feat(swarm): ADR-149 evaluation harness — GDOP, IQM+bootstrap CI, noise sweep (#875)
Stage-1 kinematic evaluator per ADR-149 (peer-reviewed). Pure Rust, no new deps. evals/: - gdop.rs: 2D Geometric Dilution of Precision ((HᵀH)⁻¹ trace-sqrt); None for <2 observers or collinear/singular geometry - stats.rs: IQM (Agarwal 2021) + 95% stratified-bootstrap CI (deterministic LCG) + probability_of_improvement - metrics.rs: EpisodeMetrics + AggregateMetrics::from_strata (IQM±CI, seed-stratified) - runner.rs: seeded kinematic rollout (FlightPattern-driven), seed×episode matrix, 3σ×3κ default noise sweep (Gaussian amplitude × von Mises phase) - report.rs + eval_swarm bin: generates evals/RESULTS.md leaderboard RESULTS.md surfaces the real coverage-vs-localization-precision trade-off via GDOP: partitioned wins coverage (100%) but single-drone sightings (GDOP 0 → 7.0m); pheromone gets multistatic fusion (GDOP 1.6 → 4.1m). Wi2SAR 5m paper-baseline row included. Stage-2 (Gazebo/PX4 SITL false-alarm + collision on median seeds) is documented follow-on. Tests: 116 default / 133 full+train (+13 eval tests), 0 failed. Clippy clean (-D warnings).
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# ADR-149: Drone Swarm Benchmarking & Evaluation Methodology — Metrics, Leaderboards, and Statistical Rigor
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| Field | Value |
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|------------|-----------------------------------------------------------------------------------------|
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| Status | Accepted (peer-reviewed 2026-05-30) |
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| Date | 2026-05-30 |
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| Deciders | ruv |
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| Relates to | ADR-148 (ruview-swarm), ADR-147 (OccWorld), ADR-146 (RF encoder), ADR-028 (witness) |
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> Companion to ADR-148. ADR-148 shipped the swarm and 5 criterion micro-benchmarks
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> plus a `SotaComparison` against Wi2SAR. This ADR defines **how we evaluate the swarm
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> rigorously** — what metrics, what statistics, what baselines, and an honest account
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> of which external leaderboards do and do not apply.
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---
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## 1. Context
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ADR-148's `ruview-swarm` reports performance via five `criterion` micro-benchmarks and a
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single `SotaComparison` (localization 1.732 m vs Wi2SAR 5 m; coverage ~223 s vs 810 s).
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These numbers are **internally valid but insufficient as scientific claims**:
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- The criterion figures (3.3 µs MARL inference, 43 µs RRT-APF, 54 ns fusion, 248 µs PPO
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step) measure **wall-clock latency**, not policy quality or coverage/localization quality.
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- The 1.732 m localization comes from a **single synthetic geometry** (3 drones at 120°
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around a known point), not a distribution of victim positions under realistic noise.
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- The 223 s coverage is an **analytic estimate** (`estimate_coverage_time_secs()`), not an
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episode rollout.
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- All numbers are **single-run point estimates**. The MARL reproducibility literature
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(Henderson 2018; Agarwal 2021; Gorsane 2022) shows single/few-seed point estimates
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routinely flip algorithm rankings and overstate gains.
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We need a defined, reproducible evaluation methodology before any "beats SOTA" claim can
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survive external review, and an honest position on external leaderboards.
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---
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## 2. Decision
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Adopt a two-tier evaluation methodology:
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1. **Micro-benchmarks (criterion)** — keep for compute-latency regression gating only.
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Explicitly labeled as latency, never as quality.
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2. **Domain evaluation harness** — a seeded, multi-run, statistically-reported harness
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producing SAR metrics (localization CEP, coverage, detection rate) and MARL metrics
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(IQM return, probability-of-improvement) over **≥10 seeds with 95% stratified-bootstrap
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confidence intervals**, against **≥3 baselines**, following the Agarwal/Gorsane standard.
