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- W4288022773 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." @default.
- W4288022773 created "2022-07-26" @default.
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- W4288022773 date "2019-11-17" @default.
- W4288022773 modified "2023-09-24" @default.
- W4288022773 title "A Configuration-Space Decomposition Scheme for Learning-based Collision Checking" @default.
- W4288022773 hasPublicationYear "2019" @default.
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