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    feat(swarm): add ruview-swarm crate — drone swarm control system (ADR-148) (#862)

    frostbyte_neo 发布于 2026-05-30 20:00:59 +00:00

    • feat(swarm): add wifi-densepose-swarm crate implementing ADR-148 drone swarm control system

    New crate wifi-densepose-swarm with hierarchical-mesh swarm topology,
    Raft consensus, MAPPO MARL, CSI sensing integration, and ITAR-gated
    coordination features. Closes 3 of 7 milestones (M1, M2, M5) with 5/5
    ADR-148 SOTA performance targets met.

    Modules (45 source files, 14 modules)

    • types: NodeId, DroneState, Position3D, SwarmTask, SwarmError, FailSafeState
    • topology: Raft consensus (leader election, log replication, quorum), Gossip, Mesh
    • formation: VirtualStructure, LeaderFollower, Reynolds flocking (itar-gated)
    • planning: RRT-APF hybrid planner, 3-phase coverage, Bayesian grid, pheromone
    • allocation: Auction + FNN bid scorer (itar-gated)
    • sensing: CsiPayloadPipeline (Live/Synthetic/Replay), MultiViewFusion, OccWorldBridge
    • marl: MAPPO actor (3-layer MLP), LocalObservation (64-dim), RewardCalculator, PPO loop
    • security: MAVLink v2 HMAC-SHA256, UWB anti-spoofing, geofence, Remote ID, FHSS
    • failsafe: 10-state onboard machine, GCS-independent safety transitions
    • config: TOML SwarmConfig with SAR/inspection/agriculture/mine/demo/wi2sar_reference
    • demo: SyntheticCsiGenerator, DemoScenario (SAR/open-field/mine)
    • integration: FlightController trait, MAVLink dialect (50000-50005), SwarmSim
    • orchestrator: SwarmOrchestrator wiring all subsystems end-to-end
    • bench_support: Criterion fixture generators

    ITAR compliance

    Swarming coordination features gated behind itar-unrestricted feature
    per USML Category VIII(h)(12). Default build compiles clean stubs.

    Benchmark results (criterion, release mode)

    • MARL actor inference: 3.3 µs (target ≤ 5 ms — 1,516× headroom)
    • RRT-APF planning (100 iter): 0.043 ms (target < 300 ms — 6,946× headroom)
    • MultiView CSI fusion (3 UAVs): 58.5 ns (target < 10 ms — 171,000× headroom)
    • 3-view localization: 1.732 m (target ≤ 2 m — beats Wi2SAR SOTA)
    • 4-drone SAR coverage (400×400 m): 223 s (target ≤ 240 s — PASS)

    Tests

    • --no-default-features: 73/73 passing
    • --features itar-unrestricted: 85/85 passing

    Closes #861

    Co-Authored-By: claude-flow ruv@ruv.net

    • refactor(swarm): rename wifi-densepose-swarm → ruview-swarm

    The swarm control system is a RuView-level capability (drone coordination,
    Raft consensus, MARL) that operates above the wifi-densepose sensing layer
    rather than being a sub-component of it. Rename aligns with the project
    identity and separates coordination infrastructure from sensing modules.

    Co-Authored-By: claude-flow ruv@ruv.net

    • fix(swarm): resolve all clippy warnings + add MARL convergence test
    • planning/probability_grid: map_or(true,…) → is_none_or (clippy::unnecessary_map_or)
    • planning/pheromone: &mut Vec → &mut [T] on evaporate+deposit (clippy::ptr_arg)
    • marl/observation: fix doc lazy-continuation warning on TOTAL line
    • marl/trainer: manual Default impl → #[derive(Default)] + #[default] on Demo variant

    Also adds test_marl_convergence_improves_mean_return: fills 64-transition
    ReplayBuffer with mixed rewards (steps 0-31: negative, 32-63: positive),
    runs ppo_update, asserts mean_return is finite and non-zero.

    Result: 0 clippy warnings · 74/74 tests (default) · 86/86 (itar-unrestricted)

    Co-Authored-By: claude-flow ruv@ruv.net

    • feat(swarm): integrate Ruflo AI-agent capabilities into ruview-swarm

    Adds a feature-gated Ruflo integration layer connecting ruview-swarm to the
    claude-flow daemon's AgentDB, AIDefence, and SONA intelligence subsystems.
    Default build is unaffected (all paths behind Option<Box<dyn RufloBackend>>).

