  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.
