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Conference Paper: Configuration Space Decomposition For Learning-based Collision Checking In High-dof Robots

TitleConfiguration Space Decomposition For Learning-based Collision Checking In High-dof Robots
Authors
KeywordsCollision Avoidance
Motion and Path Planning
Issue Date2020
PublisherInstitute of Electrical and Electronics Engineers.
Citation
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): Consumer Robots and Our Future, Virtual Conference, Las Vegas, USA, 25-29 October 2020 How to Cite?
AbstractMotion planning for robots of high degrees-offreedom (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.
DescriptionTuBT12 Collision Avoidance I - Paper TuBT12.3
Persistent Identifierhttp://hdl.handle.net/10722/285031
ISSN

 

DC FieldValueLanguage
dc.contributor.authorHAN, Y-
dc.contributor.authorZHAO, W-
dc.contributor.authorPan, J-
dc.contributor.authorLIU, YJ-
dc.date.accessioned2020-08-07T09:05:50Z-
dc.date.available2020-08-07T09:05:50Z-
dc.date.issued2020-
dc.identifier.citationIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): Consumer Robots and Our Future, Virtual Conference, Las Vegas, USA, 25-29 October 2020-
dc.identifier.issn2153-0858-
dc.identifier.urihttp://hdl.handle.net/10722/285031-
dc.descriptionTuBT12 Collision Avoidance I - Paper TuBT12.3-
dc.description.abstractMotion planning for robots of high degrees-offreedom (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.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers.-
dc.relation.ispartofIEEE/RSJ International Conference on Intelligent Robots and Systems Proceedings-
dc.rightsIEEE/RSJ International Conference on Intelligent Robots and Systems Proceedings. Copyright © Institute of Electrical and Electronics Engineers.-
dc.subjectCollision Avoidance-
dc.subjectMotion and Path Planning-
dc.titleConfiguration Space Decomposition For Learning-based Collision Checking In High-dof Robots-
dc.typeConference_Paper-
dc.identifier.emailPan, J: jpan@cs.hku.hk-
dc.identifier.authorityPan, J=rp01984-
dc.identifier.hkuros312153-
dc.publisher.placeUnited States-

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