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Tracking control of redundant manipulator under active remote center-of-motion constraints: an RNN-based metaheuristic approach

Ameer Hamza Khan, Shuai Li Orcid Logo, Xinwei Cao

Science China Information Sciences, Volume: 64, Issue: 3

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1007/s11432-019-2735-6

Abstract

In this paper, we propose a recurrent neural network (RNN) for the tracking control of surgical robots while satisfying remote center-of-motion (RCM) constraints. RCM constraints enforce rules suggesting that the surgical tip should not go beyond the region of incision while tracking the commands of...

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Published in: Science China Information Sciences
ISSN: 1674-733X 1869-1919
Published: Springer Science and Business Media LLC 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa56354
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first_indexed 2021-03-02T09:45:53Z
last_indexed 2021-06-15T03:21:05Z
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spelling 2021-06-14T15:49:06.5841011 v2 56354 2021-03-02 Tracking control of redundant manipulator under active remote center-of-motion constraints: an RNN-based metaheuristic approach 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2021-03-02 MECH In this paper, we propose a recurrent neural network (RNN) for the tracking control of surgical robots while satisfying remote center-of-motion (RCM) constraints. RCM constraints enforce rules suggesting that the surgical tip should not go beyond the region of incision while tracking the commands of the surgeon. Violations of RCM constraints can result in serious injury to the patient. We unify the RCM constraints with the tracing control by formulating a single constrained optimization problem using a penalty-term approach. The penalty-term actively rewards the optimizer for satisfying the RCM constraints. We then propose an RNN-based metaheuristic optimization algorithm called “Beetle Antennae Olfactory Recurrent Neural Network (BAORNN)” for solving the formulated optimization problem in real time. The proposed control framework can track the surgeon’s commands and satisfy the RCM constraints simultaneously. Theoretical analysis is performed to demonstrate the stability and convergence of the BAORNN algorithm. Simulations using LBR IIWA14, a 7-degree-of-freedom robotic arm, are performed to analyze the performance of the proposed framework. Journal Article Science China Information Sciences 64 3 Springer Science and Business Media LLC 1674-733X 1869-1919 tracking control, surgical robots, RCM constraints, metaheuristic optimization, recurrent neural network, RNN, redundant manipulator 5 2 2021 2021-02-05 10.1007/s11432-019-2735-6 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2021-06-14T15:49:06.5841011 2021-03-02T09:43:00.1074419 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Ameer Hamza Khan 1 Shuai Li 0000-0001-8316-5289 2 Xinwei Cao 3
title Tracking control of redundant manipulator under active remote center-of-motion constraints: an RNN-based metaheuristic approach
spellingShingle Tracking control of redundant manipulator under active remote center-of-motion constraints: an RNN-based metaheuristic approach
Shuai Li
title_short Tracking control of redundant manipulator under active remote center-of-motion constraints: an RNN-based metaheuristic approach
title_full Tracking control of redundant manipulator under active remote center-of-motion constraints: an RNN-based metaheuristic approach
title_fullStr Tracking control of redundant manipulator under active remote center-of-motion constraints: an RNN-based metaheuristic approach
title_full_unstemmed Tracking control of redundant manipulator under active remote center-of-motion constraints: an RNN-based metaheuristic approach
title_sort Tracking control of redundant manipulator under active remote center-of-motion constraints: an RNN-based metaheuristic approach
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Ameer Hamza Khan
Shuai Li
Xinwei Cao
format Journal article
container_title Science China Information Sciences
container_volume 64
container_issue 3
publishDate 2021
institution Swansea University
issn 1674-733X
1869-1919
doi_str_mv 10.1007/s11432-019-2735-6
publisher Springer Science and Business Media LLC
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, we propose a recurrent neural network (RNN) for the tracking control of surgical robots while satisfying remote center-of-motion (RCM) constraints. RCM constraints enforce rules suggesting that the surgical tip should not go beyond the region of incision while tracking the commands of the surgeon. Violations of RCM constraints can result in serious injury to the patient. We unify the RCM constraints with the tracing control by formulating a single constrained optimization problem using a penalty-term approach. The penalty-term actively rewards the optimizer for satisfying the RCM constraints. We then propose an RNN-based metaheuristic optimization algorithm called “Beetle Antennae Olfactory Recurrent Neural Network (BAORNN)” for solving the formulated optimization problem in real time. The proposed control framework can track the surgeon’s commands and satisfy the RCM constraints simultaneously. Theoretical analysis is performed to demonstrate the stability and convergence of the BAORNN algorithm. Simulations using LBR IIWA14, a 7-degree-of-freedom robotic arm, are performed to analyze the performance of the proposed framework.
published_date 2021-02-05T04:11:15Z
_version_ 1763753774714716160
score 11.036334

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