Cooperative coevolution of real predator robots and virtual robots in the pursuit domain

Sun, L; Lyu, C; Shi, Y

Cooperative coevolution of real predator robots and virtual robots in the pursuit domain

Sun, L

Lyu, C Shi, Y

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: APPLIED SOFT COMPUTING, 2020, 89
Issue Date:: 2020-04-01

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Field	Value	Language
dc.contributor.author	Sun, L https://orcid.org/0000-0002-9830-1527
dc.contributor.author	Lyu, C
dc.contributor.author	Shi, Y
dc.date.accessioned	2021-01-03T19:44:50Z
dc.date.available	2021-01-03T19:44:50Z
dc.date.issued	2020-04-01
dc.identifier.citation	APPLIED SOFT COMPUTING, 2020, 89
dc.identifier.issn	1568-4946
dc.identifier.issn	1872-9681
dc.identifier.uri	http://hdl.handle.net/10453/145036
dc.description.abstract	The pursuit domain, or predator-prey problem is a standard testbed for the study of coordination techniques. In spite that its problem setup is apparently simple, it is challenging for the research of the emerged swarm intelligence. This paper presents a particle swarm optimization (PSO) based cooperative coevolutionary algorithm for the predator robots, called CCPSO-R, where real and virtual robots coexist for the first time in an evolutionary algorithm (EA). Virtual robots sample and explore the vicinity of the corresponding real robot and act as their action spaces, while the real robots consist of the real predators swarm who actually pursue the prey robot without fixed behavior rules under the immediate guidance of the fitness function, which is designed in a modular manner with very limited domain knowledges. In addition, kinematic limits and collision avoidance considerations are integrated into the update rules of robots. Experiments are conducted on a scalable predator robots swarm with 4 types of preys, the statistical results of which show the reliability, generality, and scalability of the proposed CCPSO-R. Finally, the codes of this paper are public availabe at: https://github.com/LijunSun90/pursuitCCPSO_R.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	APPLIED SOFT COMPUTING
dc.relation.isbasedon	10.1016/j.asoc.2020.106098
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0806 Information Systems
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Cooperative coevolution of real predator robots and virtual robots in the pursuit domain
dc.type	Journal Article
utslib.citation.volume	89
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2022-04-01T00:00:00+1000Z
dc.date.updated	2021-01-03T19:44:45Z
pubs.publication-status	Published
pubs.volume	89

Abstract:

The pursuit domain, or predator-prey problem is a standard testbed for the study of coordination techniques. In spite that its problem setup is apparently simple, it is challenging for the research of the emerged swarm intelligence. This paper presents a particle swarm optimization (PSO) based cooperative coevolutionary algorithm for the predator robots, called CCPSO-R, where real and virtual robots coexist for the first time in an evolutionary algorithm (EA). Virtual robots sample and explore the vicinity of the corresponding real robot and act as their action spaces, while the real robots consist of the real predators swarm who actually pursue the prey robot without fixed behavior rules under the immediate guidance of the fitness function, which is designed in a modular manner with very limited domain knowledges. In addition, kinematic limits and collision avoidance considerations are integrated into the update rules of robots. Experiments are conducted on a scalable predator robots swarm with 4 types of preys, the statistical results of which show the reliability, generality, and scalability of the proposed CCPSO-R. Finally, the codes of this paper are public availabe at: https://github.com/LijunSun90/pursuitCCPSO_R.

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http://hdl.handle.net/10453/145036