An intelligence-based hybrid PSO-SA for mobile robot path planning in warehouse

Lin, S; Liu, A; Wang, J; Kong, X

An intelligence-based hybrid PSO-SA for mobile robot path planning in warehouse

Lin, S Liu, A Wang, J

Kong, X

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Journal of Computational Science, 2023, 67, pp. 101938
Issue Date:: 2023-03-01

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Field	Value	Language
dc.contributor.author	Lin, S
dc.contributor.author	Liu, A
dc.contributor.author	Wang, J https://orcid.org/0000-0002-6282-0456
dc.contributor.author	Kong, X
dc.date.accessioned	2023-02-18T07:26:39Z
dc.date.available	2023-02-18T07:26:39Z
dc.date.issued	2023-03-01
dc.identifier.citation	Journal of Computational Science, 2023, 67, pp. 101938
dc.identifier.issn	1877-7503
dc.identifier.uri	http://hdl.handle.net/10453/166245
dc.description.abstract	Mobile robots play crucial roles in industry and commerce, and automatic guided vehicles (AGV) are one of the primary parts of smart manufactory and intelligent logistics. Path planning is the core task for the AGV system, and it generates the path from origin to destination. The motivation of the study is to improve the scalability, flexibility, adaptability, and performance of the robot path planning systems. We propose the hybrid PSO-SA algorithm for the optimization of AGV path planning. Compared with other heuristic algorithms by benchmark functions, including HS, FA, ABC and GA, the proposed algorithm shows excellent performance in dealing with optimization problems. It reduces the possibility of getting trapped in one local optimum and enhances the efficiency to get the best global solution with faster convergence and less time consumption. It is evaluated with multiple cost functions and path planning with simulations and experiments. The objective of the proposed algorithm is to minimize the path length and produce a smooth path without collision. The proposed PSO-SA algorithm is compared with PSO in the path planning application, and the mean runtime and iteration times are usually significantly lower than PSO.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Journal of Computational Science
dc.relation.isbasedon	10.1016/j.jocs.2022.101938
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0802 Computation Theory and Mathematics, 0806 Information Systems
dc.title	An intelligence-based hybrid PSO-SA for mobile robot path planning in warehouse
dc.type	Journal Article
utslib.citation.volume	67
utslib.for	0802 Computation Theory and Mathematics
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	closed_access	*
dc.date.updated	2023-02-18T07:26:37Z
pubs.publication-status	Accepted
pubs.volume	67

Abstract:

Mobile robots play crucial roles in industry and commerce, and automatic guided vehicles (AGV) are one of the primary parts of smart manufactory and intelligent logistics. Path planning is the core task for the AGV system, and it generates the path from origin to destination. The motivation of the study is to improve the scalability, flexibility, adaptability, and performance of the robot path planning systems. We propose the hybrid PSO-SA algorithm for the optimization of AGV path planning. Compared with other heuristic algorithms by benchmark functions, including HS, FA, ABC and GA, the proposed algorithm shows excellent performance in dealing with optimization problems. It reduces the possibility of getting trapped in one local optimum and enhances the efficiency to get the best global solution with faster convergence and less time consumption. It is evaluated with multiple cost functions and path planning with simulations and experiments. The objective of the proposed algorithm is to minimize the path length and produce a smooth path without collision. The proposed PSO-SA algorithm is compared with PSO in the path planning application, and the mean runtime and iteration times are usually significantly lower than PSO.

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