14 KiB
ADR-149: Drone Swarm Benchmarking & Evaluation Methodology — Metrics, Leaderboards, and Statistical Rigor
| Field | Value |
|---|---|
| Status | Accepted (peer-reviewed 2026-05-30) |
| Date | 2026-05-30 |
| Deciders | ruv |
| Relates to | ADR-148 (ruview-swarm), ADR-147 (OccWorld), ADR-146 (RF encoder), ADR-028 (witness) |
Companion to ADR-148. ADR-148 shipped the swarm and 5 criterion micro-benchmarks plus a
SotaComparisonagainst Wi2SAR. This ADR defines how we evaluate the swarm rigorously — what metrics, what statistics, what baselines, and an honest account of which external leaderboards do and do not apply.
1. Context
ADR-148's ruview-swarm reports performance via five criterion micro-benchmarks and a
single SotaComparison (localization 1.732 m vs Wi2SAR 5 m; coverage ~223 s vs 810 s).
These numbers are internally valid but insufficient as scientific claims:
- The criterion figures (3.3 µs MARL inference, 43 µs RRT-APF, 54 ns fusion, 248 µs PPO step) measure wall-clock latency, not policy quality or coverage/localization quality.
- The 1.732 m localization comes from a single synthetic geometry (3 drones at 120° around a known point), not a distribution of victim positions under realistic noise.
- The 223 s coverage is an analytic estimate (
estimate_coverage_time_secs()), not an episode rollout. - All numbers are single-run point estimates. The MARL reproducibility literature (Henderson 2018; Agarwal 2021; Gorsane 2022) shows single/few-seed point estimates routinely flip algorithm rankings and overstate gains.
We need a defined, reproducible evaluation methodology before any "beats SOTA" claim can survive external review, and an honest position on external leaderboards.
2. Decision
Adopt a two-tier evaluation methodology:
- Micro-benchmarks (criterion) — keep for compute-latency regression gating only. Explicitly labeled as latency, never as quality.
- Domain evaluation harness — a seeded, multi-run, statistically-reported harness producing SAR metrics (localization CEP, coverage, detection rate) and MARL metrics (IQM return, probability-of-improvement) over ≥10 seeds with 95% stratified-bootstrap confidence intervals, against ≥3 baselines, following the Agarwal/Gorsane standard.
Do not claim leaderboard standing — no public leaderboard accepts drone-swarm CSI-SAR submissions. Comparisons to Wi2SAR are paper-to-paper, labeled as such, acknowledging the sensing-modality difference (RSS bearing vs CSI multi-view fusion).
3. External Leaderboard Landscape — Honest Assessment
There is no public, externally-administered leaderboard that accepts a drone-swarm, CSI-based, multi-view SAR system. This is a research niche; comparison is paper-to-paper. The adjacent options and their fit:
| Benchmark / Leaderboard | Domain | Live submission? | Fit for ruview-swarm |
|---|---|---|---|
| Wi2SAR (arxiv 2604.09115) | Drone WiFi SAR | No (paper) | Direct baseline — paper-to-paper only; RSS bearing ≠ CSI fusion |
| MARL4DRP (Springer 2023) | Drone routing MARL | No | Closest drone-MARL benchmark; would need a routing→coverage adapter |
| CSI-Bench (NeurIPS 2025) | Static WiFi sensing | Splits + paper baselines | Adjacent (localization task) but no moving-sensor/multi-view fusion |
| SMAC / SMACv2 | StarCraft cooperative MARL | No live LB | Structural analogy (CTDE) only; combat task, not coverage |
| PettingZoo MPE (Simple Spread) | 2D cooperative particles | No | Cheap MARL correctness check, no physics/CSI |
| Melting Pot | Social-dynamics MARL | Closed (NeurIPS '24) | Not applicable |
| MAMuJoCo / Hanabi / GRF / Overcooked | Various cooperative MARL | No live LB | Not applicable |
| OmniDrones / gym-pybullet-drones / Pegasus | Drone-control sim platforms | No (platforms) | Training infrastructure, not leaderboards; no CSI layer |
Conclusion: We will (a) keep Wi2SAR as the cited paper baseline, (b) optionally build a MARL4DRP/MPE adapter to post a recognized cooperative-MARL number (tangential to SAR), and (c) not represent any internal number as a leaderboard placement.
