用Python实现高速可扩展的最优运动规划，支持复杂系统实时求解。
Fast Asymptotically Optimal Kinodynamic Planning via Vectorization
- 基于JAX和XLA构建并行化RRT算法，无需特殊编程即可在GPU加速
- 结合AO-x元算法实现快速重规划，收敛速度比现有方法快2倍以上
- 适合需要高精度路径规划的机器人、自动驾驶等实时场景
基于采样的运动规划方法在具有复杂动力学约束和高维状态空间的系统中表现优异。然而，这类算法难以实现实时性能，促使研究者尝试并行化规划。尽管已有基于GPU的加速方案取得显著提速，但多数依赖专用CUDA编程，限制了可访问性和可移植性。本文提出并行渐近最优动力学RRT（PAKR），利用JAX与XLA编译器，通过标准Python工具实现GPU加速。结合AO-x元算法，通过快速迭代重规划实现渐近最优性。我们对概率完备性进行了理论分析，研究了批处理大小和分支因子对收敛的影响，并在MuJoCo-XLA模拟器中验证了其在复杂动力学系统下的可扩展性。实验表明，PAKR在运行时间上优于或媲美当前最先进的GPU规划器，同时解决方案质量更优。
原文摘要 · Abstract (English)
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.
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