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Particle swarm optimization versus genetic algorithm in manipulator trajectory planning

dc.contributor.authorPires, E. J. Solteiro
dc.contributor.authorOliveira, P. B. Moura
dc.contributor.authorTenreiro Machado, J. A.
dc.contributor.authorCunha, J. Boaventura
dc.date.accessioned2019-04-02T11:12:02Z
dc.date.available2019-04-02T11:12:02Z
dc.date.issued2006-09
dc.description.abstractThe aim of this study is the reduction of the computational burden associated with the evolutionary optimization of manipulator trajectory planning. This paper proposes the use of a particle swarm optimization algorithm to generate trajectories for robotic planar manipulators, based on direct kinematics. The design objective is to minimize the ripple in the trajectory time evolution. Several redundant and hyperredundant manipulators are considered. The particle swarm optimization algorithm is compared with genetic algorithm in solving the manipulator trajectory planning problem. Preliminary simulation results are presented.pt_PT
dc.description.versionN/Apt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/13328
dc.language.isoengpt_PT
dc.subjectParticle Swarm Optimizationpt_PT
dc.subjectGenetic Algorithmspt_PT
dc.subjectRoboticspt_PT
dc.subjectTrajectorypt_PT
dc.subjectPlanningpt_PT
dc.subjectOptimizationpt_PT
dc.titleParticle swarm optimization versus genetic algorithm in manipulator trajectory planningpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceLisboa, Portugalpt_PT
oaire.citation.endPage7pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleCONTROLO 2006 - 7th Portuguese Conference on Automatic Controlpt_PT
person.familyNameTenreiro Machado
person.givenNameJ. A.
person.identifier.ciencia-id7A18-4935-5B29
person.identifier.orcid0000-0003-4274-4879
person.identifier.ridM-2173-2013
person.identifier.scopus-author-id55989030100
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication82cd5c17-07b6-492b-b3e3-ecebdad1254f
relation.isAuthorOfPublication.latestForDiscovery82cd5c17-07b6-492b-b3e3-ecebdad1254f

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