CASSR实现毫秒级双足机器人步态规划，突破传统方法速度瓶颈。
CASSR: Continuous A-Star Search through Reachability for real time footstep planning
- 在A*搜索中递归传播连续可计算的运动学约束
- 30步规划耗时低于125毫秒，比传统方法快100倍
- 适合需要实时步态规划的双足机器人系统
足部规划面临复杂的组合搜索挑战。传统A*方法需离散化可达性约束，而混合整数规划（MIP）虽支持连续形式，但包含旋转时迅速变得不可行。本文提出CASSR框架，在A*搜索中递归传播机器人运动学约束的凸连续形式。结合基于EPA算法的新启发式估价函数，CASSR可在125毫秒内完成最多30步的接触序列规划。双足行走任务实验表明，其性能比传统离散A*快达100倍，同时优于商用MIP求解器。结果证明CASSR实现了快速、可靠且实时的双足机器人足部规划。
原文摘要 · Abstract (English)
Footstep planning involves a challenging combinatorial search. Traditional A* approaches require discretising reachability constraints, while Mixed-Integer Programming (MIP) supports continuous formulations but quickly becomes intractable, especially when rotations are included. We present CASSR, a novel framework that recursively propagates convex, continuous formulations of a robot's kinematic constraints within an A* search. Combined with a new cost-to-go heuristic based on the EPA algorithm, CASSR efficiently plans contact sequences of up to 30 footsteps in under 125 ms. Experiments on biped locomotion tasks demonstrate that CASSR outperforms traditional discretised A* by up to a factor of 100, while also surpassing a commercial MIP solver. These results show that CASSR enables fast, reliable, and real-time footstep planning for biped robots.
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