Multiagent Control of Autonomous Vehicles in Presence of Non-Cooperative Agents using Game Theory

ilustraciones, fotografías a color

Autores:: Ospina Gaitan, Nestor Ivan

Tipo de recurso:

Fecha de publicación:: 2023

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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dc.title.eng.fl_str_mv	Multiagent Control of Autonomous Vehicles in Presence of Non-Cooperative Agents using Game Theory
dc.title.translated.spa.fl_str_mv	Control multiagente de vehículos autónomos en presencia de agentes no cooperativos utilizando teoría de juegos
title	Multiagent Control of Autonomous Vehicles in Presence of Non-Cooperative Agents using Game Theory
spellingShingle	Multiagent Control of Autonomous Vehicles in Presence of Non-Cooperative Agents using Game Theory 620 - Ingeniería y operaciones afines 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Conducción de automóviles Ingeniería del tránsito Automobile driving Traffic engineering Generalized Mixed-Integer Potential Game Optimal Control Model Predictive Control Autonomous Driving Decentralized Network Teoría de juegos potenciales con enteros mixtos Control optimo Control predictivo de modelo Conducción autónoma Red descentralizada
title_short	Multiagent Control of Autonomous Vehicles in Presence of Non-Cooperative Agents using Game Theory
title_full	Multiagent Control of Autonomous Vehicles in Presence of Non-Cooperative Agents using Game Theory
title_fullStr	Multiagent Control of Autonomous Vehicles in Presence of Non-Cooperative Agents using Game Theory
title_full_unstemmed	Multiagent Control of Autonomous Vehicles in Presence of Non-Cooperative Agents using Game Theory
title_sort	Multiagent Control of Autonomous Vehicles in Presence of Non-Cooperative Agents using Game Theory
dc.creator.fl_str_mv	Ospina Gaitan, Nestor Ivan
dc.contributor.advisor.none.fl_str_mv	Mojica Nava, Eduardo Alirio Téllez Castro, Duván Andrés
dc.contributor.author.none.fl_str_mv	Ospina Gaitan, Nestor Ivan
dc.contributor.researchgroup.spa.fl_str_mv	Programa de Investigacion sobre Adquisicion y Analisis de Señales Paas-Un
dc.contributor.researchgate.spa.fl_str_mv	Ospina Gaitan, Nestor Ivan
dc.contributor.googlescholar.spa.fl_str_mv	NI Ospina
dc.subject.ddc.spa.fl_str_mv	620 - Ingeniería y operaciones afines 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
topic	620 - Ingeniería y operaciones afines 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Conducción de automóviles Ingeniería del tránsito Automobile driving Traffic engineering Generalized Mixed-Integer Potential Game Optimal Control Model Predictive Control Autonomous Driving Decentralized Network Teoría de juegos potenciales con enteros mixtos Control optimo Control predictivo de modelo Conducción autónoma Red descentralizada
dc.subject.lemb.spa.fl_str_mv	Conducción de automóviles Ingeniería del tránsito
dc.subject.lemb.eng.fl_str_mv	Automobile driving Traffic engineering
dc.subject.proposal.eng.fl_str_mv	Generalized Mixed-Integer Potential Game Optimal Control Model Predictive Control Autonomous Driving Decentralized Network
dc.subject.proposal.spa.fl_str_mv	Teoría de juegos potenciales con enteros mixtos Control optimo Control predictivo de modelo Conducción autónoma Red descentralizada
description	ilustraciones, fotografías a color
publishDate	2023
dc.date.accessioned.none.fl_str_mv	2023-03-31T16:32:58Z
dc.date.available.none.fl_str_mv	2023-03-31T16:32:58Z
