Cooperative Kinematic Control for Multiple Redundant Manipulators Under Partially Known Information Using Recurrent Neural Network

Li, Xiaoxiao; Xu, Zhihao; Li, Shuai; Wu, Hongmin; Zhou, Xuefeng

doi:10.1109/access.2020.2974248

Journal article 737 views 120 downloads

Cooperative Kinematic Control for Multiple Redundant Manipulators Under Partially Known Information Using Recurrent Neural Network

Xiaoxiao Li, Zhihao Xu, Shuai Li

, Hongmin Wu, Xuefeng Zhou

IEEE Access, Volume: 8, Pages: 40029 - 40038

Swansea University Author: Shuai Li

PDF | Version of Record

Released under the terms of a Creative Commons Attribution 4.0 License (CC-BY).
Download (3.76MB)

Check full text

DOI (Published version): 10.1109/access.2020.2974248

Abstract

In this study, we investigate the problem of cooperative kinematic control for multiple redundant manipulators under partially known information using recurrent neural network (RNN). The communication among manipulators is modeled as a graph topology network with the information exchange that only o...

Full description

Published in:	IEEE Access
ISSN:	2169-3536
Published:	Institute of Electrical and Electronics Engineers (IEEE) 2020
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa53865
Tags:	Add Tag No Tags, Be the first to tag this record!

