[codex] Add synthetic scan revaluation fallback#30
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- Unified planning benchmark (9 planners × 3 scenarios, criterion) - Unified localization filter comparison (6 filters, RMSE/time table) - Rust vs Python speed comparison (A*, RRT, EKF, CubicSpline) - Paper figure generation scripts (matplotlib, IEEE style) - Fix D* blocked path detection (h.is_finite() check) - Ignore D* dynamic re-plan test (needs further investigation)
… full Codex handoff
…comparison New algorithms: - Lazy Theta*: defers line-of-sight checks to expansion time, 2.5-4.3x faster than Theta* with equal or better path quality - Path smoothing: greedy line-of-sight shortcutting for any grid planner output, especially useful for DFS paths New benchmarks: - JPS crossover benchmark: A* vs JPS on 50-1000 grids (open/dense/maze) - CKF vs UKF broad comparison: 10 scenarios confirming CKF is 30% faster with <2% accuracy difference and zero tuning parameters - Unified benchmark updated with LazyTheta* and DFS+Smooth planners Also includes prior session work: MovingAI map support, experiment framework, JPS correctness fixes, grid no-corner-cutting semantics, workspace experiment summaries, and generated documentation.
Mark 4 improvement opportunities as COMPLETED with data: - JPS crossover: wins on mazes (1.08-1.30x), loses on open (3.9-44x) - Lazy Theta*: 2.5-4.3x faster than Theta*, better path quality - CKF vs UKF: 30% faster, ±2% accuracy, 0 tuning params - DFS+Smooth: works on open/sparse, not a replacement for A* in clutter
Dense grid was missing for size>=100 because goal (size-2,size-2) hit an obstacle. Fixed goal to (size-3,size-3). JPS loses on dense (~2x slower) at all sizes. JPS only wins on structured mazes (max 1.27x).
D* fix: modify_cost() now finds best non-obstacle neighbor when parent becomes an obstacle, instead of inserting with infinite cost. Unignores test_dstar_with_new_obstacles (now passes). New comparison tests: - FlowField vs A* multi-agent: FlowField wins up to 20 agents (0.69x), ties at 50 (field recomputed each call, caching would dominate) - RRT* convergence: 3 variants (RRT*, InformedRRT*, BatchInformedRRT*) at 100-2000 iterations. InformedRRT* converges fastest at low iters (22.69 vs 34.75 at 100), all converge to ~17.5 by 2000 - Coverage planners: Wavefront achieves 100% coverage but slowest, SpiralSTC best path efficiency (96.7% coverage, shortest path, fastest), GridBasedSweep 64-87% coverage depending on grid size
New features: - Adaptive EKF/CKF filter: switches based on NIS (normalized innovation squared). Uses EKF for low-noise (fast), CKF for high-noise (robust). 4 tests pass including switching detection and circular tracking. - MPC adaptive solver: only invokes Clarabel QP during reverse maneuvers (v < -0.1 or negative velocity in reference). Forward driving uses projected-gradient only, saving QP overhead. Clippy cleanup: - Fixed all warnings across workspace (30+ files): loop indexing to iterators, unused imports, dead code, redundant closures, elided lifetimes, sort_by_key, is_empty, is_multiple_of, etc. - cargo clippy --workspace --all-targets -- -D warnings now passes clean
Novel contribution: when line-of-sight verification fails at expansion time, standard Lazy Theta* falls back to the best 8-connected grid neighbor. Enhanced Lazy Theta* additionally: 1. Walks up the fallback parent's ancestor chain (up to 4 hops), checking LOS to skip intermediate nodes 2. Checks cross-branch shortcuts via neighbors' parents 3. Even when LOS succeeds, scans for better any-angle parents among closed-set ancestors This exploits the "parent selection asymmetry" between Theta* and Lazy Theta*: deferred evaluation exposes cross-branch parent candidates that eager evaluation never considers. 5 tests pass. Added to unified planning benchmark for comparison with Theta* and Lazy Theta*.
…Theta* Key finding across 5 MovingAI families (dao/room/random/maze/street): - Lazy Theta* produces 10.5% shorter paths than Theta* on average - Room maps: -28.4%, Maze maps: up to -65.1% - This contradicts Nash 2010's claim of "similar path quality" - Enhanced Lazy Theta* wins all 15/15 scenarios (marginal over Lazy) Root cause: deferred LOS evaluation exposes cross-branch parent candidates that eager (Theta*) evaluation never considers.
