Evolutionary computing based mobile robot localization

Kwok, NM; Liu, DK; Dissanayake, G

Evolutionary computing based mobile robot localization

Kwok, NM Liu, DK

Dissanayake, G

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Publication Type:: Journal Article
Citation:: Engineering Applications of Artificial Intelligence, 2006, 19 (8), pp. 857 - 868
Issue Date:: 2006-12-01

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Field	Value	Language
dc.contributor.author	Kwok, NM	en_US
dc.contributor.author	Liu, DK https://orcid.org/0000-0002-1581-5582	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2006-12-01	en_US
dc.identifier.citation	Engineering Applications of Artificial Intelligence, 2006, 19 (8), pp. 857 - 868	en_US
dc.identifier.issn	0952-1976	en_US
dc.identifier.uri	http://hdl.handle.net/10453/12794
dc.description.abstract	Evolutionary computing techniques, including genetic algorithms (GA), particle swarm optimization (PSO) and ants system (AS) are applied to the localization problem of a mobile robot. Salient features of robot localization are that the system is partially dynamic and information for fitness evaluation is incomplete and corrupted by noise. In this research, variations to the above three evolutionary computing techniques are proposed to tackle the specific dynamic and noisy system. Their performances are compared based on simulation and experiment results and the feasibility of the proposed approach to mobile robot localization is demonstrated. © 2006 Elsevier Ltd. All rights reserved.	en_US
dc.relation.ispartof	Engineering Applications of Artificial Intelligence	en_US
dc.relation.isbasedon	10.1016/j.engappai.2006.01.020	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Evolutionary computing based mobile robot localization	en_US
dc.type	Journal Article
utslib.citation.volume	8	en_US
utslib.citation.volume	19	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
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 Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access
pubs.issue	8	en_US
pubs.publication-status	Published	en_US
pubs.volume	19	en_US

Abstract:

Evolutionary computing techniques, including genetic algorithms (GA), particle swarm optimization (PSO) and ants system (AS) are applied to the localization problem of a mobile robot. Salient features of robot localization are that the system is partially dynamic and information for fitness evaluation is incomplete and corrupted by noise. In this research, variations to the above three evolutionary computing techniques are proposed to tackle the specific dynamic and noisy system. Their performances are compared based on simulation and experiment results and the feasibility of the proposed approach to mobile robot localization is demonstrated. © 2006 Elsevier Ltd. All rights reserved.

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http://hdl.handle.net/10453/12794