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Do **not** claim leaderboard standing — no public leaderboard accepts drone-swarm CSI-SAR
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submissions. Comparisons to Wi2SAR are **paper-to-paper**, labeled as such, acknowledging
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the sensing-modality difference (RSS bearing vs CSI multi-view fusion).
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---
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## 3. External Leaderboard Landscape — Honest Assessment
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**There is no public, externally-administered leaderboard that accepts a drone-swarm,
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CSI-based, multi-view SAR system.** This is a research niche; comparison is paper-to-paper.
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The adjacent options and their fit:
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| Benchmark / Leaderboard | Domain | Live submission? | Fit for ruview-swarm |
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|-------------------------|--------|------------------|----------------------|
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| **Wi2SAR** (arxiv 2604.09115) | Drone WiFi SAR | No (paper) | **Direct baseline** — paper-to-paper only; RSS bearing ≠ CSI fusion |
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| **MARL4DRP** (Springer 2023) | Drone routing MARL | No | Closest drone-MARL benchmark; would need a routing→coverage adapter |
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| **CSI-Bench** (NeurIPS 2025) | Static WiFi sensing | Splits + paper baselines | Adjacent (localization task) but no moving-sensor/multi-view fusion |
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| **SMAC / SMACv2** | StarCraft cooperative MARL | No live LB | Structural analogy (CTDE) only; combat task, not coverage |
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| **PettingZoo MPE** (Simple Spread) | 2D cooperative particles | No | Cheap MARL **correctness check**, no physics/CSI |
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| **Melting Pot** | Social-dynamics MARL | Closed (NeurIPS '24) | Not applicable |
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| **MAMuJoCo / Hanabi / GRF / Overcooked** | Various cooperative MARL | No live LB | Not applicable |
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| **OmniDrones / gym-pybullet-drones / Pegasus** | Drone-control sim platforms | No (platforms) | **Training infrastructure**, not leaderboards; no CSI layer |
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**Conclusion:** We will (a) keep Wi2SAR as the cited paper baseline, (b) optionally build a
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MARL4DRP/MPE adapter to post a recognized cooperative-MARL number (tangential to SAR), and
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(c) **not** represent any internal number as a leaderboard placement.
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---
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## 4. Evaluation Metrics
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### 4.1 SAR Domain Metrics (primary — comparable to Wi2SAR)
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| Metric | Definition | Reporting |
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|--------|-----------|-----------|
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| Localization CEP50 | Median horizontal error, fused victim position vs ground truth | m, 95% CI |
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| Localization CEP95 | 95th-percentile horizontal error | m |
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| **GDOP** | Geometric Dilution of Precision of the contributing-drone constellation at detection time | dimensionless (tracked per detection) |
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| Coverage rate @ T | Fraction of area scanned ≥1× within T=240 s | %, 95% CI |
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| Coverage time to 95% | Time to scan 95% of bounded area | s, mean ± CI |
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| Time-to-first-detection | Mission start → first confident detection (conf > 0.85) | s, 95% CI |
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| Detection rate | P(detected \| victim present) per mission | %, 95% CI |
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| False-alarm rate | P(confident detection \| no victim) | %, 95% CI |
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| Collision rate | Collisions (d < 1.5 m) per mission | count/mission |
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| Overlap ratio | Fraction of path re-covering scanned cells | % |
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### 4.2 MARL Policy-Quality Metrics
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| Metric | Definition |
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|--------|-----------|
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| IQM episodic return | Interquartile mean over 10 seeds × 50 eval episodes (Agarwal 2021) |
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| Probability of improvement | P(MAPPO return > IPPO return) on a random episode |
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| Optimality gap | Expected gap to a defined reference performance |
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| Performance profile | Fraction of (seed, episode) with localization error < τ, plotted vs τ ∈ [0,10] m |
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| Sample efficiency | Return vs training steps (curve, not point) |
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### 4.3 Micro-benchmarks (criterion — latency only)
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Retained from ADR-148, **labeled as compute latency, not quality**:
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`marl_actor_inference` 3.3 µs · `rrt_apf_100iter` 43 µs · `multiview_fusion_3drones` 54 ns ·
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`demo_coverage_estimate` 100 ps · `ppo_update_64transitions` 248 µs. Purpose: prove the
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control loop has no compute bottleneck (all ≪ the 10 ms / 100 Hz budget) and gate
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performance regressions. They are **not** evidence of policy or localization quality.
