A Configuration-Space Decomposition Scheme for Learning-based Collision Checking

A Configuration-Space Decomposition Scheme for Learning-based Collision Checking Yiheng Han 1, Wang Zhao 1, Jia Pan 2, Zipeng Y e 1, Ran Yi 1 and Y ong-Jin Liu 1† Abstract -- 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. I. INTRODUCTION Motion planning plays an important role in robotics, which finds a collision-free path to move a robot from a source to a target position.