This paper presents a sampling-based motion planning framework that leverages the geometry of obstacles in a workspace as well as prior experiences from motion planning problems. Previous studies have demonstrated the benefits of utilizing prior solutions to motion planning problems for improving planning efficiency. However, particularly for high-dimensional systems, achieving high performance across randomized environments remains a technical challenge for experience-based approaches due to the substantial variance between each query. To address this challenge, we propose a novel approach that involves decoupling the problem into subproblems through algorithmic workspace decomposition and graph search. Additionally, we capitalize on prior experience within each subproblem. This approach effectively reduces the variance across different problems, leading to improved performance for experience-based planners. To validate the effectiveness of our framework, we conduct experiments using 2D and 6D robotic systems. The experimental results demonstrate that our framework outperforms existing algorithms in terms of planning time and cost.

翻译：本文提出了一种基于采样的运动规划框架，该框架利用了工作空间中障碍物的几何特性以及来自运动规划问题的先验经验。以往的研究已证明利用运动规划问题的先验解能提高规划效率。然而，对于高维系统而言，由于每次查询之间存在显著差异，在随机环境中实现高性能仍然是基于经验的方法面临的技术挑战。为解决这一挑战，我们提出了一种新方法，通过算法化工作空间分解和图搜索将问题分解为子问题，并在每个子问题中利用先验经验。该方法有效降低了不同问题之间的差异，从而提升了基于经验的规划器的性能。为验证框架的有效性，我们使用2D和6D机器人系统进行了实验。实验结果表明，我们的框架在规划时间和成本方面优于现有算法。