No Cover Image

Journal article 1114 views 335 downloads

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

Check full text

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...

Full description

Published in: Multimedia Tools and Applications
ISSN: 1380-7501 1573-7721
Published: 2017
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa27019
first_indexed 2016-04-02T01:02:48Z
last_indexed 2018-02-09T05:09:40Z
id cronfa27019
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2017-04-03T23:29:49.6983510</datestamp><bib-version>v2</bib-version><id>27019</id><entry>2016-04-01</entry><title>Robot manipulator self-identification for surrounding obstacle detection</title><swanseaauthors><author><sid>d2a5024448bfac00a9b3890a8404380b</sid><ORCID/><firstname>Chenguang</firstname><surname>Yang</surname><name>Chenguang Yang</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2016-04-01</date><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 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&#x24C7; sensor and the Baxter&#x24C7; robot have been performed to demonstrate the performance of the proposed algorithms</abstract><type>Journal Article</type><journal>Multimedia Tools and Applications</journal><volume>76</volume><journalNumber>5</journalNumber><paginationStart>6495</paginationStart><paginationEnd>6520</paginationEnd><publisher/><issnPrint>1380-7501</issnPrint><issnElectronic>1573-7721</issnElectronic><keywords/><publishedDay>31</publishedDay><publishedMonth>12</publishedMonth><publishedYear>2017</publishedYear><publishedDate>2017-12-31</publishedDate><doi>10.1007/s11042-016-3275-8</doi><url>http://link.springer.com/article/10.1007/s11042-016-3275-8/fulltext.html</url><notes/><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><degreesponsorsfunders>RCUK</degreesponsorsfunders><apcterm/><lastEdited>2017-04-03T23:29:49.6983510</lastEdited><Created>2016-04-01T15:38:41.1503646</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Engineering and Applied Sciences - Uncategorised</level></path><authors><author><firstname>Xinyu</firstname><surname>Wang</surname><order>1</order></author><author><firstname>Chenguang</firstname><surname>Yang</surname><orcid/><order>2</order></author><author><firstname>Zhaojie</firstname><surname>Ju</surname><order>3</order></author><author><firstname>Hongbin</firstname><surname>Ma</surname><order>4</order></author><author><firstname>Mengyin</firstname><surname>Fu</surname><order>5</order></author></authors><documents><document><filename>0027019-01042016160755.pdf</filename><originalFilename>MTAP16.pdf</originalFilename><uploaded>2016-04-01T16:07:55.8130000</uploaded><type>Output</type><contentLength>4840498</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><embargoDate>2016-04-01T00:00:00.0000000</embargoDate><copyrightCorrect>true</copyrightCorrect></document></documents><OutputDurs/></rfc1807>
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 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 COLLEGE CODE 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 2017-12-31T05:15:20Z
_version_ 1851278250846715904
score 11.089469

Similar Items