Ranked pareto particle swarm optimization for mobile robot motion planning

Wang, D; Kwok, NM; Liu, DK; Ha, QP

Ranked pareto particle swarm optimization for mobile robot motion planning

Wang, D Kwok, NM Liu, DK

Ha, QP

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Publication Type:: Chapter
Citation:: 2009, 177 pp. 97 - 118
Issue Date:: 2009-12-01

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Field	Value	Language
dc.contributor.author	Wang, D	en_US
dc.contributor.author	Kwok, NM	en_US
dc.contributor.author	Liu, DK https://orcid.org/0000-0002-1581-5582	en_US
dc.contributor.author	Ha, QP https://orcid.org/0000-0003-0978-1758	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	2009, 177 pp. 97 - 118	en_US
dc.identifier.isbn	9783540899327	en_US
dc.identifier.uri	http://hdl.handle.net/10453/7878
dc.description.abstract	The Force Field (F 2) method is a novel approach for multi-robot motion planning and coordination. The setting of parameters in the (F 2) method, noticeably, can affect its performance. In this research, we present the Ranked Pareto Particle Swarm Optimization (RPPSO) approach as an extension of the basic idea of Particle Swarm Optimization (PSO), which makes it capable of solving multiobjective optimization problems efficiently. In the RPPSO, particles are initiated randomly in the search space; these particles are then evaluated for their qualities with regard to all objectives. Those particles with highly-ranked qualities have preferences to enter the set of Global Best vectors, which stores many currently best solutions found by particles. Thus, particles in RPPSO will search towards many possible directions and the diversity among solutions is well preserved. Ideally, a set of optimal solutions will be found when the termination criterion is met. The effectiveness of the proposed RPPSO is verified in simulation studies. Satisfactory results are obtained for multiobjective optimization problems of multi-robot motion planning in challenging environments with obstacles. © 2009 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.isbasedon	10.1007/978-3-540-89933-4_5	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Ranked pareto particle swarm optimization for mobile robot motion planning	en_US
dc.type	Chapter
utslib.citation.volume	177	en_US
utslib.for	090602 Control Systems, Robotics and Automation	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 Electrical and Data Engineering
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
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	177	en_US

Abstract:

The Force Field (F 2) method is a novel approach for multi-robot motion planning and coordination. The setting of parameters in the (F 2) method, noticeably, can affect its performance. In this research, we present the Ranked Pareto Particle Swarm Optimization (RPPSO) approach as an extension of the basic idea of Particle Swarm Optimization (PSO), which makes it capable of solving multiobjective optimization problems efficiently. In the RPPSO, particles are initiated randomly in the search space; these particles are then evaluated for their qualities with regard to all objectives. Those particles with highly-ranked qualities have preferences to enter the set of Global Best vectors, which stores many currently best solutions found by particles. Thus, particles in RPPSO will search towards many possible directions and the diversity among solutions is well preserved. Ideally, a set of optimal solutions will be found when the termination criterion is met. The effectiveness of the proposed RPPSO is verified in simulation studies. Satisfactory results are obtained for multiobjective optimization problems of multi-robot motion planning in challenging environments with obstacles. © 2009 Springer-Verlag Berlin Heidelberg.

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http://hdl.handle.net/10453/7878