PSO-tuned F2 method for multi-robot navigation

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Show simple item record Wang, Dalong en_US Kwok, Ngai en_US Liu, Dikai en_US Lau, Haye en_US Dissanayake, Gamini en_US
dc.contributor.editor Edward Grant et al en_US 2009-11-09T05:36:00Z 2009-11-09T05:36:00Z 2007 en_US
dc.identifier 2007000091 en_US
dc.identifier.citation Wang Dalong et al. 2007, 'PSO-tuned F2 method for multi-robot navigation', IEEE, USA, pp. 3765-3770. en_US
dc.identifier.issn 1-4244-0912-8/07 en_US
dc.identifier.other E1 en_US
dc.description.abstract The F (Force Field) method is a novel approach for multi-robot motion planning and collision avoidance. The setting of parameters is however vital to its performance. This paper presents an approach using Particle Swarm Optimization (PSO) to properly determine the control parameters for the F2 method. The goal of the optimization is to minimize the resultant path lengths. The approach presented in this paper can be used as a tool to obtain optimal parameters for various tasks before their execution. Simulations are carried out in various environments to show the feasibility of this approac en_US
dc.publisher IEEE en_US
dc.relation.isbasedon en_US
dc.title PSO-tuned F2 method for multi-robot navigation en_US
dc.parent 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation USA en_US
dc.identifier.startpage 3765 en_US
dc.identifier.endpage 3770 en_US FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference en_US
dc.conference Verified OK en_US
dc.conference.location San Diego, California, USA en_US
dc.for 091300 en_US
dc.personcode 105612 en_US
dc.personcode 995424 en_US
dc.personcode 000350 en_US
dc.personcode 998044 en_US
dc.personcode 011224 en_US
dc.percentage 100 en_US Mechanical Engineering en_US
dc.classification.type FOR-08 en_US
dc.custom IEEE/RSJ International Conference on Intelligent Robots and Systems en_US 20071029 en_US
dc.location.activity San Diego, California, USA en_US
dc.description.keywords collision avoidance , mobile robots , multi-robot systems , particle swarm optimisation en_US
dc.staffid 011224 en_US

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