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Collision-Free Compliance Control for Redundant Manipulators: An Optimization Case

Xuefeng Zhou, Zhihao Xu, Shuai Li Orcid Logo

Frontiers in Neurorobotics, Volume: 13

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.3389/fnbot.2019.00050

Abstract

Force control of manipulators could enhance compliance and execution capabilities, and has become a key issue in the field of robotic control. However, it is challenging for redundant manipulators, especially when there exist risks of collisions. In this paper, we propose a collision-free compliance...

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Published in: Frontiers in Neurorobotics
ISSN: 1662-5218
Published: Frontiers Media SA 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa52009
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first_indexed 2019-09-23T14:18:32Z
last_indexed 2020-10-20T03:02:45Z
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spelling 2020-10-19T16:45:39.9165993 v2 52009 2019-09-23 Collision-Free Compliance Control for Redundant Manipulators: An Optimization Case 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2019-09-23 MECH Force control of manipulators could enhance compliance and execution capabilities, and has become a key issue in the field of robotic control. However, it is challenging for redundant manipulators, especially when there exist risks of collisions. In this paper, we propose a collision-free compliance control strategy based on recurrent neural networks. Inspired by impedance control, the position-force control task is rebuilt as a reference command of task-space velocities, by combing kinematic properties, the compliance controller is then described as an equality constraint in joint velocity level. As to collision avoidance strategy, both robot and obstacles are approximately described as two sets of key points, and the distances between those points are used to scale the feasible workspace. In order to save unnecessary energy consumption while reducing impact of possible collisions, the secondary task is chosen to minimize joint velocities. Then a RNN with provable convergence is established to solve the constraint-optimization problem in realtime. Numerical results validate the effectiveness of the proposed controller. Journal Article Frontiers in Neurorobotics 13 Frontiers Media SA 1662-5218 31 12 2019 2019-12-31 10.3389/fnbot.2019.00050 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2020-10-19T16:45:39.9165993 2019-09-23T11:51:24.2878826 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Xuefeng Zhou 1 Zhihao Xu 2 Shuai Li 0000-0001-8316-5289 3 0052009-10102019121523.pdf zhou2019.pdf 2019-10-10T12:15:23.3330000 Output 1539677 application/pdf Version of Record true 2019-10-10T00:00:00.0000000 Distributed under the terms of a Creative Commons Attribution 4.0 (CC-BY) Licence. false eng http://creativecommons.org/licenses/by/4.0/
title Collision-Free Compliance Control for Redundant Manipulators: An Optimization Case
spellingShingle Collision-Free Compliance Control for Redundant Manipulators: An Optimization Case
Shuai Li
title_short Collision-Free Compliance Control for Redundant Manipulators: An Optimization Case
title_full Collision-Free Compliance Control for Redundant Manipulators: An Optimization Case
title_fullStr Collision-Free Compliance Control for Redundant Manipulators: An Optimization Case
title_full_unstemmed Collision-Free Compliance Control for Redundant Manipulators: An Optimization Case
title_sort Collision-Free Compliance Control for Redundant Manipulators: An Optimization Case
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Xuefeng Zhou
Zhihao Xu
Shuai Li
format Journal article
container_title Frontiers in Neurorobotics
container_volume 13
publishDate 2019
institution Swansea University
issn 1662-5218
doi_str_mv 10.3389/fnbot.2019.00050
publisher Frontiers Media SA
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 Force control of manipulators could enhance compliance and execution capabilities, and has become a key issue in the field of robotic control. However, it is challenging for redundant manipulators, especially when there exist risks of collisions. In this paper, we propose a collision-free compliance control strategy based on recurrent neural networks. Inspired by impedance control, the position-force control task is rebuilt as a reference command of task-space velocities, by combing kinematic properties, the compliance controller is then described as an equality constraint in joint velocity level. As to collision avoidance strategy, both robot and obstacles are approximately described as two sets of key points, and the distances between those points are used to scale the feasible workspace. In order to save unnecessary energy consumption while reducing impact of possible collisions, the secondary task is chosen to minimize joint velocities. Then a RNN with provable convergence is established to solve the constraint-optimization problem in realtime. Numerical results validate the effectiveness of the proposed controller.
published_date 2019-12-31T04:04:08Z
_version_ 1763753326944452608
score 11.013082

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