FutureRay: 機敏な四足歩行ナビゲーションのための制御整合型未来レンジ予測
FutureRay: Control-Aligned Future Range for Agile Quadruped Navigation
深度履歴から将来の距離レンジと遭遇リスクを予測し、反応・制動時間を考慮した局所プランナで四足歩行ロボットの動的障害物回避を改善する手法を提案。
著者: Tianhao Zang, Shanze Wang, Ziqian Wang, Liyou Luo, Zihan Liu, Xingjian Xie, Wei Zhang
分類: cs.RO
原文アブストラクト
Moving obstacles can block a previously clear route while a quadruped robot executes a motion command. We investigate whether predicting changing clearance improves navigation when motion selection accounts for the robot footprint and the time needed to react and brake. We present FutureRay, which predicts ranges across viewing directions and future times, together with encounter risk, from depth-derived range history and observable robot motion. Training emphasizes near-term clearance and penalizes errors that overstate available space. A local planner queries the same forecast for candidate headings and combines it with current observations to check clearance around the robot footprint. Model-based reaction--braking limits guide speed selection, and the resulting velocity commands are passed to a fixed locomotion policy. In paired evaluations on 60 static and dynamic simulation scenes, FutureRay achieves 93.3% completion, compared with 75.0% for current-range persistence and 80.0% for Cartesian Kalman rollout, with perception, planning, and locomotion held fixed. FutureRay also records fewer collisions than both baselines. Qualitative trials on a physical quadruped show avoidance initiated while an obstacle is approaching the route, followed by renewed goal progress. These results show that joint range and encounter-risk prediction can improve obstacle avoidance without retraining the locomotion policy.