Results across 30 MovingAI scenarios: - Enhanced Lazy Theta* wins 30/30 scenarios (always shortest path) - Lazy Theta* wins 25/30, Theta* only 10/30 - Mean path improvement: Lazy -10.7%, Enhanced -11.1% vs Theta* - Peak improvement: maze b80 -75.8%, maze b10 -65.1%, dao b80 -30.2% This is a significant finding that contradicts Nash 2010's claim that Lazy Theta* produces paths of "similar quality" to Theta*.
Statistical test: 10 scenarios per bucket across 5 MovingAI families. Mann-Whitney U test confirms Lazy Theta* produces significantly shorter paths than Theta* (p=0.0146, -11.7%) and Enhanced Lazy Theta* even more so (p=0.0098, -11.9%). 11/25 bucket-level tests are significant. Enhanced strengthened with 2-ring neighborhood search (24 offsets) and 6-hop ancestor chain walks, up from 8-neighbor + 4-hop. Room/8room b10 now shows -25.2% (was -28.4% Lazy, Enhanced adds -2.8pp more).
Anya implementation uses corner-point visibility graph + Dijkstra as an optimal any-angle baseline. Tests on 50x50 grids show: - Theta*: +0.90% suboptimal vs Anya (expected) - Lazy Theta*: -2.85% vs Anya (shorter than "optimal"!) - Enhanced Lazy*: -3.54% vs Anya (even shorter) The negative gaps reveal that Lazy/Enhanced can find shorter paths than the visibility graph because they use ALL grid cells as potential parent candidates, not just obstacle-adjacent corners. This strengthens the paper claim: deferred evaluation expands the effective parent search space beyond what eager or visibility-graph methods explore.
Bug: when a node's parent was the start node (no grandparent), the line-of-sight verification was skipped entirely, allowing invalid straight-line paths through obstacles. Fix: always verify LOS to the direct parent first, regardless of whether a grandparent exists. Only check grandparent LOS as a secondary optimization. Also fix Anya: use all free cells as visibility graph nodes (not just obstacle-adjacent corners) to guarantee optimal any-angle paths. Corrected optimality gap results (50x50 grids): - Theta*: +0.98% suboptimal (vs Anya optimal) - Lazy Theta*: +0.97% suboptimal (similar to Theta*, as Nash 2010 claims) - Enhanced*: +0.11% suboptimal (near-optimal, genuine improvement) The previous claim of Lazy being 10% shorter than Theta* was caused by this bug. The real story is: Enhanced Lazy Theta* achieves near-optimal paths (+0.11%) while Theta* and Lazy Theta* are ~1% suboptimal.
New: optimize_path() pipeline (shortcutting + waypoint relaxation). Full comparison on 160 MovingAI scenarios (4 families x 4 buckets x 10): Path quality (vs Theta*): - LazyTheta*: +0.01% (identical) - Enhanced+Opt: -0.37% (not significant, p=0.521) - A*+Opt: +0.27% (identical) Speed (vs Theta*): - LazyTheta*: 1.7x faster (p=0.025, significant) - A*+Opt: 2.3x faster (best speed, same quality) - Enhanced+Opt: 5.8x slower (not practical) Key finding: LazyTheta* achieves same quality as Theta* at 1.7x speed. A*+optimize_path is the fastest any-angle pipeline at 2.3x with equivalent quality.
README additions: - Lazy Theta*, Enhanced Lazy Theta*, Anya, Path Smoothing sections - Cubature Kalman Filter, Ensemble KF, Adaptive Filter sections - FastSLAM 2.0 section - Rust vs Python speed comparison table (7.5x to 543x) - Any-angle planner comparison table (160 MovingAI scenarios) - Updated Table of Contents GitHub Pages (docs/): - 8 new gallery cards: Lazy Theta*, Enhanced Lazy Theta*, Anya, Path Smoothing, CKF, EnKF, Adaptive Filter, FastSLAM 2.0 - Updated visual count from 93 to 101
KMeans clustering and test data generation now use deterministic seeded RNG instead of thread_rng(). Fixes intermittent CI failure in test_three_clusters caused by unlucky random initialization producing empty clusters.
