学習ベース衝突判定のための配置空間分解スキーム
A Configuration-Space Decomposition Scheme for Learning-based Collision Checking
高自由度ロボットの衝突判定を効率化するため、配置空間を分解して複合分類器を構築し、従来の単一分類器を大きく上回る性能を示した論文。
著者: Yiheng Han, Wang Zhao, Jia Pan, Zipeng Ye, Ran Yi, Yong-Jin Liu
分類: cs.RO, cs.LG, stat.ML
原文アブストラクト
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.
