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Robot manipulator self-identification for surrounding obstacle detection

Xinyu Wang, Chenguang Yang, Zhaojie Ju, Hongbin Ma, Mengyin Fu

Multimedia Tools and Applications, Volume: 76, Issue: 5, Pages: 6495 - 6520

Swansea University Author: Chenguang Yang

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DOI (Published version): 10.1007/s11042-016-3275-8

Abstract

Obstacle detection plays an important role for robot collision avoidance and motion planning. This paper focuses on the study of the collision prediction of a dual-arm robot based on a 3D point cloud. Firstly, a self-identification method is presented based on the over-segmentation approach and the...

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Published in: Multimedia Tools and Applications
ISSN: 1380-7501 1573-7721
Published: 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa27019
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first_indexed 2016-04-02T01:02:48Z
last_indexed 2018-02-09T05:09:40Z
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spelling 2017-04-03T23:29:49.6983510 v2 27019 2016-04-01 Robot manipulator self-identification for surrounding obstacle detection d2a5024448bfac00a9b3890a8404380b Chenguang Yang Chenguang Yang true false 2016-04-01 EEN Obstacle detection plays an important role for robot collision avoidance and motion planning. This paper focuses on the study of the collision prediction of a dual-arm robot based on a 3D point cloud. Firstly, a self-identification method is presented based on the over-segmentation approach and the forward kinematic model of the robot. Secondly, a simplified 3D model of the robot is generated using the segmented point cloud. Finally, a collision prediction algorithm is proposed to estimate the collision parameters in real-time. Experimental studies using the KinectⓇ sensor and the BaxterⓇ robot have been performed to demonstrate the performance of the proposed algorithms Journal Article Multimedia Tools and Applications 76 5 6495 6520 1380-7501 1573-7721 31 12 2017 2017-12-31 10.1007/s11042-016-3275-8 http://link.springer.com/article/10.1007/s11042-016-3275-8/fulltext.html COLLEGE NANME Engineering COLLEGE CODE EEN Swansea University RCUK 2017-04-03T23:29:49.6983510 2016-04-01T15:38:41.1503646 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Xinyu Wang 1 Chenguang Yang 2 Zhaojie Ju 3 Hongbin Ma 4 Mengyin Fu 5 0027019-01042016160755.pdf MTAP16.pdf 2016-04-01T16:07:55.8130000 Output 4840498 application/pdf Version of Record true 2016-04-01T00:00:00.0000000 true
title Robot manipulator self-identification for surrounding obstacle detection
spellingShingle Robot manipulator self-identification for surrounding obstacle detection
Chenguang Yang
title_short Robot manipulator self-identification for surrounding obstacle detection
title_full Robot manipulator self-identification for surrounding obstacle detection
title_fullStr Robot manipulator self-identification for surrounding obstacle detection
title_full_unstemmed Robot manipulator self-identification for surrounding obstacle detection
title_sort Robot manipulator self-identification for surrounding obstacle detection
author_id_str_mv d2a5024448bfac00a9b3890a8404380b
author_id_fullname_str_mv d2a5024448bfac00a9b3890a8404380b_***_Chenguang Yang
author Chenguang Yang
author2 Xinyu Wang
Chenguang Yang
Zhaojie Ju
Hongbin Ma
Mengyin Fu
format Journal article
container_title Multimedia Tools and Applications
container_volume 76
container_issue 5
container_start_page 6495
publishDate 2017
institution Swansea University
issn 1380-7501
1573-7721
doi_str_mv 10.1007/s11042-016-3275-8
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
url http://link.springer.com/article/10.1007/s11042-016-3275-8/fulltext.html
document_store_str 1
active_str 0
description Obstacle detection plays an important role for robot collision avoidance and motion planning. This paper focuses on the study of the collision prediction of a dual-arm robot based on a 3D point cloud. Firstly, a self-identification method is presented based on the over-segmentation approach and the forward kinematic model of the robot. Secondly, a simplified 3D model of the robot is generated using the segmented point cloud. Finally, a collision prediction algorithm is proposed to estimate the collision parameters in real-time. Experimental studies using the KinectⓇ sensor and the BaxterⓇ robot have been performed to demonstrate the performance of the proposed algorithms
published_date 2017-12-31T03:32:39Z
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score 11.036706

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