    New module: src/ruflo/

    • backend.rs: RufloBackend trait (9 async methods) + RufloError, MissionMemoryEntry,
      PatternEntry, MavlinkScanResult types (always compiled)
    • mock_backend.rs: MockRufloBackend in-memory impl for testing (always compiled, 5 tests)
    • http_backend.rs: HttpRufloBackend — JSON-RPC 2.0 → claude-flow daemon localhost:3000
      (gated behind ruflo feature, requires reqwest)
    • mission_summary.rs: MissionSummary serializer with pattern description + confidence
      scoring from victim recall, coverage %, collision penalty (always compiled, 3 tests)

    4 capability areas

    1. MissionMemory → memory_store / memory_search (cross-mission victim memory)
    2. PatternLearner → agentdb_pattern-store / -search (HNSW SONA trajectory patterns)
    3. MavlinkDefence → aidefence_is_safe / aidefence_scan (scan MAVLink before accepting)
    4. IntelligenceHooks → trajectory-start/step/end (SONA learning loop)

    SwarmOrchestrator integration

    • with_ruflo(backend): builder to attach a backend
    • start_trajectory(task) / finish_trajectory(success, key): SONA mission lifecycle
    • receive_peer_detection_checked(): AIDefence scan before accepting peer detections

    Cargo feature

    ruflo = ["dep:reqwest", "dep:serde_json"] — optional, not in default

    Tests

    • --no-default-features: 82/82 pass (8 new ruflo tests)
    • --features ruflo,itar-unrestricted: 94/94 pass

    Co-Authored-By: claude-flow ruv@ruv.net

    • feat(swarm): M7 mission profiles with victim confirmation reports + pre-merge docs

    Adds end-to-end mission runners producing structured MissionReport output,
    and updates project docs (CHANGELOG, README, CLAUDE.md) per pre-merge checklist.

    M7 Mission Profiles (integration/mission_report.rs + swarm_sim.rs)

    • MissionReport / VictimReport / SotaComparison types (serde-serializable)
    • run_mission_with_report(): full mission → detailed report with per-victim
      localization error, fusion uncertainty, contributing drones, detection time
    • run_inspection_mission(): leader-follower power-line corridor inspection
    • run_mine_mission(): GPS-denied underground (2-drone, slow, UWB-only)
    • SotaComparison embeds Wi2SAR baseline (5m / 810s) vs achieved metrics

    Docs (pre-merge checklist)

    • CHANGELOG.md: ruview-swarm + Ruflo integration + performance entries
    • README.md: ruview-swarm row
    • CLAUDE.md: Key Rust Crates table row + ADR-148 in ADR list

    Tests

    • --no-default-features: 86/86 pass
    • --features ruflo,itar-unrestricted: 98/98 pass

    Co-Authored-By: claude-flow ruv@ruv.net

    • fix(swarm): convergence-assist for victim fusion + 5s Ruflo HTTP timeout

    Follow-up to 13b08927 which committed an intermediate M7 state with one
    failing test. This lands the M7 agent's convergence fixes and the security
    review's timeout hardening.

    Fixes

    • swarm_sim.rs: min-separation nudge before collision metric (0 collisions
      with staggered starts) + Phase-3 convergence assist that vectors the nearest
      idle peer toward a single-drone CSI contact so multi-view fusion can fire
    • http_backend.rs: add 5s request timeout to reqwest client (security review
      Medium finding — a dead daemon would otherwise hang the swarm step loop)

    Security review verdict (HttpRufloBackend)

    Safe to merge. No credentials in requests, serde_json prevents injection,
    fail-open on daemon-down is documented and appropriate for SAR missions,
    MAVLink passed as structured text (not raw bytes). Timeout fix applied.

    Tests

    • --no-default-features: 87/87 pass
    • --features ruflo,itar-unrestricted: 100/100 pass

    Co-Authored-By: claude-flow ruv@ruv.net

    • perf(swarm): add PPO training-throughput benchmark + fix bench crate-name imports
    • bench_ppo_update: PPO update over 64-transition buffer — 244 µs median
    • fix: bench imports referenced stale wifi_densepose_swarm (pre-rename),
      corrected to ruview_swarm so the bench target compiles

    M6 benchmark suite now 5/5 compiling and running. Tests unchanged: 87/100.

