Design and simulation of vehicle controllers through genetic algorithms
Genetic 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 differ...
- Autores:
-
amelec, viloria
Lizardo Zelaya, Nelson Alberto
Varela, Noel
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7685
- Acceso en línea:
- https://hdl.handle.net/11323/7685
https://doi.org/10.1016/j.procs.2020.07.064
https://repositorio.cuc.edu.co/
- Palabra clave:
- Design
Simulation
Vehicle controllers
Genetic algorithms
- Rights
- openAccess
- License
- CC0 1.0 Universal
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| dc.title.spa.fl_str_mv |
Design and simulation of vehicle controllers through genetic algorithms |
| title |
Design and simulation of vehicle controllers through genetic algorithms |
| spellingShingle |
Design and simulation of vehicle controllers through genetic algorithms Design Simulation Vehicle controllers Genetic algorithms |
| title_short |
Design and simulation of vehicle controllers through genetic algorithms |
| title_full |
Design and simulation of vehicle controllers through genetic algorithms |
| title_fullStr |
Design and simulation of vehicle controllers through genetic algorithms |
| title_full_unstemmed |
Design and simulation of vehicle controllers through genetic algorithms |
| title_sort |
Design and simulation of vehicle controllers through genetic algorithms |
| dc.creator.fl_str_mv |
amelec, viloria Lizardo Zelaya, Nelson Alberto Varela, Noel |
| dc.contributor.author.spa.fl_str_mv |
amelec, viloria Lizardo Zelaya, Nelson Alberto Varela, Noel |
| dc.subject.spa.fl_str_mv |
Design Simulation Vehicle controllers Genetic algorithms |
| topic |
Design Simulation Vehicle controllers Genetic algorithms |
| description |
Genetic 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. |
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2020 |
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2020 |
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2021-01-13T21:42:08Z |
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2021-01-13T21:42:08Z |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://purl.org/coar/resource_type/c_6501 |
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Text |
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http://purl.org/redcol/resource_type/ART |
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1877-0509 |
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https://hdl.handle.net/11323/7685 |
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https://doi.org/10.1016/j.procs.2020.07.064 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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1877-0509 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/7685 https://doi.org/10.1016/j.procs.2020.07.064 https://repositorio.cuc.edu.co/ |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
[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, Cham. [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. [3] Li, R. (2017). Aerodynamic Drag Reduction of a Square-Back Car Model Using Linear Genetic Programming and Physic-Based Control (Doctoral dissertation). [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), 103. [5] Hein, D., Udluft, S., & Runkler, T. A. (2018). Interpretable policies for reinforcement learning by genetic programming. Engineering Applications of Artificial Intelligence, 76, 158-169. [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, Cham. [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. [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, 41-53. [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, 116476. [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), 1210-1226. [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. [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, Cham [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. [14] Boslough, M. (2017, March). Autonomous dynamic soaring. In 2017 IEEE Aerospace Conference (pp. 1-20). IEEE. [15] Mrugala, K., Tuptuk, N., & Hailes, S. (2017). Evolving attackers against wireless sensor networks using genetic programming. IET Wireless Sensor Systems, 7(4), 113-122. [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, Singapore. |
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CC0 1.0 Universal |
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http://creativecommons.org/publicdomain/zero/1.0/ |
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Corporación Universidad de la Costa |
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Procedia Computer Science |
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amelec, viloria2f22a05451ff1bbfc2d4dd00035c952fLizardo Zelaya, Nelson Alberto7745fa78c84a95af117fd9a36213eaaaVarela, Noel544417e3ea23421c46114ee4f01f436a2021-01-13T21:42:08Z2021-01-13T21:42:08Z20201877-0509https://hdl.handle.net/11323/7685https://doi.org/10.1016/j.procs.2020.07.064Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Genetic 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.application/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Procedia Computer Sciencehttps://www.sciencedirect.com/science/article/pii/S1877050920317452DesignSimulationVehicle controllersGenetic algorithmsDesign and simulation of vehicle controllers through genetic algorithmsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[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, Cham.[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.[3] Li, R. (2017). Aerodynamic Drag Reduction of a Square-Back Car Model Using Linear Genetic Programming and Physic-Based Control (Doctoral dissertation).[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), 103.[5] Hein, D., Udluft, S., & Runkler, T. A. (2018). Interpretable policies for reinforcement learning by genetic programming. Engineering Applications of Artificial Intelligence, 76, 158-169.[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, Cham.[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.[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, 41-53.[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, 116476.[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), 1210-1226.[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.[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, Cham[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.[14] Boslough, M. (2017, March). Autonomous dynamic soaring. In 2017 IEEE Aerospace Conference (pp. 1-20). IEEE.[15] Mrugala, K., Tuptuk, N., & Hailes, S. (2017). Evolving attackers against wireless sensor networks using genetic programming. IET Wireless Sensor Systems, 7(4), 113-122.[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, Singapore.ORIGINALDesign and simulation of vehicle controllers through genetic algorithms.pdfDesign and simulation of vehicle controllers through genetic algorithms.pdfapplication/pdf625854https://repositorio.cuc.edu.co/bitstream/11323/7685/1/Design%20and%20simulation%20of%20vehicle%20controllers%20through%20genetic%20algorithms.pdf483887b2b8ac56ffa59c0b267ef6a8a5MD51open accessCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstream/11323/7685/2/license_rdf42fd4ad1e89814f5e4a476b409eb708cMD52open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstream/11323/7685/3/license.txte30e9215131d99561d40d6b0abbe9badMD53open accessTHUMBNAILDesign and simulation of vehicle controllers through genetic algorithms.pdf.jpgDesign and simulation of vehicle controllers through genetic 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