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---
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## 5. Statistical Protocol (Agarwal 2021 / Gorsane 2022)
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| Requirement | Standard adopted |
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|-------------|------------------|
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| Seeds per condition | **≥10** training runs from distinct seeds |
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| Evaluation episodes | 50 fixed, versioned episodes per trained policy (10 victim layouts × 5 CSI-noise levels) |
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| Aggregate metric | **IQM** (not mean, not median) + performance profiles |
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| Confidence intervals | **95% stratified bootstrap**, 1,000 resamples |
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| Baselines (≥3) | Random walk (lower bound), Boustrophedon+manual-triangulation (heuristic), IPPO (no shared critic) |
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| Reproducibility | Versioned YAML config (drone count, area, victims, CSI σ amplitude / κ phase, wind, packet loss) + all seeds committed with results |
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Rationale: Henderson et al. (2018) found ≤5-seed point estimates flip rankings; Agarwal et
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al. (2021, NeurIPS Outstanding Paper) show IQM needs ~10 runs for the statistical power that
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the median needs ~200 runs for; Gorsane et al. (2022) made ≥10 seeds + IQM + stratified CIs
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the cooperative-MARL standard. `rliable` (google-research/rliable) is the reference impl.
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---
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## 6. Reproducibility Harness (`evals/`)
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A new evaluation harness (separate from criterion micro-benchmarks):
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1. **Seeded episodes** — every episode, noise perturbation, and training run seeded from a
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versioned config; seeds committed with results (no `Date.now()`/unseeded RNG).
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2. **Per-episode logging** — coverage %, localization error, GDOP, time-to-first-detection,
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collisions, detection binary → JSONL (reuses the ADR-148 telemetry schema).
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3. **Aggregation** — IQM ± 95% stratified-bootstrap CI across the 10-seed × 50-episode matrix.
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4. **Baseline sweep** — random / boustrophedon-heuristic / IPPO / MAPPO, so
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probability-of-improvement and performance profiles are computable.
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5. **Output** — committed `evals/RESULTS.md`: a reproducible internal leaderboard ranking
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our 6 flight patterns × learning patterns on the SAR metrics, plus the Wi2SAR paper row.
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This `RESULTS.md` is the **real, defensible "leaderboard" for this system** — patterns ranked
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against each other and the cited baseline, reproducibly, with CIs.
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### 6.1 Dual-stage pipeline (compute-cost mitigation)
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The full matrix is **10 seeds × 50 episodes × ≥4 conditions = ≥2,000 rollouts per policy**.
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Running each rollout against the OccWorld 3D prior (ADR-147, ~375 ms/inference) would melt
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the L4 / RTX 5080 budget. Split evaluation into two stages:
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- **Stage 1 — Kinematic (fast, full matrix).** Stripped vector environment; OccWorld paths
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pre-cached or treated as static analytical volumes. Produces episodic **return, IQM,
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sample-efficiency curves, coverage %, GDOP, localization error** over the full 10-seed matrix.
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- **Stage 2 — High-fidelity physics (sub-sampled).** Take the **3 median seeds** (by Stage-1
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IQM) into Gazebo + PX4 SITL with full CSI phase/amplitude noise. Extracts **false-alarm
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rate** and **collision rate** under realistic dynamics (heading-rate limits, APF repulsion,
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motor response) that the kinematic sim omits.
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Stage 1 is CI-runnable today; Stage 2 requires the Gazebo/PX4 SITL bring-up (follow-on).
