No Cover Image

Journal article 486 views 73 downloads

Model-based recurrent neural network for redundancy resolution of manipulator with remote centre of motion constraints

Zhan Li, Shuai Li Orcid Logo

International Journal of Systems Science, Volume: 53, Issue: 14, Pages: 1 - 14

Swansea University Authors: Zhan Li, Shuai Li Orcid Logo

  • 60160.pdf

    PDF | Version of Record

    © 2022 The Author(s).This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License

    Download (2.68MB)

Check full text

DOI (Published version): 10.1080/00207721.2022.2070790

Abstract

Redundancy resolution is a critical issue to achieve accurate kinematic control for manipulators. End-effectors of manipulators can track desired paths well with suitable resolved joint variables. In some manipulation applications such as selecting insertion paths to thrill through a set of points,...

Full description

Published in: International Journal of Systems Science
ISSN: 0020-7721 1464-5319
Published: Informa UK Limited 2022
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa60160
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2022-06-08T14:27:17Z
last_indexed 2023-01-11T14:41:56Z
id cronfa60160
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2023-01-04T11:13:02.3062864</datestamp><bib-version>v2</bib-version><id>60160</id><entry>2022-06-08</entry><title>Model-based recurrent neural network for redundancy resolution of manipulator with remote centre of motion constraints</title><swanseaauthors><author><sid>94f19a09e17bad497ef1b4a0992c1d56</sid><firstname>Zhan</firstname><surname>Li</surname><name>Zhan Li</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>42ff9eed09bcd109fbbe484a0f99a8a8</sid><ORCID>0000-0001-8316-5289</ORCID><firstname>Shuai</firstname><surname>Li</surname><name>Shuai Li</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2022-06-08</date><deptcode>SCS</deptcode><abstract>Redundancy resolution is a critical issue to achieve accurate kinematic control for manipulators. End-effectors of manipulators can track desired paths well with suitable resolved joint variables. In some manipulation applications such as selecting insertion paths to thrill through a set of points, it requires the distal link of a manipulator to translate along such fixed point and then perform manipulation tasks. The point is known as remote centre of motion (RCM) to constrain motion planning and kinematic control of manipulators. Together with its end-effector finishing path tracking tasks, the redundancy resolution of a manipulators has to maintain RCM to produce reliable resolved joint angles. However, current existing redundancy resolution schemes on manipulators based on recurrent neural networks (RNNs) mainly are focusing on unrestricted motion without RCM constraints considered. In this paper, an RNN-based approach is proposed to solve the redundancy resolution issue with RCM constraints, developing a new general dynamic optimisation formulation containing the RCM constraints. Theoretical analysis shows the theoretical derivation and convergence of the proposed RNN for redundancy resolution of manipulators with RCM constraints. Simulation results further demonstrate the efficiency of the proposed method in end-effector path tracking control under RCM constraints based on an industrial redundant manipulator system.</abstract><type>Journal Article</type><journal>International Journal of Systems Science</journal><volume>53</volume><journalNumber>14</journalNumber><paginationStart>1</paginationStart><paginationEnd>14</paginationEnd><publisher>Informa UK Limited</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0020-7721</issnPrint><issnElectronic>1464-5319</issnElectronic><keywords>Redundant; motion planning; kinematics</keywords><publishedDay>31</publishedDay><publishedMonth>5</publishedMonth><publishedYear>2022</publishedYear><publishedDate>2022-05-31</publishedDate><doi>10.1080/00207721.2022.2070790</doi><url/><notes>The codes and data that support the this study are available on request from the corresponding author Zhan Li. The data are not publicly available due to copyright restrictions by the authors on potential applications in robotics.</notes><college>COLLEGE NANME</college><department>Computer Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>SCS</DepartmentCode><institution>Swansea University</institution><apcterm>SU Library paid the OA fee (TA Institutional Deal)</apcterm><funders/><projectreference/><lastEdited>2023-01-04T11:13:02.3062864</lastEdited><Created>2022-06-08T15:25:07.7757513</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Zhan</firstname><surname>Li</surname><order>1</order></author><author><firstname>Shuai</firstname><surname>Li</surname><orcid>0000-0001-8316-5289</orcid><order>2</order></author></authors><documents><document><filename>60160__24256__939aa0fe37e342eb9f4e637297ca284b.pdf</filename><originalFilename>60160.pdf</originalFilename><uploaded>2022-06-08T15:28:06.7288059</uploaded><type>Output</type><contentLength>2811811</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9; 2022 The Author(s).This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by-nc-nd/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2023-01-04T11:13:02.3062864 v2 60160 2022-06-08 Model-based recurrent neural network for redundancy resolution of manipulator with remote centre of motion constraints 94f19a09e17bad497ef1b4a0992c1d56 Zhan Li Zhan Li true false 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2022-06-08 