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Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach

Ameer Hamza Khan, Shuai Li Orcid Logo, Xin Luo

IEEE Transactions on Industrial Informatics, Volume: 16, Issue: 7, Pages: 4670 - 4680

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1109/tii.2019.2941916

Abstract

This paper presents a metaheuristic-based control framework, called Beetle Antennae Olfactory Recurrent Neural Network (BAORNN), for simultaneous tracking control and obstacle avoidance of a redundant manipulator. The ability to avoid obstacles while tracking a predefined reference path is critical...

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Published in: IEEE Transactions on Industrial Informatics
ISSN: 1551-3203 1941-0050
Published: 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa51997
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spelling 2021-01-15T10:32:12.8077157 v2 51997 2019-09-23 Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2019-09-23 MECH This paper presents a metaheuristic-based control framework, called Beetle Antennae Olfactory Recurrent Neural Network (BAORNN), for simultaneous tracking control and obstacle avoidance of a redundant manipulator. The ability to avoid obstacles while tracking a predefined reference path is critical for any industrial manipulator. The formulated control framework unifies the tracking control and obstacle avoidance into a single constrained optimization problem by introducing a penalty term into the objective function, which actively rewards the optimizer for avoiding the obstacles. One of the significant features of the proposed framework is the way that the penalty term is formulated following a straightforward principle: maximize the minimum distance between manipulator and obstacle. The distance calculations are based on GJK (Gilbert-Johnson-Keerthi) algorithm, which calculates the distance between manipulator and obstacle by directly using their 3D-geometries. Which also implies that our algorithm works for arbitrarily shaped manipulator and obstacle. Theoretical treatment proves the stability and convergence, and simulations results using LBR IIWA 7-DOF manipulator are presented to analyze the performance of the proposed framework. Journal Article IEEE Transactions on Industrial Informatics 16 7 4670 4680 1551-3203 1941-0050 1 7 2020 2020-07-01 10.1109/tii.2019.2941916 http://dx.doi.org/10.1109/tii.2019.2941916 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2021-01-15T10:32:12.8077157 2019-09-23T11:36:46.5959330 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 Xin Luo 3 0051997-11102019093931.pdf khan2019.pdf 2019-10-11T09:39:31.2400000 Output 10245230 application/pdf Accepted Manuscript true 2019-10-11T00:00:00.0000000 true eng
title Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach
spellingShingle Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach
Shuai Li
title_short Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach
title_full Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach
title_fullStr Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach
title_full_unstemmed Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach
title_sort Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: 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
Xin Luo
format Journal article
container_title IEEE Transactions on Industrial Informatics
container_volume 16
container_issue 7
container_start_page 4670
publishDate 2020
institution Swansea University
issn 1551-3203
1941-0050
doi_str_mv 10.1109/tii.2019.2941916
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
url http://dx.doi.org/10.1109/tii.2019.2941916
document_store_str 1
active_str 0
description This paper presents a metaheuristic-based control framework, called Beetle Antennae Olfactory Recurrent Neural Network (BAORNN), for simultaneous tracking control and obstacle avoidance of a redundant manipulator. The ability to avoid obstacles while tracking a predefined reference path is critical for any industrial manipulator. The formulated control framework unifies the tracking control and obstacle avoidance into a single constrained optimization problem by introducing a penalty term into the objective function, which actively rewards the optimizer for avoiding the obstacles. One of the significant features of the proposed framework is the way that the penalty term is formulated following a straightforward principle: maximize the minimum distance between manipulator and obstacle. The distance calculations are based on GJK (Gilbert-Johnson-Keerthi) algorithm, which calculates the distance between manipulator and obstacle by directly using their 3D-geometries. Which also implies that our algorithm works for arbitrarily shaped manipulator and obstacle. Theoretical treatment proves the stability and convergence, and simulations results using LBR IIWA 7-DOF manipulator are presented to analyze the performance of the proposed framework.
published_date 2020-07-01T04:04:06Z
_version_ 1763753325475397632
score 11.036334

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