dc.date.issued.none.fl_str_mv	2023-03-08
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	DataPaper Text
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status_str	acceptedVersion
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dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/83682 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.references.spa.fl_str_mv	Alrifaee, Bassam: Networked Model Predictive Control for Vehicle Collision Avoidance, Tesis de Grado, 05 2017 Alrifaee, Bassam ; Ghanbarpour, Masoumeh ; Abel, Dirk: Centralized Non-Convex Model Predictive Control for Cooperative Collision Avoidance of Networked Vehicles, 2014 Alrifaee, Bassam ; Maczijewski, Janis ; Abel, Dirk: Sequential Convex Programming MPC for Dynamic Vehicle Collision Avoidance, 2017 Bauso, Dario: Game Theory: Models, Numerical Methods and Applications. En:Foundations and Trends in Systems and Control 1 (2014), 10, p. 379–522 Bemporad, Alberto ; Morari, Manfred: Control of systems integrating logic, dynamics, and constraints. En: Automatica 35 (1999), Nr. 3, p. 407–427. Bennett, S.: A brief history of automatic control. En: IEEE Control Systems Magazine 16 (1996), Nr. 3, p. 17–25 Brock, Oliver ; Khatib, Oussama: High-Speed Navigation Using the Global Dynamic Window Approach. (2000), 01 Carona, Ricardo ; Aguiar, A P. ; Gaspar, Jose: Control of unicycle type robots tracking, path following and point stabilization. (2008) Cesa-Bianchi, Nicol`o ; Lugosi, G ́abor: Prediction, Learning, and Games. 2006 Cheng, Shuo ; Li, Liang ; Chen, Xiang ; nan Fang, Sheng ; yu Wang, Xiang ; heng Wu, Xiu ; bing Li, Wei: Longitudinal autonomous driving based on game theory for intelligent hybrid electric vehicles with connectivity. En: Applied Energy 268 (2020). Dogra, Anutusha ; Jha, Rakesh K. ; Jain, Shubha: A Survey on Beyond 5G Network With the Advent of 6G: Architecture and Emerging Technologies. Dunbar, William: Distributed Receding Horizon Control of Multiagent Systems. (2004) Dunbar, William ; Murray, Richard: Distributed receding horizon control for multivehicle formation stabilization. En: Automatica 42 (2006) abiani, Filippo ; Grammatico, Sergio: Multi-Vehicle Automated Driving as a Generalized Mixed-Integer Potential Game. En: IEEE Transactions on Intelligent Transportation Systems PP (2019) Facchinei, F. ; Kanzow, Christian: Generalized Nash equilibrium problems. En: Annals of Operations Research 175 (2010) Facchinei, Francisco ; Pang, Jong-Shi: Nash equilibria: The variational approach. En: Convex Optimization in Signal Processing and Communications (2009 Facchinei, Francisco ; Piccialli, Veronica: Decomposition algorithms for generalized potential games. En: Computational Optimization and Applications 50 (2011) Fudenberg, Drew ; Tirole, Jean: Game Theory. Cambridge, MA : MIT Press, 1991.– Translated into Chinesse by Renin University Press, Bejing: China. Garcia, Carlos E. ; Prett, David M. ; Morari, Manfred: Model predictive control: Theory and practice—A survey. En: Automatica 25 (1989), Nr. 3, p. 335–348. Garrido-Jurado, Sergio ; Mu ̃noz-Salinas, Rafael ; Madrid-Cuevas, Francisco J. ; Mar ́ın-Jim ́enez, Manuel J.: Automatic generation and detection of highly reliable fiducial markers under occlusion. Gehrig, S. K. ; Stein, F. J.: Dead reckoning and cartography using stereo vision for an autonomous car. En: Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289) Vol. 3, 1999 Hespanha, Joao: Noncooperative Game Theory: An Introduction for Engineers and Computer Scientists. 