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Improving CGDA execution through genetic algorithms incorporating spatial and velocity constraints

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dc.affiliation.dptoUC3M. Departamento de Ingeniería de Sistemas y Automáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Laboratorio de Sistemas Inteligenteses
dc.contributor.authorFernández Fernández, Raúl
dc.contributor.authorEstévez Fernández, David
dc.contributor.authorGonzález Víctores, Juan Carlos
dc.contributor.authorBalaguer Bernaldo de Quirós, Carlos
dc.contributor.funderComunidad de Madrides
dc.date.accessioned2022-02-08T11:48:53Z
dc.date.available2022-02-08T11:48:53Z
dc.date.issued2017-04-26
dc.descriptionProceedings of: 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), 26-28 April 2017, Coimbra, Portugal.en
dc.description.abstractIn the Continuous Goal Directed Actions (CGDA) framework, actions are modelled as time series which contain the variations of object and environment features. As robot joint trajectories are not explicitly encoded in CGDA, Evolutionary Algorithms (EA) are used for the execution of these actions. These computations usually require a large number of evaluations. As a consequence of this, these evaluations are performed in a simulated environment, and the computed trajectory is then transferred to the physical robot. In this paper, constraints are introduced in the CGDA framework, as a way to reduce the number of evaluations needed by the system to converge to the optimal robot joint trajectory. Specifically, spatial and velocity constraints are introduced in the framework. Their effects in two different CGDA commonly studied use cases (the “wax” and “paint” actions) are analyzed and compared. The experimental results obtained using these constraints are compared with those obtained with the Steady State Tournament (SST) algorithm used in the original proposal of CGDA. Conclusions extracted from this study depict a high reduction in the required number of evaluations when incorporating spatial constraints. Velocity constraints provide however less promising results, which will be discussed within the context of previous CGDA works.en
dc.description.sponsorshipThe research leading to these results has received funding from the RoboCity2030-III-CM project (Robtica aplicada a la mejora de la calidad de vida de los ciudadanos. fase Ill; S2013IMIT-2748), funded by Program as de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU, and by a FPU grant funded by Miniesterio de Educacion, Cultura y deporte.en
dc.format.extent6
dc.identifier.bibliographicCitationFernandez-Fernandez, R., Estevez, D., Victores, J. G. & Balaguer, C. (26-28 April 2017). Improving CGDA execution through Genetic Algorithms incorporating Spatial and Velocity constraints [proceedings]. 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Coimbra, Portugal.en
dc.identifier.doihttps://doi.org/10.1109/ICARSC.2017.7964090
dc.identifier.isbn978-1-5090-6235-5
dc.identifier.publicationfirstpage290
dc.identifier.publicationlastpage295
dc.identifier.publicationtitle2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)en
dc.identifier.urihttps://hdl.handle.net/10016/34066
dc.identifier.uxxiCC/0000029054
dc.language.isoengen
dc.publisherIEEEen
dc.relation.eventdate2017-04-26
dc.relation.eventplacePortugalen
dc.relation.eventtitle2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)en
dc.relation.projectIDComunidad de Madrid. S2013IMIT-2748es
dc.rights© 2017, IEEE.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaRobótica e Informática Industriales
dc.subject.otherTrajectoryen
dc.subject.otherIonsen
dc.subject.otherPaintsen
dc.subject.otherAngular velocity controlen
dc.subject.otherGenetic algorithmsen
dc.subject.otherOptimal controlen
dc.subject.otherRobotsen
dc.subject.otherTime seriesen
dc.subject.otherTrajectory controlen
dc.titleImproving CGDA execution through genetic algorithms incorporating spatial and velocity constraintsen
dc.typeconference proceedings*
dc.type.hasVersionAM*
dspace.entity.typePublication
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