,"x":13.60,"y":219.36,"hdg":1.522,"batt":100.0,"det":false},{"id":3,"x":206.44,"y":215.18,"hdg":2.133,"batt":100.0,"det":false}]}
-{"type":"step","ep":19,"step":6,"t":6.00,"coverage":0.0253,"drones":[{"id":0,"x":13.41,"y":23.24,"hdg":2.618,"batt":100.0,"det":false},{"id":1,"x":207.75,"y":15.33,"hdg":-2.897,"batt":100.0,"det":false},{"id":2,"x":9.45,"y":212.53,"hdg":-2.117,"batt":100.0,"det":false},{"id":3,"x":205.61,"y":207.22,"hdg":-1.675,"batt":100.0,"det":false}]}
-{"type":"step","ep":19,"step":7,"t":7.00,"coverage":0.0256,"drones":[{"id":0,"x":17.81,"y":16.56,"hdg":-0.988,"batt":100.0,"det":false},{"id":1,"x":215.27,"y":12.60,"hdg":-0.347,"batt":100.0,"det":false},{"id":2,"x":17.30,"y":214.03,"hdg":0.189,"batt":100.0,"det":false},{"id":3,"x":211.19,"y":212.96,"hdg":0.800,"batt":100.0,"det":false}]}
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