Two deploy-time fixes that take v1 360°-trained BC/RL from 0/n to n/n
penned on the canonical 140° LiDAR proto for diff/field:
* SheepTracker now supports a consensus stage: new detections start as
candidate tracks invisible to get_positions(). A candidate must
accumulate consensus_k matches within consensus_radius_m of itself
inside a consensus_max_age window to be promoted; otherwise it
expires. Real sheep self-confirm within 3 frames (≪0.05 m/step);
wall-return cluster centroids jitter beyond 0.3 m as the dog moves
and never promote. consensus_k=1 (default) is a no-op so unconfigured
callers and HERDING_DEFAULT keep prior behaviour.
* HERDING_WEBOTS preset gets consensus_k=3, radius=0.3, max_age=20,
plus longer forget_steps=300 and predict_steps=180 so confirmed
sheep persist through long FOV-occlusion gaps a narrow 140° cone
produces. max_new_tracks_per_step=1 still rate-caps spawn bursts.
* shepherd_dog.py BC/RL empty-obs fallback now rotates the desired
heading with step_count so the cone actively sweeps the field
instead of driving due north into the wall.
Verified in headless Webots (HERDING_USE_GT=0, LiDAR only):
BC diff/field: 5/5 @ 11698, 10/10 @ 15079
RL diff/field: 5/5 @ 10039, 9/10 @ 18200 (timeout)
Strömbom diff/field: 5/5 @ 7528
All previously 0/n. 120 unit tests pass; 9 new consensus tests cover
the candidate stage, promotion radius, and one-shot phantom rejection.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Johnny Fernandes
2d23289052