4. Evaluation Metrics
4.1 SAR Domain Metrics (primary — comparable to Wi2SAR)
| Metric | Definition | Reporting |
|---|---|---|
| Localization CEP50 | Median horizontal error, fused victim position vs ground truth | m, 95% CI |
| Localization CEP95 | 95th-percentile horizontal error | m |
| GDOP | Geometric Dilution of Precision of the contributing-drone constellation at detection time | dimensionless (tracked per detection) |
| Coverage rate @ T | Fraction of area scanned ≥1× within T=240 s | %, 95% CI |
| Coverage time to 95% | Time to scan 95% of bounded area | s, mean ± CI |
| Time-to-first-detection | Mission start → first confident detection (conf > 0.85) | s, 95% CI |
| Detection rate | P(detected | victim present) per mission | %, 95% CI |
| False-alarm rate | P(confident detection | no victim) | %, 95% CI |
| Collision rate | Collisions (d < 1.5 m) per mission | count/mission |
| Overlap ratio | Fraction of path re-covering scanned cells | % |
4.2 MARL Policy-Quality Metrics
| Metric | Definition |
|---|---|
| IQM episodic return | Interquartile mean over 10 seeds × 50 eval episodes (Agarwal 2021) |
| Probability of improvement | P(MAPPO return > IPPO return) on a random episode |
| Optimality gap | Expected gap to a defined reference performance |
| Performance profile | Fraction of (seed, episode) with localization error < τ, plotted vs τ ∈ [0,10] m |
| Sample efficiency | Return vs training steps (curve, not point) |
4.3 Micro-benchmarks (criterion — latency only)
Retained from ADR-148, labeled as compute latency, not quality:
marl_actor_inference 3.3 µs · rrt_apf_100iter 43 µs · multiview_fusion_3drones 54 ns ·
demo_coverage_estimate 100 ps · ppo_update_64transitions 248 µs. Purpose: prove the
control loop has no compute bottleneck (all ≪ the 10 ms / 100 Hz budget) and gate
performance regressions. They are not evidence of policy or localization quality.
5. Statistical Protocol (Agarwal 2021 / Gorsane 2022)
| Requirement | Standard adopted |
|---|---|
| Seeds per condition | ≥10 training runs from distinct seeds |
| Evaluation episodes | 50 fixed, versioned episodes per trained policy (10 victim layouts × 5 CSI-noise levels) |
| Aggregate metric | IQM (not mean, not median) + performance profiles |
| Confidence intervals | 95% stratified bootstrap, 1,000 resamples |
| Baselines (≥3) | Random walk (lower bound), Boustrophedon+manual-triangulation (heuristic), IPPO (no shared critic) |
| Reproducibility | Versioned YAML config (drone count, area, victims, CSI σ amplitude / κ phase, wind, packet loss) + all seeds committed with results |
Rationale: Henderson et al. (2018) found ≤5-seed point estimates flip rankings; Agarwal et
al. (2021, NeurIPS Outstanding Paper) show IQM needs ~10 runs for the statistical power that
the median needs ~200 runs for; Gorsane et al. (2022) made ≥10 seeds + IQM + stratified CIs
the cooperative-MARL standard. rliable (google-research/rliable) is the reference impl.
6. Reproducibility Harness (evals/)
A new evaluation harness (separate from criterion micro-benchmarks):
- Seeded episodes — every episode, noise perturbation, and training run seeded from a
versioned config; seeds committed with results (no
Date.now()/unseeded RNG). - Per-episode logging — coverage %, localization error, GDOP, time-to-first-detection, collisions, detection binary → JSONL (reuses the ADR-148 telemetry schema).
- Aggregation — IQM ± 95% stratified-bootstrap CI across the 10-seed × 50-episode matrix.
- Baseline sweep — random / boustrophedon-heuristic / IPPO / MAPPO, so probability-of-improvement and performance profiles are computable.
- Output — committed
evals/RESULTS.md: a reproducible internal leaderboard ranking our 6 flight patterns × learning patterns on the SAR metrics, plus the Wi2SAR paper row.
This RESULTS.md is the real, defensible "leaderboard" for this system — patterns ranked
against each other and the cited baseline, reproducibly, with CIs.
6.1 Dual-stage pipeline (compute-cost mitigation)
The full matrix is 10 seeds × 50 episodes × ≥4 conditions = ≥2,000 rollouts per policy. Running each rollout against the OccWorld 3D prior (ADR-147, ~375 ms/inference) would melt the L4 / RTX 5080 budget. Split evaluation into two stages:
- Stage 1 — Kinematic (fast, full matrix). Stripped vector environment; OccWorld paths pre-cached or treated as static analytical volumes. Produces episodic return, IQM, sample-efficiency curves, coverage %, GDOP, localization error over the full 10-seed matrix.
- Stage 2 — High-fidelity physics (sub-sampled). Take the 3 median seeds (by Stage-1 IQM) into Gazebo + PX4 SITL with full CSI phase/amplitude noise. Extracts false-alarm rate and collision rate under realistic dynamics (heading-rate limits, APF repulsion, motor response) that the kinematic sim omits.
Stage 1 is CI-runnable today; Stage 2 requires the Gazebo/PX4 SITL bring-up (follow-on).
6.2 Noise sweep (coherence-gate threshold)
The config generator systematically varies the two CSI noise parameters:
- σ — Gaussian amplitude noise (CSI magnitude)
- κ — von Mises phase concentration (lower κ = noisier phase)
Sweeping (σ, κ) isolates the exact environmental threshold where CrossViewpointAttention
(ADR-016) drops out of its coherence gate (coherence_gate.rs Accept → PredictOnly/Reject,
ADR-135). This finds the operating envelope, not just a single-point accuracy.
