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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
Machine learning
Multi-robot systems
Intelligent robots
Issue Date2020
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000393
Citation
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): Consumer Robots and Our Future, Virtual Conference, Las Vegas, USA, 25 October 2020 - 24 January 2021, p. 5678-5684 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
2023 SCImago Journal Rankings: 1.094
ISI Accession Number ID

 

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 October 2020 - 24 January 2021, p. 5678-5684-
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. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000393-
dc.relation.ispartofIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Proceedings-
dc.subjectCollision Avoidance-
dc.subjectMotion and Path Planning-
dc.subjectMachine learning-
dc.subjectMulti-robot systems-
dc.subjectIntelligent robots-
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.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/IROS45743.2020.9341526-
dc.identifier.scopuseid_2-s2.0-85102395963-
dc.identifier.hkuros312153-
dc.identifier.spage5678-
dc.identifier.epage5684-
dc.identifier.isiWOS:000714033803058-
dc.publisher.placeUnited States-
dc.identifier.issnl2153-0858-

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