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Development of a Neural Network-Based Control System for the DLR-HIT II Robot Hand Using Leap Motion

Chunxu Li Orcid Logo, Ashraf Fahmy Abdo Orcid Logo, Johann Sienz Orcid Logo

IEEE Access, Volume: 7, Pages: 136914 - 136923

Swansea University Authors: Chunxu Li Orcid Logo, Ashraf Fahmy Abdo Orcid Logo, Johann Sienz Orcid Logo

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DOI (Published version): 10.1109/ACCESS.2019.2942648

Abstract

In this paper, a neural network (NN) based adaptive controller has been successfully developed for the teleoperation of a DLR-HIT II robot hand using Leap Motion sensor. To achieve this, the coordinate positions of the human hand fingers are captured by a Leap Motion sensor. Moreover, inverse kinema...

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Published in: IEEE Access
ISSN: 2169-3536
Published: 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa52484
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first_indexed 2019-10-17T14:22:42Z
last_indexed 2019-10-17T14:22:42Z
id cronfa52484
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spelling v2 52484 2019-10-17 Development of a Neural Network-Based Control System for the DLR-HIT II Robot Hand Using Leap Motion e6ed70d02c25b05ab52340312559d684 0000-0001-7851-0260 Chunxu Li Chunxu Li true false b952b837f8a8447055210d209892b427 0000-0003-1624-1725 Ashraf Fahmy Abdo Ashraf Fahmy Abdo true false 17bf1dd287bff2cb01b53d98ceb28a31 0000-0003-3136-5718 Johann Sienz Johann Sienz true false 2019-10-17 FGSEN In this paper, a neural network (NN) based adaptive controller has been successfully developed for the teleoperation of a DLR-HIT II robot hand using Leap Motion sensor. To achieve this, the coordinate positions of the human hand fingers are captured by a Leap Motion sensor. Moreover, inverse kinematics is used to transform the Cartesian position data of the fingertips into corresponding joint angles of all the five fingers, which are then directly sent to teleoperate the DLR-HIT II hand via User Datagram Protocol (UDP). In addition, a NN-based control system programmed by the MATLAB Simulink function has been investigated to enhance the overall control performance by compensating the dynamic uncertainties existing in the teleoperation system. The stability of the control system has been proved mathematically using Lyapunov stability equations. A series of experiments have been conducted to test the performance of the proposed control technique, which has been proved to be an effective teleoperation strategy for the DLR-HIT II robot hand. Journal Article IEEE Access 7 136914 136923 2169-3536 2 10 2019 2019-10-02 10.1109/ACCESS.2019.2942648 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University 2024-04-10T09:32:13.7245285 2019-10-17T10:53:28.7800733 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Chunxu Li 0000-0001-7851-0260 1 Ashraf Fahmy Abdo 0000-0003-1624-1725 2 Johann Sienz 0000-0003-3136-5718 3
title Development of a Neural Network-Based Control System for the DLR-HIT II Robot Hand Using Leap Motion
spellingShingle Development of a Neural Network-Based Control System for the DLR-HIT II Robot Hand Using Leap Motion
Chunxu Li
Ashraf Fahmy Abdo
Johann Sienz
title_short Development of a Neural Network-Based Control System for the DLR-HIT II Robot Hand Using Leap Motion
title_full Development of a Neural Network-Based Control System for the DLR-HIT II Robot Hand Using Leap Motion
title_fullStr Development of a Neural Network-Based Control System for the DLR-HIT II Robot Hand Using Leap Motion
title_full_unstemmed Development of a Neural Network-Based Control System for the DLR-HIT II Robot Hand Using Leap Motion
title_sort Development of a Neural Network-Based Control System for the DLR-HIT II Robot Hand Using Leap Motion
author_id_str_mv e6ed70d02c25b05ab52340312559d684
b952b837f8a8447055210d209892b427
17bf1dd287bff2cb01b53d98ceb28a31
author_id_fullname_str_mv e6ed70d02c25b05ab52340312559d684_***_Chunxu Li
b952b837f8a8447055210d209892b427_***_Ashraf Fahmy Abdo
17bf1dd287bff2cb01b53d98ceb28a31_***_Johann Sienz
author Chunxu Li
Ashraf Fahmy Abdo
Johann Sienz
author2 Chunxu Li
Ashraf Fahmy Abdo
Johann Sienz
format Journal article
container_title IEEE Access
container_volume 7
container_start_page 136914
publishDate 2019
institution Swansea University
issn 2169-3536
doi_str_mv 10.1109/ACCESS.2019.2942648
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 0
active_str 0
description In this paper, a neural network (NN) based adaptive controller has been successfully developed for the teleoperation of a DLR-HIT II robot hand using Leap Motion sensor. To achieve this, the coordinate positions of the human hand fingers are captured by a Leap Motion sensor. Moreover, inverse kinematics is used to transform the Cartesian position data of the fingertips into corresponding joint angles of all the five fingers, which are then directly sent to teleoperate the DLR-HIT II hand via User Datagram Protocol (UDP). In addition, a NN-based control system programmed by the MATLAB Simulink function has been investigated to enhance the overall control performance by compensating the dynamic uncertainties existing in the teleoperation system. The stability of the control system has been proved mathematically using Lyapunov stability equations. A series of experiments have been conducted to test the performance of the proposed control technique, which has been proved to be an effective teleoperation strategy for the DLR-HIT II robot hand.
published_date 2019-10-02T09:32:11Z
_version_ 1795935888967991296
score 11.012924

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