Show simple item record

dc.contributor.authoramelec, viloria
dc.contributor.authorLizardo Zelaya, Nelson Alberto
dc.contributor.authorVarela, Noel
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
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universal*
dc.sourceProcedia Computer Sciencespa
dc.subjectVehicle controllersspa
dc.subjectGenetic algorithmsspa
dc.titleDesign and simulation of vehicle controllers through genetic algorithmsspa
dc.typeArtículo de revistaspa
dcterms.references[1] Kasparavičiūtė, G., Nielsen, S. A., Boruah, D., Nordin, P., & Dancu, A. (2018, July). Plastic Grabber: Underwater Autonomous Vehicle Simulation for Plastic Objects Retrieval Using Genetic Programming. In International Conference on Business Information Systems (pp. 527- 533). Springer,
dcterms.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
dcterms.references[3] Li, R. (2017). Aerodynamic Drag Reduction of a Square-Back Car Model Using Linear Genetic Programming and Physic-Based Control (Doctoral dissertation).spa
dcterms.references[4] Li, R., Noack, B. R., Cordier, L., Borée, J., & Harambat, F. (2017). Drag reduction of a car model by linear genetic programming control. Experiments in Fluids, 58(8),
dcterms.references[5] Hein, D., Udluft, S., & Runkler, T. A. (2018). Interpretable policies for reinforcement learning by genetic programming. Engineering Applications of Artificial Intelligence, 76,
dcterms.references[6] Bartczuk, Ł., Łapa, K., & Koprinkova-Hristova, P. (2016, June). A new method for generating of fuzzy rules for the nonlinear modelling based on semantic genetic programming. In International Conference on Artificial Intelligence and Soft Computing (pp. 262-278). Springer,
dcterms.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).
dcterms.references[8] Ji, X., He, X., Lv, C., Liu, Y., & Wu, J. (2018). Adaptive-neural-network-based robust lateral motion control for autonomous vehicle at driving limits. Control Engineering Practice, 76,
dcterms.references[9] Phan, D., Bab-Hadiashar, A., Lai, C. Y., Crawford, B., Hoseinnezhad, R., Jazar, R. N., & Khayyam, H. (2020). Intelligent energy management system for conventional autonomous vehicles. Energy, 191,
dcterms.references[10] Lam, A. Y., Leung, Y. W., & Chu, X. (2016). Autonomous-vehicle public transportation system: scheduling and admission control. IEEE Transactions on Intelligent Transportation Systems, 17(5),
dcterms.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).
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),
dcterms.references[14] Boslough, M. (2017, March). Autonomous dynamic soaring. In 2017 IEEE Aerospace Conference (pp. 1-20).
dcterms.references[15] Mrugala, K., Tuptuk, N., & Hailes, S. (2017). Evolving attackers against wireless sensor networks using genetic programming. IET Wireless Sensor Systems, 7(4),
dcterms.references[16] Viloria A. et al. (2019) Analyzing and Predicting Power Consumption Profiles Using Big Data. In: Wang G., Bhuiyan M., De Capitani di Vimercati S., Ren Y. (eds) Dependability in Sensor, Cloud, and Big Data Systems and Applications. DependSys 2019. Communications in Computer and Information Science, vol 1123. Springer,

Files in this item


This item appears in the following Collection(s)

  • Artículos científicos [2634]
    Artículos de investigación publicados por miembros de la comunidad universitaria.

Show simple item record

CC0 1.0 Universal
Except where otherwise noted, this item's license is described as CC0 1.0 Universal