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https://github.com/ruvnet/RuView
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ruv
944e014c59
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>
46 lines
1.4 KiB
Rust
46 lines
1.4 KiB
Rust
//! Benchmark support utilities: scenario builders and timing helpers for criterion benchmarks.
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use crate::types::{DroneState, NodeId, Position3D, Velocity3D};
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/// Generate N drone states arranged in a grid.
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pub fn grid_drone_states(n: usize, spacing_m: f64) -> Vec<DroneState> {
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let side = (n as f64).sqrt().ceil() as usize;
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(0..n)
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.map(|i| {
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let row = i / side;
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let col = i % side;
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DroneState {
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id: NodeId(i as u32),
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position: Position3D {
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x: col as f64 * spacing_m,
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y: row as f64 * spacing_m,
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z: -30.0,
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},
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velocity: Velocity3D::default(),
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heading_rad: 0.0,
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altitude_agl_m: 30.0,
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battery_pct: 80.0,
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link_quality: 0.9,
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timestamp_ms: 0,
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}
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})
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.collect()
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}
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/// Generate N evenly-spaced positions in a circle.
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pub fn circle_positions(n: usize, radius_m: f64) -> Vec<(NodeId, Position3D)> {
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(0..n)
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.map(|i| {
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let angle = 2.0 * std::f64::consts::PI * i as f64 / n as f64;
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(
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NodeId(i as u32),
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Position3D {
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x: radius_m * angle.cos(),
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y: radius_m * angle.sin(),
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z: -30.0,
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},
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)
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})
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.collect()
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}
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