FutureRay：面向敏捷四足导航的控制对齐未来范围

中文摘要 AI 辅助

移动障碍物可能在四足机器人执行运动指令时阻塞先前畅通的路径。我们研究当运动选择考虑机器人足迹以及反应和制动所需时间时，预测变化的净空是否能够改善导航。我们提出FutureRay，它从深度导出的范围历史和可观测的机器人运动中，预测跨视角方向和未来时间的范围，以及遭遇风险。训练强调近期净空，并对夸大可用空间的错误进行惩罚。局部规划器查询相同的预测以获取候选航向，并将其与当前观测相结合，以检查机器人足迹周围的净空。基于模型的反应-制动限制指导速度选择，所得速度指令传递给固定的运动策略。在60个静态和动态模拟场景的配对评估中，FutureRay实现了93.3%的完成率，而当前范围持久性为75.0%，笛卡尔卡尔曼滚动为80.0%，感知、规划与运动均保持固定。FutureRay记录的碰撞次数也少于两个基线。在实体四足机器人上的定性试验显示，当障碍物接近路径时即启动避让，随后恢复目标进展。这些结果表明，联合范围与遭遇风险预测可以在不重新训练运动策略的情况下改善障碍物避让。

英文摘要

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.

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8 pages