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Brain–Machine Interface and Visual Compressive Sensing-Based Teleoperation Control of an Exoskeleton Robot

Shiyuan Qiu, Zhijun Li, Wei He, Longbin Zhang, Chenguang Yang, Chun-Yi Su

IEEE Transactions on Fuzzy Systems, Volume: 25, Issue: 1, Pages: 58 - 69

Swansea University Author: Chenguang Yang

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DOI (Published version): 10.1109/TFUZZ.2016.2566676

Abstract

This paper presents a teleoperation control for an exoskeleton robotic system based on the brain-machine interface and vision feedback. Vision compressive sensing, brain-machine reference commands, and adaptive fuzzy controllers in joint-space have been effectively integrated to enable the robot per...

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Published in: IEEE Transactions on Fuzzy Systems
ISSN: 1063-6706 1941-0034
Published: 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa28341
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first_indexed 2016-05-27T18:16:02Z
last_indexed 2023-02-03T03:34:45Z
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spelling 2023-02-02T15:57:27.2974902 v2 28341 2016-05-27 Brain–Machine Interface and Visual Compressive Sensing-Based Teleoperation Control of an Exoskeleton Robot d2a5024448bfac00a9b3890a8404380b Chenguang Yang Chenguang Yang true false 2016-05-27 EEN This paper presents a teleoperation control for an exoskeleton robotic system based on the brain-machine interface and vision feedback. Vision compressive sensing, brain-machine reference commands, and adaptive fuzzy controllers in joint-space have been effectively integrated to enable the robot performing manipulation tasks guided by human operator's mind. First, a visual-feedback link is implemented by a video captured by a camera, allowing him/her to visualize the manipulator's workspace and movements being executed. Then, the compressed images are used as feedback errors in a nonvector space for producing steady-state visual evoked potentials electroencephalography (EEG) signals, and it requires no prior information on features in contrast to the traditional visual servoing. The proposed EEG decoding algorithm generates control signals for the exoskeleton robot using features extracted from neural activity. Considering coupled dynamics and actuator input constraints during the robot manipulation, a local adaptive fuzzy controller has been designed to drive the exoskeleton tracking the intended trajectories in human operator's mind and to provide a convenient way of dynamics compensation with minimal knowledge of the dynamics parameters of the exoskeleton robot. Extensive experiment studies employing three subjects have been performed to verify the validity of the proposed method. Journal Article IEEE Transactions on Fuzzy Systems 25 1 58 69 1063-6706 1941-0034 Fuzzy logic system; Teleoperation; Visual compress sense; EEG; Brain-machine interface; Exoskeleton robot 28 2 2017 2017-02-28 10.1109/TFUZZ.2016.2566676 COLLEGE NANME Engineering COLLEGE CODE EEN Swansea University 2023-02-02T15:57:27.2974902 2016-05-27T13:57:02.4265122 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Shiyuan Qiu 1 Zhijun Li 2 Wei He 3 Longbin Zhang 4 Chenguang Yang 5 Chun-Yi Su 6 0028341-28062016233428.pdf TFS-2015-0711R2.pdf 2016-06-28T23:34:28.9230000 Output 6752462 application/pdf Accepted Manuscript true 2016-06-28T00:00:00.0000000 true
title Brain–Machine Interface and Visual Compressive Sensing-Based Teleoperation Control of an Exoskeleton Robot
spellingShingle Brain–Machine Interface and Visual Compressive Sensing-Based Teleoperation Control of an Exoskeleton Robot
Chenguang Yang
title_short Brain–Machine Interface and Visual Compressive Sensing-Based Teleoperation Control of an Exoskeleton Robot
title_full Brain–Machine Interface and Visual Compressive Sensing-Based Teleoperation Control of an Exoskeleton Robot
title_fullStr Brain–Machine Interface and Visual Compressive Sensing-Based Teleoperation Control of an Exoskeleton Robot
title_full_unstemmed Brain–Machine Interface and Visual Compressive Sensing-Based Teleoperation Control of an Exoskeleton Robot
title_sort Brain–Machine Interface and Visual Compressive Sensing-Based Teleoperation Control of an Exoskeleton Robot
author_id_str_mv d2a5024448bfac00a9b3890a8404380b
author_id_fullname_str_mv d2a5024448bfac00a9b3890a8404380b_***_Chenguang Yang
author Chenguang Yang
author2 Shiyuan Qiu
Zhijun Li
Wei He
Longbin Zhang
Chenguang Yang
Chun-Yi Su
format Journal article
container_title IEEE Transactions on Fuzzy Systems
container_volume 25
container_issue 1
container_start_page 58
publishDate 2017
institution Swansea University
issn 1063-6706
1941-0034
doi_str_mv 10.1109/TFUZZ.2016.2566676
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 This paper presents a teleoperation control for an exoskeleton robotic system based on the brain-machine interface and vision feedback. Vision compressive sensing, brain-machine reference commands, and adaptive fuzzy controllers in joint-space have been effectively integrated to enable the robot performing manipulation tasks guided by human operator's mind. First, a visual-feedback link is implemented by a video captured by a camera, allowing him/her to visualize the manipulator's workspace and movements being executed. Then, the compressed images are used as feedback errors in a nonvector space for producing steady-state visual evoked potentials electroencephalography (EEG) signals, and it requires no prior information on features in contrast to the traditional visual servoing. The proposed EEG decoding algorithm generates control signals for the exoskeleton robot using features extracted from neural activity. Considering coupled dynamics and actuator input constraints during the robot manipulation, a local adaptive fuzzy controller has been designed to drive the exoskeleton tracking the intended trajectories in human operator's mind and to provide a convenient way of dynamics compensation with minimal knowledge of the dynamics parameters of the exoskeleton robot. Extensive experiment studies employing three subjects have been performed to verify the validity of the proposed method.
published_date 2017-02-28T03:34:29Z
_version_ 1763751461289721856
score 11.017797

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