Modern sampling-based motion planning algorithms typically take between hundreds of milliseconds to dozens of seconds to find collision-free motions for high degree-of-freedom problems. This paper presents performance improvements of more than 500x over the state-of-the-art, bringing planning times into the range of microseconds and solution rates into the range of kilohertz, without specialized hardware. Our key insight is how to exploit fine-grained parallelism within sampling-based planners, providing generality-preserving algorithmic improvements to any such planner and significantly accelerating critical subroutines, such as forward kinematics and collision checking. We demonstrate our approach over a diverse set of challenging, realistic problems for complex robots ranging from 7 to 14 degrees-of-freedom. Moreover, we show that our approach does not require high-power hardware by also evaluating on a low-power single-board computer. The planning speeds demonstrated are fast enough to reside in the range of control frequencies and open up new avenues of motion planning research.

翻译：现代基于采样的运动规划算法通常需要数百毫秒至数十秒才能为高自由度问题找到无碰撞运动。本文提出了一种超过当前最优方法500倍以上的性能提升，将规划时间缩短至微秒级，求解频率提升至千赫兹级，且无需专用硬件。我们的核心洞察在于如何利用基于采样规划器内部的细粒度并行性，为任何此类规划器提供保持通用性的算法改进，并显著加速正向运动学和碰撞检测等关键子程序。我们在从7到14自由度的复杂机器人所面临的多样化、具有挑战性的实际问题上验证了该方法。此外，通过在低功耗单板计算机上的评估，我们证明该方法无需高功耗硬件。所展示的规划速度足以媲美控制频率范围，为运动规划研究开辟了新途径。