Ranked pareto particle swarm optimization for mobile robot motion planning

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dc.contributor.author Wang, Dalong en_US
dc.contributor.author Kwok, Ngai en_US
dc.contributor.author Liu, Dikai en_US
dc.contributor.author Ha, Quang en_US
dc.contributor.editor Dikai Liu, Lingfeng Wang, Kay Chen Tan en_US
dc.date.accessioned 2010-05-28T09:38:27Z
dc.date.available 2010-05-28T09:38:27Z
dc.date.issued 2009 en_US
dc.identifier 2009001116 en_US
dc.identifier.citation Wang Dalong et al. 2009, 'Ranked pareto particle swarm optimization for mobile robot motion planning', in http://dx.doi.org/10.1007/978-3-540-89933-4_5 (ed.), Springer, Springer-Verlag Berlin Heidelberg, pp. 97-118. en_US
dc.identifier.issn 978-3-540-89932-7 en_US
dc.identifier.other B1 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. en_US
dc.language English en_US
dc.publisher Springer en_US
dc.relation.isbasedon http://dx.doi.org/10.1007/978-3-540-89933-4_5 en_US
dc.title Ranked pareto particle swarm optimization for mobile robot motion planning en_US
dc.parent Design and Control of Intelligent Robotic Systems en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation Springer-Verlag Berlin Heidelberg en_US
dc.identifier.startpage 97 en_US
dc.identifier.endpage 118 en_US
dc.cauo.name FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.for 090602 en_US
dc.personcode 105612 en_US
dc.personcode 995424 en_US
dc.personcode 000350 en_US
dc.personcode 000935 en_US
dc.percentage 100 en_US
dc.classification.name Control Systems, Robotics and Automation en_US
dc.classification.type FOR-08 en_US
dc.edition 2009 en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords NA en_US
dc.staffid en_US
dc.staffid 000935 en_US

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