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### 6.2 Noise sweep (coherence-gate threshold)
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The config generator systematically varies the two CSI noise parameters:
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- **σ** — Gaussian amplitude noise (CSI magnitude)
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- **κ** — von Mises phase concentration (lower κ = noisier phase)
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Sweeping (σ, κ) isolates the exact environmental threshold where `CrossViewpointAttention`
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(ADR-016) drops out of its coherence gate (`coherence_gate.rs` Accept → PredictOnly/Reject,
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ADR-135). This finds the operating envelope, not just a single-point accuracy.
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### 6.3 GDOP tracking
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Localization accuracy is meaningless without the constellation geometry that produced it.
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The harness records **GDOP** per detection: 3 drones in a ~120° constellation give the
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√3 ≈ 1.73× CRLB improvement; 3 **collinear** drones degrade toward the single-view
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Cramer-Rao limit (~2.9 m). Reporting localization error **stratified by GDOP band** prevents
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the headline number from being a best-case geometric artifact.
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---
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## 7. Evidence Grading of Current ADR-148 Numbers
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| Claim | Grade | Why |
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|-------|-------|-----|
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| criterion latencies (3.3 µs / 43 µs / 54 ns / 248 µs) | **High** | Deterministic compute, hardware-specific, reproducible |
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| Wi2SAR baseline (5 m, 160k m²/13.5 min) | **High** | Published field trial, open source |
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| 1.732 m 3-view localization | **Low–Medium** | Single synthetic geometry; no noise distribution; CRLB predicts ~2.9 m for N=3 |
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| 223 s 4-drone coverage | **Low** | Analytic estimate, not an episode rollout |
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| "beats SOTA" | **Directional only** | Valid as paper-to-paper direction; not leaderboard, not multi-seed |
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The √N multi-view scaling claim is theoretically sound (CRLB: σ ∝ 1/√(N·SNR); N=3 → √3 ≈
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1.73× improvement), but the measured 1.732 m must be reproduced over a victim-position and
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noise distribution before it is defensible.
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---
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## 8. Consequences
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### Positive
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- Converts scattered numbers into a reproducible, statistically-honest evaluation.
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- The `RESULTS.md` internal leaderboard ranks the 6 flight × 4 learning patterns fairly.
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- Aligns with the recognized MARL evaluation standard (IQM + stratified CIs + ≥10 seeds).
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- Honest external-leaderboard position avoids overclaiming.
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### Costs / Risks
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- ≥10 seeds × 50 episodes × N patterns × N baselines is a real compute cost — this is where
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the ADR-148 GCP L4 / local RTX 5080 training budget is actually spent.
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- Requires the MARL policy to be **trained to convergence** first (the ADR-148 5-episode CPU
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run shows decreasing value_loss, not convergence).
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- Coverage/localization must move from analytic estimate / synthetic geometry to **episode
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rollouts under realistic CSI noise** before headline numbers are republished.
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### Open issues → follow-on work
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1. Train MAPPO/IPPO to convergence (M4 follow-on) before running the eval harness.
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2. Build the seeded `evals/` harness + `RESULTS.md` generator.
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3. Optional: MARL4DRP or MPE Simple-Spread adapter for a recognized cooperative-MARL number.
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4. Re-state ADR-148 §14 headline numbers with CIs once the harness has run.
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---
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## 9. Research Notes & References
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Compiled by `ruflo-goals:deep-researcher` (2026-05-30). Full landscape in the agent record.
