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Neural-Learning-Based Telerobot Control With Guaranteed Performance

Chenguang Yang, Xinyu Wang, Long Cheng, Hongbin Ma

IEEE Transactions on Cybernetics, Volume: 47, Issue: 10, Pages: 3148 - 3159

Swansea University Author: Chenguang Yang

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DOI (Published version): 10.1109/tcyb.2016.2573837

Abstract

We developed a neural networks (NN) enhanced telerobot control system and tested it on a Baxter robot. Obstacle avoidance at kinematic level enables the human operator only concentrate on motion of robot's end-effector without concern on possible collision. A posture restoration scheme ensures...

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Published in: IEEE Transactions on Cybernetics
ISSN: 2168-2267 2168-2275
Published: 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa28343
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first_indexed 2016-05-27T18:16:02Z
last_indexed 2023-02-03T03:34:45Z
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spelling 2023-02-02T15:57:44.2956046 v2 28343 2016-05-27 Neural-Learning-Based Telerobot Control With Guaranteed Performance d2a5024448bfac00a9b3890a8404380b Chenguang Yang Chenguang Yang true false 2016-05-27 EEN We developed a neural networks (NN) enhanced telerobot control system and tested it on a Baxter robot. Obstacle avoidance at kinematic level enables the human operator only concentrate on motion of robot's end-effector without concern on possible collision. A posture restoration scheme ensures the manipulator restore back to the natural posture in the absence of obstacles. Neural networks (NN) enhanced controller at dynamic level compensate for the effect caused by the internal and external uncertainties, e.g., unknown payload. Both the steady state and the transient performance are guaranteed to satisfy a prescribed performance requirement. Comparative experiments have been performed to test the effectiveness and to demonstrate the performance of the proposed methods. Journal Article IEEE Transactions on Cybernetics 47 10 3148 3159 2168-2267 2168-2275 Telerobot Control; Neural Networks; Collision Avoidance; Guaranteed Performance 1 10 2017 2017-10-01 10.1109/tcyb.2016.2573837 http://dx.doi.org/10.1109/tcyb.2016.2573837 COLLEGE NANME Engineering COLLEGE CODE EEN Swansea University RCUK, EPSRC, EP/L026856/2 2023-02-02T15:57:44.2956046 2016-05-27T14:09:54.7094627 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Chenguang Yang 1 Xinyu Wang 2 Long Cheng 3 Hongbin Ma 4 0028343-09092016143640.pdf yang2016.pdf 2016-09-09T14:36:40.9530000 Output 2651844 application/pdf Proof true 2016-09-09T00:00:00.0000000 This work is licensed under a Creative Commons Attribution 3.0 License. false eng http://creativecommons.org/licenses/by/3.0/
title Neural-Learning-Based Telerobot Control With Guaranteed Performance
spellingShingle Neural-Learning-Based Telerobot Control With Guaranteed Performance
Chenguang Yang
title_short Neural-Learning-Based Telerobot Control With Guaranteed Performance
title_full Neural-Learning-Based Telerobot Control With Guaranteed Performance
title_fullStr Neural-Learning-Based Telerobot Control With Guaranteed Performance
title_full_unstemmed Neural-Learning-Based Telerobot Control With Guaranteed Performance
title_sort Neural-Learning-Based Telerobot Control With Guaranteed Performance
author_id_str_mv d2a5024448bfac00a9b3890a8404380b
author_id_fullname_str_mv d2a5024448bfac00a9b3890a8404380b_***_Chenguang Yang
author Chenguang Yang
author2 Chenguang Yang
Xinyu Wang
Long Cheng
Hongbin Ma
format Journal article
container_title IEEE Transactions on Cybernetics
container_volume 47
container_issue 10
container_start_page 3148
publishDate 2017
institution Swansea University
issn 2168-2267
2168-2275
doi_str_mv 10.1109/tcyb.2016.2573837
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
url http://dx.doi.org/10.1109/tcyb.2016.2573837
document_store_str 1
active_str 0
description We developed a neural networks (NN) enhanced telerobot control system and tested it on a Baxter robot. Obstacle avoidance at kinematic level enables the human operator only concentrate on motion of robot's end-effector without concern on possible collision. A posture restoration scheme ensures the manipulator restore back to the natural posture in the absence of obstacles. Neural networks (NN) enhanced controller at dynamic level compensate for the effect caused by the internal and external uncertainties, e.g., unknown payload. Both the steady state and the transient performance are guaranteed to satisfy a prescribed performance requirement. Comparative experiments have been performed to test the effectiveness and to demonstrate the performance of the proposed methods.
published_date 2017-10-01T03:34:29Z
_version_ 1763751461413453824
score 11.036706

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