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rUv 0d3d835bf8 feat(swarm): add ruview-swarm crate — drone swarm control system (ADR-148) (#862) 
* 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<T> → &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>
 2026-05-30 16:00:59 -04:00
ruv 9ad550d95f feat(worldmodel): Candle Rust port + GCP GPU scripts (ADR-147 Phase 4+6) 
Candle native port — wifi-densepose-occworld-candle v0.3.0:
- config.rs: OccWorldConfig (14 params matching occworld.py)
- vqvae.rs: ClassEmbedding(18→64), VQCodebook(512×512, squared-L2),
  QuantConv/PostQuantConv(1×1 Conv2d), fold_3d_to_2d helpers
  ResNet encoder/decoder are documented stubs (Phase 5 checkpoint pending)
- transformer.rs: full Candle MHA transformer (2 layers, temporal+spatial
  cross-attention, FFN, pre-norm residuals)
- inference.rs: OccWorldCandle::dummy() + ::load() + predict()
  InferenceOutput: sem_pred(1,15,200,200,16) + trajectory_priors
- 14/14 tests pass (12 lib + 2 doctests)

GCP GPU scripts — scripts/gcp/:
- provision_training.sh: a2-highgpu-8g (8×A100 40GB) for Phase 5 retraining
- run_training.sh: rsync + torchrun 8-GPU train + checkpoint download
- provision_cosmos.sh: a2-ultragpu-1g (A100 80GB) for Cosmos evaluation
- cosmos_eval.sh: run Cosmos-Transfer2.5 inference, download results
- teardown.sh: safe checkpoint download + instance delete

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-29 20:52:51 -04:00