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dc.creatoramelec, viloria
dc.creatorLizardo Zelaya, Nelson Alberto
dc.creatorVarela, Noel
dc.date.accessioned2021-01-13T21:42:08Z
dc.date.available2021-01-13T21:42:08Z
dc.date.issued2020
dc.identifier.issn1877-0509
dc.identifier.urihttps://hdl.handle.net/11323/7685
dc.description.abstractGenetic Programming (GP) is a population-based evolutionary technique, which, unlike a Genetic Algorithm (GA) does not work on a fixed-length data structure, but on a variable-length structure and aims to evolve functions, models or programs, rather than finding a set of parameters. There are different histories of driver development, so different proposals of the use of PG to evolve driver structures are presented. In the case of an autonomous vehicle, the development of a steering controller is complex in the sense that it is a non-linear system, and the control actions are very limited by the maximum angle allowed by the steering wheels. This paper presents the development of an autonomous vehicle controller with Ackermann steering evolved by means of Genetic Programming.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.sourceProcedia Computer Sciencespa
dc.subjectDesignspa
dc.subjectSimulationspa
dc.subjectVehicle controllersspa
dc.subjectGenetic algorithmsspa
dc.titleDesign and simulation of vehicle controllers through genetic algorithmsspa
dc.typearticlespa
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dcterms.references[12] Vásquez C. et al. (2020) Conglomerates of Bus Rapid Transit in Latin American Countries. In: Pandian A., Ntalianis K., Palanisamy R. (eds) Intelligent Computing, Information and Control Systems. ICICCS 2019. Advances in Intelligent Systems and Computing, vol 1039. Springer, Chamspa
dcterms.references[13] van Lon, R. R., Branke, J., & Holvoet, T. (2018). Optimizing agents with genetic programming: an evaluation of hyper-heuristics in dynamic real-time logistics. Genetic programming and evolvable machines, 19(1-2), 93-120.spa
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dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.source.urlhttps://www.sciencedirect.com/science/article/pii/S1877050920317452spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1016/j.procs.2020.07.064


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