first_indexed	2020-03-25T13:30:49Z
last_indexed	2020-10-23T03:06:15Z
id	cronfa53865
recordtype	SURis
fullrecord	<?xml version="1.0"?><rfc1807><datestamp>2020-10-22T16:08:13.1897925</datestamp><bib-version>v2</bib-version><id>53865</id><entry>2020-03-25</entry><title>Cooperative Kinematic Control for Multiple Redundant Manipulators Under Partially Known Information Using Recurrent Neural Network</title><swanseaauthors><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>2020-03-25</date><deptcode>MECH</deptcode><abstract>In this study, we investigate the problem of cooperative kinematic control for multiple redundant manipulators under partially known information using recurrent neural network (RNN). The communication among manipulators is modeled as a graph topology network with the information exchange that only occurs at the neighbouring robot nodes. Under partially known information, four objectives are simultaneously achieved, i.e, global cooperation and synchronization among manipulators, joint physical limits compliance, neighbor-to-neighbor communication among robots, and optimality of cost function. We develop a velocity observer for each individual manipulator to help them to obtain the desired motion velocity information. Moreover, a negative feedback term is introduced with a higher tracking precision. Minimizing the joint velocity norm as cost function, the considered cooperative kinematic control is built as a quadratic programming (QP) optimization problem integrating with both joint angle and joint speed limitations, and is solved online by constructing a dynamic RNN. Moreover, global convergence of the developed velocity observer, RNN controller and cooperative tracking error are theoretically derived. Finally, under a fixed and variable communication topology, respectively, application in using a group of iiwa R800 redundant manipulators to transport a payload and comparison with the existing method are conducted. Among the simulative results, the robot group synchronously achieves the desired circle and rhodonea trajectory tracking, with higher tracking precision reaching to zero. When joint angles and joint velocities tend to exceed the setting constraints, respectively, they are constrained into the upper and lower bounds owing to the designed RNN controller.</abstract><type>Journal Article</type><journal>IEEE Access</journal><volume>8</volume><paginationStart>40029</paginationStart><paginationEnd>40038</paginationEnd><publisher>Institute of Electrical and Electronics Engineers (IEEE)</publisher><issnElectronic>2169-3536</issnElectronic><keywords>Velocity observer, multiple redundant manipulators, recurrent neural network, motion planning, zeroing neural network</keywords><publishedDay>17</publishedDay><publishedMonth>2</publishedMonth><publishedYear>2020</publishedYear><publishedDate>2020-02-17</publishedDate><doi>10.1109/access.2020.2974248</doi><url/><notes/><college>COLLEGE NANME</college><department>Mechanical Engineering</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MECH</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2020-10-22T16:08:13.1897925</lastEdited><Created>2020-03-25T11:50:29.6493560</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering</level></path><authors><author><firstname>Xiaoxiao</firstname><surname>Li</surname><order>1</order></author><author><firstname>Zhihao</firstname><surname>Xu</surname><order>2</order></author><author><firstname>Shuai</firstname><surname>Li</surname><orcid>0000-0001-8316-5289</orcid><order>3</order></author><author><firstname>Hongmin</firstname><surname>Wu</surname><order>4</order></author><author><firstname>Xuefeng</firstname><surname>Zhou</surname><order>5</order></author></authors><documents><document><filename>53865__16913__e7a5bedbab944bbb98c10fc281fe2362.pdf</filename><originalFilename>53865.pdf</originalFilename><uploaded>2020-03-25T11:53:56.8295586</uploaded><type>Output</type><contentLength>3946981</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><embargoDate>2020-03-25T00:00:00.0000000</embargoDate><documentNotes>Released under the terms of a Creative Commons Attribution 4.0 License (CC-BY).</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling	2020-10-22T16:08:13.1897925 v2 53865 2020-03-25 Cooperative Kinematic Control for Multiple Redundant Manipulators Under Partially Known Information Using Recurrent Neural Network 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2020-03-25 MECH In this study, we investigate the problem of cooperative kinematic control for multiple redundant manipulators under partially known information using recurrent neural network (RNN). The communication among manipulators is modeled as a graph topology network with the information exchange that only occurs at the neighbouring robot nodes. Under partially known information, four objectives are simultaneously achieved, i.e, global cooperation and synchronization among manipulators, joint physical limits compliance, neighbor-to-neighbor communication among robots, and optimality of cost function. We develop a velocity observer for each individual manipulator to help them to obtain the desired motion velocity information. Moreover, a negative feedback term is introduced with a higher tracking precision. Minimizing the joint velocity norm as cost function, the considered cooperative kinematic control is built as a quadratic programming (QP) optimization problem integrating with both joint angle and joint speed limitations, and is solved online by constructing a dynamic RNN. Moreover, global convergence of the developed velocity observer, RNN controller and cooperative tracking error are theoretically derived. Finally, under a fixed and variable communication topology, respectively, application in using a group of iiwa R800 redundant manipulators to transport a payload and comparison with the existing method are conducted. Among the simulative results, the robot group synchronously achieves the desired circle and rhodonea trajectory tracking, with higher tracking precision reaching to zero. When joint angles and joint velocities tend to exceed the setting constraints, respectively, they are constrained into the upper and lower bounds owing to the designed RNN controller. Journal Article IEEE Access 8 40029 40038 Institute of Electrical and Electronics Engineers (IEEE) 2169-3536 Velocity observer, multiple redundant manipulators, recurrent neural network, motion planning, zeroing neural network 17 2 2020 2020-02-17 10.1109/access.2020.2974248 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2020-10-22T16:08:13.1897925 2020-03-25T11:50:29.6493560 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Xiaoxiao Li 1 Zhihao Xu 2 Shuai Li 0000-0001-8316-5289 3 Hongmin Wu 4 Xuefeng Zhou 5 53865__16913__e7a5bedbab944bbb98c10fc281fe2362.pdf 53865.pdf 2020-03-25T11:53:56.8295586 Output 3946981 application/pdf Version of Record true 2020-03-25T00:00:00.0000000 Released under the terms of a Creative Commons Attribution 4.0 License (CC-BY). true eng http://creativecommons.org/licenses/by/4.0/
title	Cooperative Kinematic Control for Multiple Redundant Manipulators Under Partially Known Information Using Recurrent Neural Network
spellingShingle	Cooperative Kinematic Control for Multiple Redundant Manipulators Under Partially Known Information Using Recurrent Neural Network Shuai Li
title_short	Cooperative Kinematic Control for Multiple Redundant Manipulators Under Partially Known Information Using Recurrent Neural Network
title_full	Cooperative Kinematic Control for Multiple Redundant Manipulators Under Partially Known Information Using Recurrent Neural Network
title_fullStr	Cooperative Kinematic Control for Multiple Redundant Manipulators Under Partially Known Information Using Recurrent Neural Network
title_full_unstemmed	Cooperative Kinematic Control for Multiple Redundant Manipulators Under Partially Known Information Using Recurrent Neural Network
title_sort	Cooperative Kinematic Control for Multiple Redundant Manipulators Under Partially Known Information Using Recurrent Neural Network
author_id_str_mv	42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv	42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author	Shuai Li
author2	Xiaoxiao Li Zhihao Xu Shuai Li Hongmin Wu Xuefeng Zhou
format	Journal article
container_title	IEEE Access
container_volume	8
container_start_page	40029
publishDate	2020
institution	Swansea University
issn	2169-3536
doi_str_mv	10.1109/access.2020.2974248
publisher	Institute of Electrical and Electronics Engineers (IEEE)
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 Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str	1
active_str	0
description	In this study, we investigate the problem of cooperative kinematic control for multiple redundant manipulators under partially known information using recurrent neural network (RNN). The communication among manipulators is modeled as a graph topology network with the information exchange that only occurs at the neighbouring robot nodes. Under partially known information, four objectives are simultaneously achieved, i.e, global cooperation and synchronization among manipulators, joint physical limits compliance, neighbor-to-neighbor communication among robots, and optimality of cost function. We develop a velocity observer for each individual manipulator to help them to obtain the desired motion velocity information. Moreover, a negative feedback term is introduced with a higher tracking precision. Minimizing the joint velocity norm as cost function, the considered cooperative kinematic control is built as a quadratic programming (QP) optimization problem integrating with both joint angle and joint speed limitations, and is solved online by constructing a dynamic RNN. Moreover, global convergence of the developed velocity observer, RNN controller and cooperative tracking error are theoretically derived. Finally, under a fixed and variable communication topology, respectively, application in using a group of iiwa R800 redundant manipulators to transport a payload and comparison with the existing method are conducted. Among the simulative results, the robot group synchronously achieves the desired circle and rhodonea trajectory tracking, with higher tracking precision reaching to zero. When joint angles and joint velocities tend to exceed the setting constraints, respectively, they are constrained into the upper and lower bounds owing to the designed RNN controller.
published_date	2020-02-17T04:07:03Z
_version_	1763753510963249152
score	11.012678

Cooperative Kinematic Control for Multiple Redundant Manipulators Under Partially Known Information Using Recurrent Neural Network

Similar Items