SCS Redundancy resolution is a critical issue to achieve accurate kinematic control for manipulators. End-effectors of manipulators can track desired paths well with suitable resolved joint variables. In some manipulation applications such as selecting insertion paths to thrill through a set of points, it requires the distal link of a manipulator to translate along such fixed point and then perform manipulation tasks. The point is known as remote centre of motion (RCM) to constrain motion planning and kinematic control of manipulators. Together with its end-effector finishing path tracking tasks, the redundancy resolution of a manipulators has to maintain RCM to produce reliable resolved joint angles. However, current existing redundancy resolution schemes on manipulators based on recurrent neural networks (RNNs) mainly are focusing on unrestricted motion without RCM constraints considered. In this paper, an RNN-based approach is proposed to solve the redundancy resolution issue with RCM constraints, developing a new general dynamic optimisation formulation containing the RCM constraints. Theoretical analysis shows the theoretical derivation and convergence of the proposed RNN for redundancy resolution of manipulators with RCM constraints. Simulation results further demonstrate the efficiency of the proposed method in end-effector path tracking control under RCM constraints based on an industrial redundant manipulator system. Journal Article International Journal of Systems Science 53 14 1 14 Informa UK Limited 0020-7721 1464-5319 Redundant; motion planning; kinematics 31 5 2022 2022-05-31 10.1080/00207721.2022.2070790 The codes and data that support the this study are available on request from the corresponding author Zhan Li. The data are not publicly available due to copyright restrictions by the authors on potential applications in robotics. COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University SU Library paid the OA fee (TA Institutional Deal) 2023-01-04T11:13:02.3062864 2022-06-08T15:25:07.7757513 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Zhan Li 1 Shuai Li 0000-0001-8316-5289 2 60160__24256__939aa0fe37e342eb9f4e637297ca284b.pdf 60160.pdf 2022-06-08T15:28:06.7288059 Output 2811811 application/pdf Version of Record true © 2022 The Author(s).This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title Model-based recurrent neural network for redundancy resolution of manipulator with remote centre of motion constraints
spellingShingle Model-based recurrent neural network for redundancy resolution of manipulator with remote centre of motion constraints
Zhan Li
Shuai Li
title_short Model-based recurrent neural network for redundancy resolution of manipulator with remote centre of motion constraints
title_full Model-based recurrent neural network for redundancy resolution of manipulator with remote centre of motion constraints
title_fullStr Model-based recurrent neural network for redundancy resolution of manipulator with remote centre of motion constraints
title_full_unstemmed Model-based recurrent neural network for redundancy resolution of manipulator with remote centre of motion constraints
title_sort Model-based recurrent neural network for redundancy resolution of manipulator with remote centre of motion constraints
author_id_str_mv 94f19a09e17bad497ef1b4a0992c1d56
42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 94f19a09e17bad497ef1b4a0992c1d56_***_Zhan Li
42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Zhan Li
Shuai Li
author2 Zhan Li
Shuai Li
format Journal article
container_title International Journal of Systems Science
container_volume 53
container_issue 14
container_start_page 1
publishDate 2022
institution Swansea University
issn 0020-7721
1464-5319
doi_str_mv 10.1080/00207721.2022.2070790
publisher Informa UK Limited
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 Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description Redundancy resolution is a critical issue to achieve accurate kinematic control for manipulators. End-effectors of manipulators can track desired paths well with suitable resolved joint variables. In some manipulation applications such as selecting insertion paths to thrill through a set of points, it requires the distal link of a manipulator to translate along such fixed point and then perform manipulation tasks. The point is known as remote centre of motion (RCM) to constrain motion planning and kinematic control of manipulators. Together with its end-effector finishing path tracking tasks, the redundancy resolution of a manipulators has to maintain RCM to produce reliable resolved joint angles. However, current existing redundancy resolution schemes on manipulators based on recurrent neural networks (RNNs) mainly are focusing on unrestricted motion without RCM constraints considered. In this paper, an RNN-based approach is proposed to solve the redundancy resolution issue with RCM constraints, developing a new general dynamic optimisation formulation containing the RCM constraints. Theoretical analysis shows the theoretical derivation and convergence of the proposed RNN for redundancy resolution of manipulators with RCM constraints. Simulation results further demonstrate the efficiency of the proposed method in end-effector path tracking control under RCM constraints based on an industrial redundant manipulator system.
published_date 2022-05-31T04:18:02Z
_version_ 1763754201886752768
score 11.012678

Similar Items