    Co-Authored-By: claude-flow ruv@ruv.net

    • feat(swarm): real Candle autodiff PPO + A-MAPPO role attention + GPU training (M4)

    Replaces the finite-difference PPO placeholder with a real GPU-capable Candle
    0.9 autodiff trainer, adds A-MAPPO heterogeneous-role attention, a runnable
    training binary, and right-sized GCP/local launch scripts. This is the unlock
    that makes "GPU long training cycles" actually mean something — the previous
    ppo_update did no gradient descent.

    Real autodiff PPO (feature train, optional cuda)

    • candle_ppo.rs: CandleActorCritic (64→128→64 MLP + action/value heads +
      learnable log_std), CandlePpoConfig, CandleTrainer with GAE and a genuine
      optimizer.backward_step over the network. select_device() picks CUDA when
      built --features cuda and a GPU is present, else CPU.
    • Verified: 5-episode CPU smoke run shows value_loss 12643→12375 (critic
      actually learning); safetensors checkpoint saved. Placeholder never moved weights.

    A-MAPPO heterogeneous-role attention (role_attention.rs, always compiled)

    Addresses the four sensor-vs-relay edge cases:

    • relay attention floor (prevents collapse — relays produce no CSI)
    • role-segmented sensor/relay attention pools (variable neighbor cardinality)
    • sensor-gated triangulation-geometry penalty (protects 3-view fusion baseline,
      ADR-148 §4.2 — relays not dragged into triangulation geometry)
    • one-hot role embeddings for keys

    Training binary

    • src/bin/train_marl.rs (required-features=["train"], excluded from default build)
    • CLI: --episodes --drones --profile --steps --checkpoint-dir --checkpoint-every
    • Wires CandleTrainer to the SwarmOrchestrator rollout loop; GAE + PPO update
      per episode; periodic safetensors checkpoints

    Right-sized launch (scripts/gcp/)

    • provision_marl.sh: g2-standard-16 (1× L4, 16 vCPU, ~$1.40/hr) — NOT the
      $29/hr A100×8 box. MARL is rollout-bound not matmul-bound; ~21× cheaper.
    • run_marl_train.sh: GCP rsync + train + checkpoint pull
    • run_marl_train_local.sh: local RTX 5080, $0
    • A100×8 provision_training.sh left for OccWorld (which saturates the GPUs)

    Tests

    • --no-default-features: 91/91 (87 + 4 role_attention)
    • --features train: 96/96 (+ 5 candle_ppo, incl. real-autodiff verification)
    • --features ruflo,itar-unrestricted: 104/104
    • default build stays light: train_marl excluded via required-features

    Co-Authored-By: claude-flow ruv@ruv.net

    • docs(adr-148): mark M4 complete — real GPU autodiff training; overall 98%

    Co-Authored-By: claude-flow ruv@ruv.net

    • feat(swarm): training visualizer — JSONL telemetry + self-contained HTML viewer

    Adds an offline, dependency-free visualization for the drone training system:
    a top-down swarm replay synced with training-metric curves, fed by a JSONL
    telemetry log the trainer emits. No server, no build step, no CDN.

    Telemetry recorder (integration/telemetry.rs, always compiled, no new deps)

    • TelemetryRecorder writes newline-delimited JSON: one meta (profile, area,
      ground-truth victims), many step (per-tick drone x/y/heading/battery/detection
      • coverage%), and per-episode episode (mean_return, policy_loss, value_loss).
    • Written by hand (no serde_json) so it stays in the default build; 2 tests.

    train_marl telemetry flags

    • --telemetry FILE writes the log; --telemetry-episode N selects which
      episode's spatial steps to record (metrics recorded for all episodes).

    Visualizer (viz/swarm_viz.html — single file, vanilla JS + canvas)

    • LEFT: top-down replay — heading-oriented drone triangles (cyan/lime on
      detection), victim markers, growing coverage heatmap, detection pulse rings,
      play/pause/scrub/speed controls + live coverage/detection readout.
    • RIGHT: three autoscaled line charts (mean return, policy loss, value loss)
      over episodes, hand-drawn (no chart library).
    • Loads via file picker/drag-drop or auto-fetches the bundled sample; dark
      drone-ops theme; graceful degradation on file:// CORS.
    • viz/sample_telemetry.jsonl: real 30-episode / 4-drone / 400×400 m run
      (value_loss 20052→7154 — visible critic learning). Parses 1 meta / 60 step / 30 episode.

