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Motion Planning of Manipulators for Simultaneous Obstacle Avoidance and Target Tracking: An RNN Approach with Guaranteed Performance

Zhihao Xu, Xuefeng Zhou, Hongmin Wu, Xiaoxiao Li, Shuai Li Orcid Logo

IEEE Transactions on Industrial Electronics, Pages: 1 - 1

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1109/tie.2021.3073305

Abstract

Motion planning is a core issue in the field of robotic control, which directly affects the programming efficiency of robots. In this paper, we study the motion planning problem of manipulators for simultaneous obstacle avoidance and target tracking and propose a novel real-time planning method in a...

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Published in: IEEE Transactions on Industrial Electronics
ISSN: 0278-0046 1557-9948
Published: Institute of Electrical and Electronics Engineers (IEEE) 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa56810
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first_indexed 2021-05-06T08:56:22Z
last_indexed 2021-05-29T03:21:31Z
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spelling 2021-05-28T11:35:09.0203418 v2 56810 2021-05-06 Motion Planning of Manipulators for Simultaneous Obstacle Avoidance and Target Tracking: An RNN Approach with Guaranteed Performance 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2021-05-06 MECH Motion planning is a core issue in the field of robotic control, which directly affects the programming efficiency of robots. In this paper, we study the motion planning problem of manipulators for simultaneous obstacle avoidance and target tracking and propose a novel real-time planning method in a complex workspace. One important feature of the proposed method is that the robot can avoid colliding with obstacles by easily defining virtual fences, which are described by a group of level set functions. Thus, the feasible space can be abstracted as inequality constraints. Taking the predefined task, physical constraints, and feasible space constraints into consideration, the motion planning problem is formulated into quadratic programming (QP) one, in which the redundant DOFs are used to optimize the velocities of the robot. Then, the control command is obtained by an established recurrent neural network, which is capable of solving the QP problem in an online manner. Theoretical conduction and verification in several typical workspaces demonstrate the efficacy of the established method, such as the ability to remove physical fences, quick rearrangement, and performance optimization. Journal Article IEEE Transactions on Industrial Electronics 1 1 Institute of Electrical and Electronics Engineers (IEEE) 0278-0046 1557-9948 20 4 2021 2021-04-20 10.1109/tie.2021.3073305 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2021-05-28T11:35:09.0203418 2021-05-06T09:53:06.9872172 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Zhihao Xu 1 Xuefeng Zhou 2 Hongmin Wu 3 Xiaoxiao Li 4 Shuai Li 0000-0001-8316-5289 5
title Motion Planning of Manipulators for Simultaneous Obstacle Avoidance and Target Tracking: An RNN Approach with Guaranteed Performance
spellingShingle Motion Planning of Manipulators for Simultaneous Obstacle Avoidance and Target Tracking: An RNN Approach with Guaranteed Performance
Shuai Li
title_short Motion Planning of Manipulators for Simultaneous Obstacle Avoidance and Target Tracking: An RNN Approach with Guaranteed Performance
title_full Motion Planning of Manipulators for Simultaneous Obstacle Avoidance and Target Tracking: An RNN Approach with Guaranteed Performance
title_fullStr Motion Planning of Manipulators for Simultaneous Obstacle Avoidance and Target Tracking: An RNN Approach with Guaranteed Performance
title_full_unstemmed Motion Planning of Manipulators for Simultaneous Obstacle Avoidance and Target Tracking: An RNN Approach with Guaranteed Performance
title_sort Motion Planning of Manipulators for Simultaneous Obstacle Avoidance and Target Tracking: An RNN Approach with Guaranteed Performance
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Zhihao Xu
Xuefeng Zhou
Hongmin Wu
Xiaoxiao Li
Shuai Li
format Journal article
container_title IEEE Transactions on Industrial Electronics
container_start_page 1
publishDate 2021
institution Swansea University
issn 0278-0046
1557-9948
doi_str_mv 10.1109/tie.2021.3073305
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 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 Motion planning is a core issue in the field of robotic control, which directly affects the programming efficiency of robots. In this paper, we study the motion planning problem of manipulators for simultaneous obstacle avoidance and target tracking and propose a novel real-time planning method in a complex workspace. One important feature of the proposed method is that the robot can avoid colliding with obstacles by easily defining virtual fences, which are described by a group of level set functions. Thus, the feasible space can be abstracted as inequality constraints. Taking the predefined task, physical constraints, and feasible space constraints into consideration, the motion planning problem is formulated into quadratic programming (QP) one, in which the redundant DOFs are used to optimize the velocities of the robot. Then, the control command is obtained by an established recurrent neural network, which is capable of solving the QP problem in an online manner. Theoretical conduction and verification in several typical workspaces demonstrate the efficacy of the established method, such as the ability to remove physical fences, quick rearrangement, and performance optimization.
published_date 2021-04-20T04:12:03Z
_version_ 1763753824681459712
score 11.036334

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