メッセージパッシングモンテカルロによるサンプリングベース運動計画の効率改善
Improving Efficiency of Sampling-based Motion Planning via Message-Passing Monte Carlo
グラフニューラルネットワークで低不一致な点集合を生成するMPMCをサンプリングベース運動計画に導入し、探索効率を向上させる手法を提案した。
著者: Makram Chahine, T. Konstantin Rusch, Zach J. Patterson, Daniela Rus
分類: cs.RO
原文アブストラクト
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 $\cL_p$-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.
