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An L₁-Norm Based Optimization Method for Sparse Redundancy Resolution of Robotic Manipulators

Zhan Li, Shuai Li Orcid Logo

IEEE Transactions on Circuits and Systems II: Express Briefs, Volume: 69, Issue: 2, Pages: 469 - 473

Swansea University Authors: Zhan Li, Shuai Li Orcid Logo

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DOI (Published version): 10.1109/tcsii.2021.3088942

Abstract

For targeted motion control tasks of manipulators, it is frequently necessary to make use of full levels of joint actuation to guarantee successful motion planning and path tracking. Such way of motion planning and control may keep the joint actuation in a non-sparse manner during motion control pro...

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Published in: IEEE Transactions on Circuits and Systems II: Express Briefs
ISSN: 1549-7747 1558-3791
Published: Institute of Electrical and Electronics Engineers (IEEE) 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa57319
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spelling 2022-03-14T15:48:16.3584695 v2 57319 2021-07-14 An L₁-Norm Based Optimization Method for Sparse Redundancy Resolution of Robotic Manipulators 94f19a09e17bad497ef1b4a0992c1d56 Zhan Li Zhan Li true false 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2021-07-14 SCS For targeted motion control tasks of manipulators, it is frequently necessary to make use of full levels of joint actuation to guarantee successful motion planning and path tracking. Such way of motion planning and control may keep the joint actuation in a non-sparse manner during motion control process. In order to improve sparsity of joint actuation for manipulator systems, a novel motion planning scheme which can optimally and sparsely adopt joint actuation is proposed in this paper. The proposed motion planning strategy is formulated as a constrained L1 norm optimization problem, and an equivalent enhanced optimization solution dealing with bounded joint velocity is proposed as well. A new primal dual neural network with a new solution set division is further proposed and applied to solve such bounded optimization which can sparsely adopt joint actuation for motion control. Simulation and experiment results demonstrate the efficiency, accuracy and superiority of the proposed method for optimally and sparsely adopting joint actuation. The average sparsity (i.e., -||˙θ||p where θ denotes the joint angle) of the joint motion of the manipulator can be increased by 39.22% and 51.30% for path tracking tasks in X-Y and X-Z planes respectively, indicating that the sparsity of joint actuation can be enhanced. Journal Article IEEE Transactions on Circuits and Systems II: Express Briefs 69 2 469 473 Institute of Electrical and Electronics Engineers (IEEE) 1549-7747 1558-3791 1 2 2022 2022-02-01 10.1109/tcsii.2021.3088942 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2022-03-14T15:48:16.3584695 2021-07-14T11:42:39.9633333 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Zhan Li 1 Shuai Li 0000-0001-8316-5289 2 57319__20474__ab8c24baf55f4bb8a70a43605cdf0823.pdf An_L1-norm_based_Optimization_Method_for_Sparse_Redundancy_Resolution_of_Robotic_Manipulators.pdf 2021-07-28T14:55:17.8653343 Output 564454 application/pdf Accepted Manuscript true true eng
title An L₁-Norm Based Optimization Method for Sparse Redundancy Resolution of Robotic Manipulators
spellingShingle An L₁-Norm Based Optimization Method for Sparse Redundancy Resolution of Robotic Manipulators
Zhan Li
Shuai Li
title_short An L₁-Norm Based Optimization Method for Sparse Redundancy Resolution of Robotic Manipulators
title_full An L₁-Norm Based Optimization Method for Sparse Redundancy Resolution of Robotic Manipulators
title_fullStr An L₁-Norm Based Optimization Method for Sparse Redundancy Resolution of Robotic Manipulators
title_full_unstemmed An L₁-Norm Based Optimization Method for Sparse Redundancy Resolution of Robotic Manipulators
title_sort An L₁-Norm Based Optimization Method for Sparse Redundancy Resolution of Robotic Manipulators
author_id_str_mv 94f19a09e17bad497ef1b4a0992c1d56
42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 94f19a09e17bad497ef1b4a0992c1d56_***_Zhan Li
42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Zhan Li
Shuai Li
author2 Zhan Li
Shuai Li
format Journal article
container_title IEEE Transactions on Circuits and Systems II: Express Briefs
container_volume 69
container_issue 2
container_start_page 469
publishDate 2022
institution Swansea University
issn 1549-7747
1558-3791
doi_str_mv 10.1109/tcsii.2021.3088942
publisher Institute of Electrical and Electronics Engineers (IEEE)
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 Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description For targeted motion control tasks of manipulators, it is frequently necessary to make use of full levels of joint actuation to guarantee successful motion planning and path tracking. Such way of motion planning and control may keep the joint actuation in a non-sparse manner during motion control process. In order to improve sparsity of joint actuation for manipulator systems, a novel motion planning scheme which can optimally and sparsely adopt joint actuation is proposed in this paper. The proposed motion planning strategy is formulated as a constrained L1 norm optimization problem, and an equivalent enhanced optimization solution dealing with bounded joint velocity is proposed as well. A new primal dual neural network with a new solution set division is further proposed and applied to solve such bounded optimization which can sparsely adopt joint actuation for motion control. Simulation and experiment results demonstrate the efficiency, accuracy and superiority of the proposed method for optimally and sparsely adopting joint actuation. The average sparsity (i.e., -||˙θ||p where θ denotes the joint angle) of the joint motion of the manipulator can be increased by 39.22% and 51.30% for path tracking tasks in X-Y and X-Z planes respectively, indicating that the sparsity of joint actuation can be enhanced.
published_date 2022-02-01T04:12:58Z
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score 11.012678

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