Collaboration of multiple SCARA robots with guaranteed safety using recurrent neural networks

He, Yuhong; Li, Xiaoxiao; Xu, Zhihao; Zhou, Xuefeng; Li, Shuai

doi:10.1016/j.neucom.2021.05.049

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Collaboration of multiple SCARA robots with guaranteed safety using recurrent neural networks

Yuhong He, Xiaoxiao Li, Zhihao Xu, Xuefeng Zhou, Shuai Li

Neurocomputing, Volume: 456, Pages: 1 - 10

Swansea University Author: Shuai Li

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DOI (Published version): 10.1016/j.neucom.2021.05.049

Abstract

SCARA robot is one of the most popularly used robots in industry. The obstacle avoidance feature of multiple SCARA robot collaboration is essential and prominent, which can be used to support multiple robots to accomplish not only more sophisticated tasks but also more efficient than individual robo...

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Published in:	Neurocomputing
ISSN:	0925-2312
Published:	Elsevier BV 2021
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa56979
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first_indexed	2021-05-28T07:57:31Z
last_indexed	2021-06-16T03:22:07Z
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spelling	2021-06-15T15:32:14.0455762 v2 56979 2021-05-28 Collaboration of multiple SCARA robots with guaranteed safety using recurrent neural networks 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2021-05-28 MECH SCARA robot is one of the most popularly used robots in industry. The obstacle avoidance feature of multiple SCARA robot collaboration is essential and prominent, which can be used to support multiple robots to accomplish not only more sophisticated tasks but also more efficient than individual robot. This paper mainly focuses on studying the problem of simultaneous multi-robot coordination and obstacle avoidance. A cooperative kinematic control problem of multiple robot manipulators, collision avoidance is taken into account to be the primary task as an inequality constraint and trajectory planning task is considered to be the secondary objective as to ensure the priority of safety, is described as a quadratic programming(QP) problem. Then, a recurrent neural network (RNN) based dynamic controller is designed to solve the formulated QP problem recursively. The convergence of the designed neural network is proved through Lyapunov analysis. With three SCARA planar robots, the effectiveness of the proposed controller is validated through numerical simulations. As observed in the results, when the minimal distance between robots is less than the setting safety distance, the collision avoidance strategy reacts to impel robots to avoid collision, which achieves the primary objective for obstacle avoidance; otherwise, the robot performs the desired trajectory tracking task. Journal Article Neurocomputing 456 1 10 Elsevier BV 0925-2312 Multi-robot collaboration, obstacle avoidence, kinematic control, constrained optimization, recurrent neural network(RNN), safety 7 10 2021 2021-10-07 10.1016/j.neucom.2021.05.049 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2021-06-15T15:32:14.0455762 2021-05-28T08:55:07.8425133 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Yuhong He 1 Xiaoxiao Li 2 Zhihao Xu 3 Xuefeng Zhou 4 Shuai Li 0000-0001-8316-5289 5 56979__20020__b22cb50f44a04f6292ccffbd0eccc855.pdf 56979.pdf 2021-05-28T08:57:01.2616009 Output 1743067 application/pdf Accepted Manuscript true 2022-05-27T00:00:00.0000000 ©2021 All rights reserved. All article content, except where otherwise noted, is licensed under a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND) true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title	Collaboration of multiple SCARA robots with guaranteed safety using recurrent neural networks
spellingShingle	Collaboration of multiple SCARA robots with guaranteed safety using recurrent neural networks Shuai Li
title_short	Collaboration of multiple SCARA robots with guaranteed safety using recurrent neural networks
title_full	Collaboration of multiple SCARA robots with guaranteed safety using recurrent neural networks
title_fullStr	Collaboration of multiple SCARA robots with guaranteed safety using recurrent neural networks
title_full_unstemmed	Collaboration of multiple SCARA robots with guaranteed safety using recurrent neural networks
title_sort	Collaboration of multiple SCARA robots with guaranteed safety using recurrent neural networks
author_id_str_mv	42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv	42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author	Shuai Li
author2	Yuhong He Xiaoxiao Li Zhihao Xu Xuefeng Zhou Shuai Li
format	Journal article
container_title	Neurocomputing
container_volume	456
container_start_page	1
publishDate	2021
institution	Swansea University
issn	0925-2312
doi_str_mv	10.1016/j.neucom.2021.05.049
publisher	Elsevier BV
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	SCARA robot is one of the most popularly used robots in industry. The obstacle avoidance feature of multiple SCARA robot collaboration is essential and prominent, which can be used to support multiple robots to accomplish not only more sophisticated tasks but also more efficient than individual robot. This paper mainly focuses on studying the problem of simultaneous multi-robot coordination and obstacle avoidance. A cooperative kinematic control problem of multiple robot manipulators, collision avoidance is taken into account to be the primary task as an inequality constraint and trajectory planning task is considered to be the secondary objective as to ensure the priority of safety, is described as a quadratic programming(QP) problem. Then, a recurrent neural network (RNN) based dynamic controller is designed to solve the formulated QP problem recursively. The convergence of the designed neural network is proved through Lyapunov analysis. With three SCARA planar robots, the effectiveness of the proposed controller is validated through numerical simulations. As observed in the results, when the minimal distance between robots is less than the setting safety distance, the collision avoidance strategy reacts to impel robots to avoid collision, which achieves the primary objective for obstacle avoidance; otherwise, the robot performs the desired trajectory tracking task.
published_date	2021-10-07T04:12:21Z
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score	11.012678

Collaboration of multiple SCARA robots with guaranteed safety using recurrent neural networks

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