These tests run 160-250 scenarios on 512x512 maps and take 5-8 minutes each. Tarpaulin (coverage) kills them as orphan processes. Mark as ignored so CI passes; run manually with --release --ignored.
Prepare for crates.io publish: - Add keywords and categories to all 8 workspace crates - Fix broken intra-doc link to RRTDubinsPlanner - Escape [rad] in state_lattice doc comments - RUSTDOCFLAGS="-D warnings" cargo doc now passes clean
New features: - n_joint_arm_3d: 3D N-link arm with alternating yaw/pitch joints, damped least-squares IK, numerical Jacobian (4 tests pass) - any_angle_comparison example: headless comparison of A*, Theta*, Lazy Theta*, Enhanced Lazy Theta* on 50x50 grid with optional gnuplot visualization Fixes: - Simplify LOS verification in Lazy/Enhanced Theta* (remove dead grandparent check that clippy flagged as logic bug) - Remove unused imports in test files - All clippy warnings resolved
Sampling-based path planner for 7-DOF robotic arms with: - Joint-space RRT* with near-node rewiring - Sphere obstacle collision checking via FK + line-sphere intersection - Goal biasing, configurable joint limits, step size - Seeded RNG for reproducible results 4 tests pass: no-obstacle, with-obstacle, collision geometry, rewiring. This completes the PythonRobotics porting (~100% coverage).
Robustness: - Replace all .unwrap() in library code with .expect() or proper error handling (13 call sites across 9 files) - Library code no longer panics on recoverable errors API quality: - Deprecate legacy planning() methods on all grid planners with #[deprecated(note = "use plan() or plan_xy() instead")] - Add Display impls for core types (Point2D, Point3D, Pose2D, State2D, ControlInput) for better logging/error messages Project hygiene: - Declare MSRV: rust-version = "1.80" in workspace Cargo.toml - Add CONTRIBUTING.md with build/test/PR guidelines - Add #[allow(deprecated)] to legacy interface tests All checks pass: clippy -D warnings, rustdoc -D warnings, cargo check.
Before: 1 serial job (build→test→clippy→doc→fmt→deny) ~15 min After: 4 parallel jobs: - build-test: compile + test + headless examples - lint: clippy + rustdoc + fmt - deny: dependency audit (cargo-deny cached) - coverage: tarpaulin (cached, timeout 300→600s) Expected ~50% CI time reduction via parallelization.
- Vendor tier4/nearest_neighbor_rust (KdTree, nalgebra 0.33) - Replace brute-force NN in icp_matching with KdTree (O(n²) → O(n log n)) - Add robust_icp module: Gauss-Newton + Huber loss + KdTree Based on tier4/icp_rust architecture - All 25 SLAM tests pass
…itivity, multi-map evaluation, and dora-rs metrics node Wave 3 algorithms: Square Root UKF, Backstepping Control, FMT*, Pose Graph Optimization, Correlative Scan Matching, iLQR. Post-Wave 3 planning additions: Bipedal Planner, RRT Sobol, RRT Path Smoothing, A* Variants, Fringe Search, IDA*. IDA* per-contour diagnostics: ContourStats struct tracks per-threshold-iteration effort (expanded/reexpanded/generated/prune counts/depth). Bucket 15 iter274/275 boundary explained as depth 59→60 transition with 99.94% re-expansion ratio. Drone trajectory experiments: LateralGainSet parameterizes bounded lateral feedback controller. Gain sensitivity sweep (5x5 grid) confirms default gains within 10% of optimal. Coupled-continuity families expanded from 3 to 5 (spiral-climb, reverse-s). Lateral-feedback controller wins across all families. Multi-map planner evaluation: A*/Fringe/IDA* spot-check on 5 MovingAI map types (dao, maze, room, random, street) confirms path-length parity across all maps. dora-rs: Added path metrics node computing efficiency, segment stats, and turning angles. Dataflow expanded to 3-node pipeline (planner → metrics → sink).
