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dc.contributor.authoramelec, viloriaspa
dc.contributor.authorLizardo Zelaya, Nelson Albertospa
dc.contributor.authorVarela, Noelspa
dc.date.accessioned2021-01-13T21:42:08Z
dc.date.available2021-01-13T21:42:08Z
dc.date.issued2020
dc.identifier.issn1877-0509spa
dc.identifier.urihttps://hdl.handle.net/11323/7685spa
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.isoeng
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universalspa
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/spa
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.typeArtículo de revistaspa
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.2020.07.064spa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
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dc.relation.references[2] Li, R., Noack, B. R., Cordier, L., Borée, J., Kaiser, E., & Harambat, F. (2017). Linear genetic programming control for strongly nonlinear dynamics with frequency crosstalk. arXiv preprint arXiv:1705.00367.spa
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dc.relation.references[7] Yusuf, R., Podusenko, A., Tanev, I., & Shimohara, K. (2018, November). Recognition of mistaken pedal pressing based on pedal pressing behavior by using genetic programming. In 2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS) (pp. 104-108). IEEE.spa
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dc.relation.references[11] Alekseeva, N., Tanev, I., & Shimohara, K. (2019, July). On the Emergence of Oscillations in the Evolved Autosteering of a Car on Slippery Roads. In 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) (pp. 1371-1378). IEEE.spa
dc.relation.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
dc.relation.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.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa


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