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Differential evolution Markov chain filter for global localization

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dc.affiliation.dptoUC3M. Departamento de Ingeniería de Sistemas y Automáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Laboratorio de Robótica (Robotics Lab)es
dc.contributor.authorMoreno Lorente, Luis Enrique
dc.contributor.authorMartín Monar, Fernando
dc.contributor.authorMuñoz, María Luisa
dc.contributor.authorGarrido Bullón, Luis Santiago
dc.contributor.funderComunidad de Madrides
dc.date.accessioned2022-03-21T12:35:35Z
dc.date.available2022-03-21T12:35:35Z
dc.date.issued2016-06-01
dc.description.abstractA key challenge for an autonomous mobile robot is to estimate its location according to the available information. A particular aspect of this task is the global localization problem. In our previous work, we developed an algorithm based on the Differential Evolution method that solves this problem in 2D and 3D environments. The robot’s pose is represented by a set of possible location estimates weighted by a fitness function. The Markov Chain Monte Carlo algorithms have been successfully applied to multiple fields such as econometrics or computing science. It has been demonstrated that they can be combined with the Differential Evolution method to solve efficiently many optimization problems. In this work, we have combined both approaches to develop a global localization filter. The algorithm performance has been tested in simulated and real maps. The population requirements have been reduced when compared to the previous version.en
dc.description.sponsorshipThe research leading to these results has received funding from the RoboCity2030-III-CM project (Robotica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU.en
dc.description.statusPublicadoes
dc.format.extent23
dc.identifier.bibliographicCitationJournal of Intelligent & Robotic Systems, (2016), 82(3-4), pp.: 513–536.en
dc.identifier.doihttps://doi.org/10.1007/s10846-015-0245-8
dc.identifier.issn0921-0296
dc.identifier.publicationfirstpage513
dc.identifier.publicationissue3-4
dc.identifier.publicationlastpage536
dc.identifier.publicationtitleJOURNAL OF INTELLIGENT & ROBOTIC SYSTEMSen
dc.identifier.publicationvolume82
dc.identifier.urihttps://hdl.handle.net/10016/34429
dc.identifier.uxxiAR/0000018546
dc.language.isoengen
dc.publisherSpringer
dc.relation.projectIDComunidad de Madrid. S2013/MIT-2748/RoboCity2030-III-CMes
dc.rights© Springer Science+Business Media Dordrecht 2015en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaRobótica e Informática Industriales
dc.subject.otherDifferential evolutionen
dc.subject.otherMarkov chainen
dc.subject.otherMonte Carloen
dc.subject.otherOptimization methoden
dc.subject.otherGlobal localizationen
dc.subject.otherMobile robotsen
dc.titleDifferential evolution Markov chain filter for global localizationen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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