Motion planning for robots of high degrees-of-freedom (DOFs) is an important problem in robotics with sampling-based methods in configuration space C as one popular solution. Recently, machine learning methods have been introduced into sampling-based motion planning methods, which train a classifier to distinguish collision free subspace from in-collision subspace in C. In this paper, we propose a novel configuration space decomposition method and show two nice properties resulted from this decomposition. Using these two properties, we build a composite classifier that works compatibly with previous machine learning methods by using them as the elementary classifiers. Experimental results are presented, showing that our composite classifier outperforms state-of-the-art single classifier methods by a large margin. A real application of motion planning in a multi-robot system in plant phenotyping using three UR5 robotic arms is also presented.
@article{arxiv.1911.08581,
title = {A Configuration-Space Decomposition Scheme for Learning-based Collision Checking},
author = {Yiheng Han and Wang Zhao and Jia Pan and Zipeng Ye and Ran Yi and Yong-Jin Liu},
journal= {arXiv preprint arXiv:1911.08581},
year = {2019}
}