外观
论文arXiv 2609.321581 个月前
FutureRay：面向敏捷四足导航的控制对齐未来距离
原文链接：FutureRay: Control-Aligned Future Range for Agile Quadruped Navigation（arXiv 2609.32158 · 发布日期：2026-09-26）
FutureRay: Control-Aligned Future Range for Agile Quadruped Navigation
FutureRay预测未来方向距离和遭遇风险，结合局部规划，在动静态仿真场景和实体四足定性试验中验证避障。
核心要点
- 动障碍可能在四足机器人执行运动命令时阻断原本畅通的路径。
- FutureRay由深度距离历史和可观测机器人运动预测多方向未来距离与遭遇风险。
- 局部规划器用预测检查机器人足迹周围净空，并以反应—制动限制指导速度选择。
- 在60个动静态仿真场景中完成率93.3%，碰撞少于两个基线；真机试验展示提前避障。
验证范围与局限：验证包括60个静态和动态仿真场景的配对评估，以及实体四足机器人的定性试验。
英文摘要 (Abstract)
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.
作者：Tianhao Zang, Shanze Wang, Ziqian Wang, Liyou Luo, Zihan Liu, Xingjian Xie, Wei Zhang
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