6.3 GDOP tracking
Localization accuracy is meaningless without the constellation geometry that produced it. The harness records GDOP per detection: 3 drones in a ~120° constellation give the √3 ≈ 1.73× CRLB improvement; 3 collinear drones degrade toward the single-view Cramer-Rao limit (~2.9 m). Reporting localization error stratified by GDOP band prevents the headline number from being a best-case geometric artifact.
7. Evidence Grading of Current ADR-148 Numbers
| Claim | Grade | Why |
|---|---|---|
| criterion latencies (3.3 µs / 43 µs / 54 ns / 248 µs) | High | Deterministic compute, hardware-specific, reproducible |
| Wi2SAR baseline (5 m, 160k m²/13.5 min) | High | Published field trial, open source |
| 1.732 m 3-view localization | Low–Medium | Single synthetic geometry; no noise distribution; CRLB predicts ~2.9 m for N=3 |
| 223 s 4-drone coverage | Low | Analytic estimate, not an episode rollout |
| "beats SOTA" | Directional only | Valid as paper-to-paper direction; not leaderboard, not multi-seed |
The √N multi-view scaling claim is theoretically sound (CRLB: σ ∝ 1/√(N·SNR); N=3 → √3 ≈ 1.73× improvement), but the measured 1.732 m must be reproduced over a victim-position and noise distribution before it is defensible.
8. Consequences
Positive
- Converts scattered numbers into a reproducible, statistically-honest evaluation.
- The
RESULTS.mdinternal leaderboard ranks the 6 flight × 4 learning patterns fairly. - Aligns with the recognized MARL evaluation standard (IQM + stratified CIs + ≥10 seeds).
- Honest external-leaderboard position avoids overclaiming.
Costs / Risks
- ≥10 seeds × 50 episodes × N patterns × N baselines is a real compute cost — this is where the ADR-148 GCP L4 / local RTX 5080 training budget is actually spent.
- Requires the MARL policy to be trained to convergence first (the ADR-148 5-episode CPU run shows decreasing value_loss, not convergence).
- Coverage/localization must move from analytic estimate / synthetic geometry to episode rollouts under realistic CSI noise before headline numbers are republished.
Open issues → follow-on work
- Train MAPPO/IPPO to convergence (M4 follow-on) before running the eval harness.
- Build the seeded
evals/harness +RESULTS.mdgenerator. - Optional: MARL4DRP or MPE Simple-Spread adapter for a recognized cooperative-MARL number.
- Re-state ADR-148 §14 headline numbers with CIs once the harness has run.
9. Research Notes & References
Compiled by ruflo-goals:deep-researcher (2026-05-30). Full landscape in the agent record.
MARL evaluation rigor
- Henderson et al., "Deep RL That Matters", arxiv 1709.06560 — ≤5-seed estimates flip rankings
- Agarwal et al., "Deep RL at the Edge of the Statistical Precipice", NeurIPS 2021, arxiv 2108.13264 — IQM, performance profiles, stratified bootstrap;
rliable - Gorsane et al., "Standardised Evaluation Protocol for Cooperative MARL", NeurIPS 2022, arxiv 2209.10485 — ≥10 seeds + IQM standard
- BenchMARL, arxiv 2312.01472 — operationalizes the above
Cooperative-MARL benchmarks
- SMACv2, arxiv 2212.07489 · PettingZoo MPE (Farama) · Melting Pot (DeepMind, NeurIPS 2024 contest) · MAMuJoCo (Gymnasium-Robotics) · MARL4DRP, Springer 2023 (closest drone-MARL)
Drone-sim platforms
- gym-pybullet-drones, arxiv 2103.02142 · OmniDrones, IEEE RA-L 2024 · Pegasus, arxiv 2307.05263 · Flightmare (IROS 2021) · AirSim (discontinued 2022) · Crazyswarm2
SAR / coverage / CSI sensing
- Wi2SAR, arxiv 2604.09115 (direct baseline: 5 m, 160k m²/13.5 min, 18.4° median AoA)
- CSI-Bench, NeurIPS 2025, arxiv 2505.21866 (461 h WiFi sensing, localization task)
- Coverage path planning, PMC9571681 (boustrophedon ~5% faster than spiral)
- Bio-inspired SAR, Nature s41598-025-33223-z (PSO > Levy/ACO on exploration score)
- CRLB for CSI localization, IEEE 8110647 (σ ∝ 1/√(N·SNR))
Tooling
- criterion.rs known limitations — wall-clock only, not algorithmic quality
- rliable, github.com/google-research/rliable
ADR authored with research support from ruflo-goals:deep-researcher (2026-05-30).
Companion to ADR-148. Defines the evaluation methodology that the ADR-148 headline
numbers must satisfy before being republished as defensible claims.