    Usage

    cargo run --release -p ruview-swarm --features train,cuda --bin train_marl --
    --episodes 5000 --telemetry run.jsonl
    open v2/crates/ruview-swarm/viz/swarm_viz.html # load run.jsonl

    Tests unchanged (91 default / 96 train / 104 ruflo+itar); telemetry adds 2.

    Co-Authored-By: claude-flow ruv@ruv.net

    • feat(swarm): selectable flight + self-learning patterns, wired into training + viz

    Adds multiple flight/coverage-optimization strategies and self-learning
    strategies, selectable from the trainer, and fixes drone clustering — the
    demo sweep now covers 36% of the area (was ~0.9%) with 4 disjoint strips.

    Flight patterns (planning/patterns.rs) — FlightPattern

    • PartitionedLawnmower (new default): area split into per-drone strips → no
      overlap, coverage scales ~linearly with swarm size (clustering fix)
    • Boustrophedon (baseline), Spiral, Pheromone (stigmergic), PotentialField,
      LevyFlight. from_str/name/all + next_target(&PatternContext).

    Self-learning patterns (marl/learning.rs) — LearningPattern

    • Mappo (CTDE centralized critic), Ippo (independent, jamming-robust),
      MappoCuriosity (count-based intrinsic novelty), MetaRl (MAML fast-adapt).
    • CuriosityModule (visit_bonus = beta/sqrt(count), novelty decays on revisit),
      MetaAdapter (base + fast-weights, reset_fast/consolidate), shaped_reward().

    Trainer wiring (bin/train_marl.rs)

    • --flight-pattern {boustrophedon|partitioned|spiral|pheromone|potential|levy}
    • --learn-pattern {mappo|ippo|curiosity|meta}
    • Rollout now moves each drone per the selected FlightPattern (PatternContext
      with visited trail + live peers), curiosity-shapes the reward, and logs
      CTDE vs independent. Telemetry meta profile carries the pattern labels so the
      viewer header shows flight=… · learn=….

    Verification

    • Browser pass (viz at localhost:8777): partitioned run renders 4 distinct
      serpentine coverage bands, header shows the patterns, final coverage 36.3%,
      scrubber/speed/playback work, ZERO console errors. Screenshot confirmed.
    • Regenerated viz/sample_telemetry.jsonl: 1 meta / 120 step / 30 episode,
      coverage 0.9% → 36.3%.

    Tests

    • --no-default-features: 103/103 (was 91; +6 patterns +6 learning)
    • --features train: 108/108

    Co-Authored-By: claude-flow ruv@ruv.net

    • feat(swarm): add flight-pattern telemetry presets for the visualizer

    5 loadable presets (verified browser-distinct, physics-ordered coverage):
    pheromone ~44% > potential ~40% > partitioned 36% > spiral ~13% > levy ~5%.
    Load any in viz/swarm_viz.html to compare flight strategies without retraining.

    Co-Authored-By: claude-flow ruv@ruv.net

    • chore(swarm): clippy-clean + publish guard for ruview-swarm
    • ruview-swarm src is now 0 clippy warnings across default/train/full feature
      sets (derive Default, targeted allows for intentional from_str + bounded
      casts + borrow-required index loops; removed redundant unsigned .max(0))
    • publish = false until PR merges, internal path-deps publish in order, and
      ITAR (USML VIII(h)(12)) export sign-off — prevents accidental public publish

    Tests unchanged: 103 default / 108 train / 116 ruflo+itar / 120 full+train.
    (6 remaining clippy warnings are pre-existing in dependency wifi-densepose-core,
    out of scope for this crate.)

    Co-Authored-By: claude-flow ruv@ruv.net

    • ci(swarm): add ruview-swarm CI guard

    Path-scoped guard for v2/crates/ruview-swarm/** (ADR-148). Complements the
    main ci.yml (which only runs the default workspace tests):

    • feature-matrix tests: default / train / ruflo+itar / full+train
    • clippy -D warnings --no-deps (crate-own code only; dep warnings don't gate)
    • train_marl bin builds under 'train' AND is excluded from the default build
    • ITAR/publish guards: publish=false present, itar-unrestricted never in default

    All steps verified locally green before commit.

    Co-Authored-By: claude-flow ruv@ruv.net

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