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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 Orcid Logo

Neurocomputing, Volume: 456, Pages: 1 - 10

Swansea University Author: Shuai Li Orcid Logo

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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
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URI: https://cronfa.swan.ac.uk/Record/cronfa56979
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 ACEM 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 Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM 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 2021-10-07T14:05:38Z
_version_ 1821324018716770304
score 11.04787

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