发表机构
Department of Computer Science, Purdue University（普渡大学计算机科学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究复杂运动动力学系统的规划问题，提出基于JAX和XLA编译器的并行渐近最优运动动力学RRT规划器，结合AO - x元算法实现渐近最优，经理论分析和实验验证其性能优势。
AI 中文摘要
基于采样的运动规划器对复杂运动动力学约束和高维系统有效，但难实时运行，现有GPU加速规划器有局限。我们提出并行渐近最优运动动力学RRT（PAKR），利用JAX和XLA编译器通过标准Python工具实现GPU加速，结合AO - x元算法经快速迭代重规划实现渐近最优，给出相关分析并验证性能。
英文摘要
Sampling-based motion planners have been shown to be effective for systems with complex kinodynamic constraints and high dimensionality. However, these algorithms struggle to achieve real-time performance, leading to recent efforts to parallelize planning. While GPU-accelerated planners have achieved significant speedups, existing approaches require specialized CUDA programming that limits accessibility and portability. We present Parallel Asymptotically Optimal Kinodynamic RRT (PAKR), a massively parallel kinodynamic planner leveraging JAX and the XLA compiler to achieve GPU acceleration through standard Python tooling. By combining our parallel planner with the AO-x meta-algorithm, we achieve asymptotic optimality through fast iterative replanning. We provide a theoretical analysis of probabilistic completeness, analyze the effects of batch size and branching factor on convergence, and demonstrate scalability to complex dynamics using the MuJoCo-XLA simulator. Experiments show competitive runtimes with state-of-the-art GPU planners and superior solution quality.
Comments8 pages, 5 figures, 4 tables. Accepted to IROS 2026