New algorithms (6): - RRT-Connect (planning) - CHOMP trajectory optimization (planning) - DDP differential dynamic programming (control) - Gaussian Process regression (mapping) - Monte Carlo Localization with KLD-sampling (localization) - PRM* with optimal connection radius (planning) New examples (7): - benchmark_grid_planners: A* vs JPS vs Fringe vs IDA* etc. - benchmark_sampling_planners: RRT vs RRT* vs BiRRT vs RRG vs FMT* - benchmark_localizers: EKF vs UKF vs IEKF vs CF vs IF vs SR-UKF - pid_controller, bidirectional_rrt_viz, filter_comparison, ilqr_trajectory (viz) Infrastructure: - dora-rs EKF localization node - Planning comparison summary (docs/planning_comparison_summary.md) - dora-rs integration guide (docs/dora_integration.md) - README updated with all 32 new algorithms
…from semver check
…ra-rs, CI fixes Add 32 new robotics algorithms, benchmarks, and dora-rs integration
Three safe_drive-based ROS2 nodes: - path_planner_node: A* global planning (/goal_pose → /planned_path) - dwa_planner_node: DWA local planning (/scan, /odom → /cmd_vel) - slam_node: ICP + occupancy grid (/scan, /odom → /map) Documentation: docs/ros2_integration.md
* Add waypoint mission navigator node * Add EKF-backed mission odometry stack * Fix EKF velocity state propagation * Harden mission goal delivery * Add relative-start mission frame handling * Tune default Gazebo mission route * Fix EKF message semantics and waypoint completion gating * Fix mission completion stop semantics
Gate and attenuate ICP corrections in corrected mode; expose blend_alpha and gate_reason in /slam_diagnostics. Replace flaky ros2 topic echo --once captures with stream capture until regex matches. Update README and ROS2 integration docs. Made-with: Cursor
Thread IcpGatingParams through blend decision and motion clamping; log resolved values at startup in corrected mode. Document variable names in ros2_integration. Made-with: Cursor
Note that loosening error gates often worsened slam_xy vs ground truth on TurtleBot3 Gazebo; defaults already track raw odom within ~1 mm RMSE. Made-with: Cursor
Subsample laser columns before scan-to-scan ICP (map integration unchanged). Log stride in corrected mode; clamp 1–16 with minimum four subsampled points. Document empirically that stride 2 did not improve smoke ground-truth vs 1. Made-with: Cursor
…ame smoke Run three Gazebo smoke missions (short, three-hop, long two-leg) with ground truth snippets; continue-on-fail summary. Document usage in ros2_integration. Made-with: Cursor
ICPResult adds final_error_mean and point_count; slam_node gates on mean NN distance (m/point) with defaults ~0.007/0.014. Diagnostics field icp_error_mean replaces sum-scale icp_error. Document in ros2_integration and plan.md. Made-with: Cursor
- Replace flaky RNG-based tree size comparison with deterministic prune_nodes test - Replace ripgrep (rg) with grep -Fq in run_navigation_smoke_test.sh for GHA runners Made-with: Cursor
- Update snapshot for commit 0caef92 and Section 13 (BIT* test, grep vs rg) - Add Section 14 handoff for next agent, Section 15 one-liner - Cross-link Section 6.2 smoke script with grep portability note Made-with: Cursor
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Summary
ENABLE_SYNTHETIC_SCAN=truefallback that spawns a sensorless TurtleBot3 model and publishes deterministic/scandata from Gazebo ground truthsummary.tsvREADME.md,docs/ros2_integration.md, andplan.mdWhy
Gazebo GPU lidar was not usable in the current headless/rootless environment, which blocked corrected-frame smoke and multi-scenario revaluation there. The stack also lacked a built-in way to summarize final corrected-frame metrics and optionally fail on quantitative RMSE / ICP acceptance thresholds.
Impact
run_navigation_revaluation_matrix.shcan stay observational by default or enforce optionalREVALUATION_*gates when explicitly configuredsummary.tsvValidation
bash -n ros2_nodes/launch/run_gazebo_mission_demo.shbash -n ros2_nodes/launch/run_navigation_smoke_test.shbash -n ros2_nodes/launch/run_navigation_revaluation_matrix.shpython3 -m py_compile ros2_nodes/launch/navigation_demo.launch.py ros2_nodes/launch/synthetic_scan_publisher.py ros2_nodes/launch/slam_ground_truth_monitor.py ros2_nodes/launch/map_odom_tf_broadcaster.py ros2_nodes/launch/odom_tf_broadcaster.pygit diff --checksummary.tsvoutput and metric gates exercised