ST-pRRTC：具有自适应目标时间森林的并行时空RRT-C

中文摘要 AI 辅助

我们提出ST-pRRTC，一种GPU并行的时空RRT-Connect运动规划器，用于处理具有已知障碍物轨迹和未指定到达时间的问题。在多个到达时间上进行搜索拓宽了时间覆盖范围，但将有限的规划预算分配给更多的后向树。为解决这一挑战，ST-pRRTC构建了一个共享的前向树和一个自适应的后向目标时间树森林。其区间根公式对目标到达时间进行连续采样，并在有界时间域内在所述假设下保证概率完备性和渐近到达时间最优性。实用的根回收策略没有这样的保证。它调整固定数量的后向树，在保留有用搜索进展的同时替换较晚的根。在三个动态基准上的实验表明，在所有比较方法都能解决的问题上，两种变体都比ST-RRT*和SI-RRT实现了更低的平均首次解时间和更早的平均最终到达时间。进一步的实验证明了在广泛的到达时间范围内回收的益处。真实机器人演示展示了根回收ST-pRRTC在移动的Crazyflie四旋翼中为UR5e规划运动。

英文摘要

We propose ST-pRRTC, a GPU-parallel space- time RRT-Connect motion planner for problems with known obstacle trajectories and unspecified arrival time. Searching over many arrival times broadens temporal coverage but divides a finite planning budget among more backward trees. To address the challenge, ST-pRRTC builds a shared forward tree and an adaptive forest of backward goal-time trees. Its interval root formulation samples goal arrival times continuously and guarantees probabilistic completeness and asymptotic arrival- time optimality under the stated assumptions in a bounded time domain. The practical root recycling policy has no such guar- antees. It adapts a fixed number of backward trees, replacing later roots while retaining useful search progress. Experiments on three dynamic benchmarks show that both variants achieve lower mean first-solution times and earlier mean final arrivals than ST-RRT* and SI-RRT on problems solved by all compared methods. Further experiments demonstrate the benefit of recy- cling over broad arrival-time ranges. Real-robot demonstrations show root-recycling ST-pRRTC planning motions for a UR5e among moving Crazyflie quadrotors.

发表机构

• Rutgers, the State University of New Jersey（罗格斯，新泽西州立大学）

机构由 AI 辅助整理，请以论文原文为准。