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Kinematic Control of Manipulator with Remote Center of Motion Constraints Synthesised by a Simplified Recurrent Neural Network

Zhan Li, Shuai Li Orcid Logo

Neural Processing Letters, Volume: 54

Swansea University Authors: Zhan Li, Shuai Li Orcid Logo

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DOI (Published version): 10.1007/s11063-021-10678-5

Abstract

Redundancy manipulators need favorable redundancy resolution to obtain suitable control actions to guarantee accurate kinematic control. Among numerous kinematic control applications, some specific tasks such as minimally invasive manipulation/surgery require the distal link of a manipulator to tran...

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Published in: Neural Processing Letters
ISSN: 1370-4621 1573-773X
Published: Springer Science and Business Media LLC 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa58642
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first_indexed 2021-11-13T16:30:27Z
last_indexed 2022-05-07T03:31:33Z
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spelling 2022-05-06T16:53:52.8960797 v2 58642 2021-11-13 Kinematic Control of Manipulator with Remote Center of Motion Constraints Synthesised by a Simplified Recurrent Neural Network 94f19a09e17bad497ef1b4a0992c1d56 Zhan Li Zhan Li true false 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2021-11-13 SCS Redundancy manipulators need favorable redundancy resolution to obtain suitable control actions to guarantee accurate kinematic control. Among numerous kinematic control applications, some specific tasks such as minimally invasive manipulation/surgery require the distal link of a manipulator to translate along such fixed point. Such a point is known as remote center of motion (RCM) to constrain motion planning and kinematic control of manipulators. Recurrent neural network (RNN) which possesses parallel processing ability, is a powerful alternative and has achieved success in conventional redundancy resolution and kinematic control with physical constraints of joint limits. However, up to now, there still is few related works on the RNNs for redundancy resolution and kinematic control of manipulators with RCM constraints considered yet. In this paper, for the first time, an RNN-based approach with a simplified neural network architecture is proposed to solve the redundancy resolution issue with RCM constraints, with a new and general dynamic optimization formulation containing the RCM constraints investigated. Theoretical results analyze and convergence properties of the proposed simplified 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 a redundant manipulator. Journal Article Neural Processing Letters 54 Springer Science and Business Media LLC 1370-4621 1573-773X Redundant; Motion planning; Kinematics 13 11 2021 2021-11-13 10.1007/s11063-021-10678-5 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University SU Library paid the OA fee (TA Institutional Deal) National Natural Science Foundation of China 61603078 2022-05-06T16:53:52.8960797 2021-11-13T09:46:06.9725068 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Zhan Li 1 Shuai Li 0000-0001-8316-5289 2 58642__21728__40e7e3101ce0406b861df9a0f95b5ea4.pdf 58642.pdf 2021-11-30T11:49:14.4029824 Output 2002304 application/pdf Version of Record true © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License true eng http://creativecommons.org/licenses/by/4.0/
title Kinematic Control of Manipulator with Remote Center of Motion Constraints Synthesised by a Simplified Recurrent Neural Network
spellingShingle Kinematic Control of Manipulator with Remote Center of Motion Constraints Synthesised by a Simplified Recurrent Neural Network
Zhan Li
Shuai Li
title_short Kinematic Control of Manipulator with Remote Center of Motion Constraints Synthesised by a Simplified Recurrent Neural Network
title_full Kinematic Control of Manipulator with Remote Center of Motion Constraints Synthesised by a Simplified Recurrent Neural Network
title_fullStr Kinematic Control of Manipulator with Remote Center of Motion Constraints Synthesised by a Simplified Recurrent Neural Network
title_full_unstemmed Kinematic Control of Manipulator with Remote Center of Motion Constraints Synthesised by a Simplified Recurrent Neural Network
title_sort Kinematic Control of Manipulator with Remote Center of Motion Constraints Synthesised by a Simplified Recurrent Neural Network
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 Neural Processing Letters
container_volume 54
publishDate 2021
institution Swansea University
issn 1370-4621
1573-773X
doi_str_mv 10.1007/s11063-021-10678-5
publisher Springer Science and Business Media LLC
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 manipulators need favorable redundancy resolution to obtain suitable control actions to guarantee accurate kinematic control. Among numerous kinematic control applications, some specific tasks such as minimally invasive manipulation/surgery require the distal link of a manipulator to translate along such fixed point. Such a point is known as remote center of motion (RCM) to constrain motion planning and kinematic control of manipulators. Recurrent neural network (RNN) which possesses parallel processing ability, is a powerful alternative and has achieved success in conventional redundancy resolution and kinematic control with physical constraints of joint limits. However, up to now, there still is few related works on the RNNs for redundancy resolution and kinematic control of manipulators with RCM constraints considered yet. In this paper, for the first time, an RNN-based approach with a simplified neural network architecture is proposed to solve the redundancy resolution issue with RCM constraints, with a new and general dynamic optimization formulation containing the RCM constraints investigated. Theoretical results analyze and convergence properties of the proposed simplified 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 a redundant manipulator.
published_date 2021-11-13T04:15:19Z
_version_ 1763754031074770944
score 11.012678

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