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**MARL evaluation rigor**
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- Henderson et al., "Deep RL That Matters", arxiv 1709.06560 — ≤5-seed estimates flip rankings
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- Agarwal et al., "Deep RL at the Edge of the Statistical Precipice", NeurIPS 2021, arxiv 2108.13264 — IQM, performance profiles, stratified bootstrap; `rliable`
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- Gorsane et al., "Standardised Evaluation Protocol for Cooperative MARL", NeurIPS 2022, arxiv 2209.10485 — ≥10 seeds + IQM standard
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- BenchMARL, arxiv 2312.01472 — operationalizes the above
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**Cooperative-MARL benchmarks**
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- SMACv2, arxiv 2212.07489 · PettingZoo MPE (Farama) · Melting Pot (DeepMind, NeurIPS 2024 contest) · MAMuJoCo (Gymnasium-Robotics) · MARL4DRP, Springer 2023 (closest drone-MARL)
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**Drone-sim platforms**
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- gym-pybullet-drones, arxiv 2103.02142 · OmniDrones, IEEE RA-L 2024 · Pegasus, arxiv 2307.05263 · Flightmare (IROS 2021) · AirSim (discontinued 2022) · Crazyswarm2
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**SAR / coverage / CSI sensing**
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- Wi2SAR, arxiv 2604.09115 (direct baseline: 5 m, 160k m²/13.5 min, 18.4° median AoA)
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- CSI-Bench, NeurIPS 2025, arxiv 2505.21866 (461 h WiFi sensing, localization task)
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- Coverage path planning, PMC9571681 (boustrophedon ~5% faster than spiral)
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- Bio-inspired SAR, Nature s41598-025-33223-z (PSO > Levy/ACO on exploration score)
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- CRLB for CSI localization, IEEE 8110647 (σ ∝ 1/√(N·SNR))
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**Tooling**
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- criterion.rs known limitations — wall-clock only, not algorithmic quality
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- rliable, github.com/google-research/rliable
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---
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*ADR authored with research support from `ruflo-goals:deep-researcher` (2026-05-30).
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Companion to ADR-148. Defines the evaluation methodology that the ADR-148 headline
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numbers must satisfy before being republished as defensible claims.*
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@ -78,3 +78,7 @@ harness = false
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[[bin]]
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name = "train_marl"
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required-features = ["train"]
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# ADR-149 Stage-1 evaluation CLI — pure Rust, no special feature needed.
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[[bin]]
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name = "eval_swarm"
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# ADR-149 evaluation outputs
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RESULTS.md is generated by the `eval_swarm` binary.
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# ruview-swarm Evaluation Results (ADR-149 Stage 1, kinematic)
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Statistically-rigorous evaluation harness: seeded multi-run rollouts with IQM + 95% stratified-bootstrap confidence intervals (Agarwal et al., NeurIPS 2021).
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## Run configuration
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- **Stage**: 1 (kinematic, self-contained, deterministic per seed)
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- **Episodes per pattern**: 100 (seed × episode matrix)
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- **CI method**: 95% stratified bootstrap of the IQM, stratified by seed
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- **GDOP**: 2-D geometric dilution of precision at first detection
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> **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-149 §6.1. The collision figures below are a kinematic min-separation proxy, not SITL physics.
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## Flight-pattern leaderboard
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| Flight pattern | Coverage IQM [95% CI] | Localization (m) IQM [95% CI] | Detection rate | Mean GDOP |
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|----------------|-----------------------|-------------------------------|----------------|-----------|
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| partitioned_lawnmower | 1.000 [1.000, 1.000] | 7.022 [5.669, 8.379] | 100.0% | 0.000 |
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| pheromone | 0.662 [0.652, 0.671] | 4.110 [3.346, 5.141] | 95.0% | 1.598 |
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| levy_flight | 0.490 [0.489, 0.491] | 3.523 [2.897, 4.160] | 100.0% | 0.000 |
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| boustrophedon | 0.370 [0.370, 0.370] | 2.740 [2.357, 3.207] | 100.0% | 0.000 |
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| spiral | 0.336 [0.336, 0.336] | 3.082 [2.678, 3.568] | 100.0% | 0.000 |
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| potential_field | 0.254 [0.252, 0.256] | 4.343 [3.489, 5.265] | 100.0% | 0.000 |
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| _Wi2SAR (paper baseline)_ | _n/a_ | _5.0 (paper)_ | _n/a_ | _n/a_ |
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_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._
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//! ADR-149 Stage-1 evaluation CLI.
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//!
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//! Runs the kinematic eval matrix over every flight pattern (default) and
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//! writes a ranked `RESULTS.md` leaderboard. Pure Rust — no special feature
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//! flag required, so it builds and runs in default CI.
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//!
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//! Defaults are intentionally small (10 seeds × 10 episodes) so the run is fast.
