A Configuration-Space Decomposition Scheme for Learning-based Collision Checking
This addresses motion planning challenges in robotics, particularly for high-DOF systems, with incremental improvements over existing learning-based methods.
The paper tackles the problem of motion planning for high-DOF robots by proposing a configuration-space decomposition method that builds a composite classifier, which outperforms state-of-the-art single classifier methods by a large margin in experiments.
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.