外观
论文arXiv 2610.1319519 天前
PATACON：面向约束运动规划的GPU并行切丛RRT-Connect
原文链接：PATACON: GPU-Parallel Tangent-Bundle RRT-Connect for Constrained Motion Planning（arXiv 2610.13195 · 发布日期：2026-10-09）
PATACON: GPU-Parallel Tangent-Bundle RRT-Connect for Constrained Motion Planning
PATACON针对约束全身运动规划开销高，采用GPU并行切空间双向规划，并在双臂闭链和人形全身约束任务上验证。
核心要点
- 快速全身运动规划需满足避碰与运动学约束，但约束投影带来较大计算开销。
- PATACON利用已知约束函数诱导的局部流形几何，在切空间中进行GPU并行双向探索。
- 在双臂闭链和人形全身约束规划任务中，维度和约束余维增长时优于先前工作。
- 实验报告最高达到11.25倍加速，表明切空间并行探索有助于接近实时性能。
验证范围与局限：验证范围为双臂闭链和人形全身约束规划任务，摘要未说明仿真或真机。
英文摘要 (Abstract)
Fast whole-body motion planning enables continuous replanning for humanoid robots in dynamic environments while satisfying collision avoidance and kinematic constraints. However, constrained motion planning incurs substantial computational cost due to repeated constraint projection in addition to collision checking and nearest-neighbor search. Recently, GPU-based massively parallel planners have been proposed to reduce computational cost. Nevertheless, these planners still steer in the ambient configuration space, so projecting candidates that deviate from the manifold remains a significant source of overhead, particularly in highly constrained, high-dimensional problems. In this work, we introduce PATACON, a GPU-parallel bidirectional planner that explores tangent space to exploit the local manifold geometry induced by known constraint functions. Experiments on dual-arm closed-chain and humanoid whole-body constrained planning tasks show that PATACON outperforms the prior work as configuration-space dimension and constraint codimension grow, achieving up to an 11.25 times speedup. These results suggest that GPU-parallel tangent-space exploration brings constrained whole-body motion planning closer to real-time performance.
作者：Gahyun Oh, Junho An, Suhyun Jeon, Suhan Park
来源
本页事实对应的公开页面，以原文为准。