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//! The full ADR-149 reporting configuration is 10 seeds × 50 episodes — pass
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//! `--seeds 10 --episodes 50` for the publication run.
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//!
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//! ```text
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//! cargo run -p ruview-swarm --bin eval_swarm -- \
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//! --seeds 10 --episodes 10 --out crates/ruview-swarm/evals/RESULTS.md
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//! ```
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use std::path::PathBuf;
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use ruview_swarm::evals::metrics::AggregateMetrics;
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use ruview_swarm::evals::report::render_results_md;
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use ruview_swarm::evals::runner::{run_matrix, EvalConfig};
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use ruview_swarm::planning::patterns::FlightPattern;
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fn main() {
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let args: Vec<String> = std::env::args().collect();
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let mut seeds = 10usize;
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let mut episodes = 10usize;
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let mut out = PathBuf::from("crates/ruview-swarm/evals/RESULTS.md");
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let mut i = 1;
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while i < args.len() {
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match args[i].as_str() {
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"--seeds" => {
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i += 1;
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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-149 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-149 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());
|
||||
}
|
||||
|
|
@ -0,0 +1,118 @@
|
|||
//! 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<f64> {
|
||||
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());
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,150 @@
|
|||
//! Per-episode and aggregate SAR + MARL metrics (ADR-149 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<f64>,
|
||||
/// GDOP of the contributing-drone constellation at detection; `None` if none.
|
||||
pub gdop_at_detection: Option<f64>,
|
||||
/// Mission-elapsed seconds to first detection; `None` if no detection.
|
||||
pub time_to_first_detection_s: Option<f64>,
|
||||
/// 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-149).
|
||||
pub fn from_strata(per_seed: &[Vec<EpisodeMetrics>], boot_seed: u64) -> Self {
|
||||
const N_BOOT: usize = 1000;
|
||||
|
||||
let coverage_strata: Vec<Vec<f64>> = per_seed
|
||||
.iter()
|
||||
.map(|s| s.iter().map(|e| e.coverage_pct).collect())
|
||||
.collect();
|
||||
let return_strata: Vec<Vec<f64>> = 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<Vec<f64>> = per_seed
|
||||
.iter()
|
||||
.map(|s| {
|
||||
s.iter()
|
||||
.filter_map(|e| e.localization_error_m)
|
||||
.collect::<Vec<f64>>()
|
||||
})
|
||||
.filter(|v: &Vec<f64>| !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<f64>, 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);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,19 @@
|
|||
//! ADR-149 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-149 §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;
|
||||
|
|
@ -0,0 +1,120 @@
|
|||
//! RESULTS.md leaderboard generator (ADR-149 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-149 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-149 §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"));
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,364 @@
|
|||
//! Stage-1 kinematic rollout + seed × episode matrix (ADR-149).
|
||||
//!
|
||||
//! 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-149
|
||||
pub episodes_per_seed: usize, // e.g. 50
|
||||
pub victims: Vec<Position3D>,
|
||||
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<Position3D> = (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<Position3D> = 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<f64> = None;
|
||||
let mut gdop_val: Option<f64> = None;
|
||||
let mut t_detect: Option<f64> = 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<Position3D> = 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<Position3D> = 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<f64>) {
|
||||
// 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<Vec<EpisodeMetrics>> {
|
||||
(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-149 noise sweep grid: cartesian product of σ × κ levels.
|
||||
pub fn default_noise_sweep() -> Vec<NoiseLevel> {
|
||||
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);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,203 @@
|
|||
//! 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::<f64>() / 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::<f64>() / n as f64;
|
||||
}
|
||||
mid.iter().sum::<f64>() / 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<f64>],
|
||||
n_boot: usize,
|
||||
seed: u64,
|
||||
) -> ConfidenceInterval {
|
||||
let pooled: Vec<f64> = 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<f64> = 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<f64> = (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}");
|
||||
}
|
||||
}
|
||||
|
|
@ -13,6 +13,7 @@ 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;
|
||||
|
|
|
|||
Loading…
Reference in New Issue