Improving Efficiency of Sampling-based Motion Planning via Message-Passing Monte Carlo

Makram Chahine, T. Konstantin Rusch, Zach J Patterson, Daniela Rus

Keywords: Graph Neural Networks, Discrepancy Theory, Motion Planning

TL;DR: This paper introduces a new sampling method for robot motion planning, using graph neural networks to generate more uniform sample distributions, which leads to improved planning efficiency in complex environments.

Abstract: Sampling-based motion planning methods, while effective in high-dimensional spaces, often suffer from inefficiencies due to irregular sampling distributions, leading to suboptimal exploration of the configuration space. In this paper, we propose an approach that enhances the efficiency of these methods by utilizing low-discrepancy distributions generated through Message-Passing Monte Carlo (MPMC). MPMC leverages Graph Neural Networks (GNNs) to generate point sets that uniformly cover the space, with uniformity assessed using the the $\mathcal{L}_p$-discrepancy measure, which quantifies the irregularity of sample distributions. By improving the uniformity of the point sets, our approach significantly reduces computational overhead and the number of samples required for solving motion planning problems. Experimental results demonstrate that our method outperforms traditional sampling techniques in terms of planning efficiency.

Submission Number: 31