2017. Hu, J. ; Bhowmick, P. ; Arvin, F. ; Lanzon, A. ; Lennox, B.: Cooperative Control of Heterogeneous Connected Vehicle Platoons: An Adaptive Leader Following Approach. En: IEEE Robotics and Automation Letters 5 (2020) Jr, John ; Nash, John: Two-Person Cooperative Game. En: Econometrica 21 (1953), 02, p. 128–140 Leyton-Brown, Kevin ; Shoham, Yoav: Essentials of Game Theory: A Concise Multidisciplinary Introduction. Vol. 2. 2008. Maestre, J.M.: Distributed Model Predictive Control Based on Game Theory, Tesis de Grado, 10 2010 Marden, Jason R. ; Shamma, Jeff S.: Game Theory and Control. En: Annual Review of Control, Robotics, and Autonomous Systems 1 (2018), Nr. 1, p. 105–134. Mayne, D.Q. ; Rawlings, J.B. ; Rao, C.V. ; Scokaert, P.O.M.: Constrained model predictive control: Stability and optimality. En: Automatica 36 (2000), Nr. 6, p. 789–814. Mohseni, Fatemeh ; Frisk, Erik ; ̊Aslund, Jan ; Nielsen, Lars: Distributed Model Predictive Control for Highway Maneuvers. En: IFAC-PapersOnLine 50 (2017 Moler., Clever: Matlab Optimizacion Toolbox,. (2022) MYERSON, ROGER B.: Game Theory: Analysis of Conflict. Harvard University Press, 1991 Nash, John: Equilibrium Points in N-Person Games. En: Proceedings of the National Academy of Sciences of the United States of America 36 (1950) Nisan, Noam ; Roughgarden, Tim ; Tardos, ́Eva ; Vazirani, Vijay: Algorithmic Game Theory. 2007 Optimization., LLC G.: gurobi optimizer reference manual,. (2021) Osborne, Martin ; Rubinstein, Ariel: A course in Game Theory. Vol. 63. 1994 Ospina Gaitan, Nestor ; Mojica-Nava, Eduardo ; Jaimes, L.G. ; Calderon, Juan: ARGroHBotS: An Affordable and Replicable Ground Homogeneous Robot Swarm Testbed. En: IFAC-PapersOnLine 54 (2021), 01, p. 256–261 Sagratella, Simone: Algorithms for generalized potential games with mixed-integer variables. En: Computational Optimization and Applications 68 (2017) sar, Tamer ; Olsder, G.J.: Dynamic Noncooperative Game Theory. 1995 Shakey, Peter H.: the world’s first mobile, intelligent robot. 2015 Taeihagh, Araz ; Lim, Hazel Si M.: Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks. En: Transport Reviews 39 (2018), Jul, Nr. 1, p. 103–128 Thrun, S.: Toward robotic cars. En: Commun. ACM 53 (2010), p. 99–106 Valencia, Felipe ; Pati ̃no, Julian: Game Theory Based Distributed Model Predictive Control for a Hydro-Power Valley Control, 2013. Worthmann, Karl ; Mehrez, Mohamed ; Zanon, Mario ; Mann, G.K.I. ; Gosine, Ray ; Diehl, Moritz: Model Predictive Control of Nonholonomic Mobile Robots Without Stabilizing Constraints and Costs. Yang, Xue ; Liu, Jie ; Zhao, Feng ; Vaidya, Nitin: A Vehicle-to-Vehicle Communication Protocol for Cooperative Collision Warning., 2004, p. 114–123
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dc.format.extent.spa.fl_str_mv	xiv, 87 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ingeniería - Maestría en Ingeniería - Automatización Industrial
dc.publisher.faculty.spa.fl_str_mv	Facultad de Administración
dc.publisher.place.spa.fl_str_mv	Bogotá,Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Mojica Nava, Eduardo Alirio609c35fb4a7e288ee81a2ef0fb802397600Téllez Castro, Duván Andrés41119cb00ab4b3c16ad1afe543e48611Ospina Gaitan, Nestor Ivan1dc43cda9092bf3d7854eb45f7ac8929Programa de Investigacion sobre Adquisicion y Analisis de Señales Paas-UnOspina Gaitan, Nestor IvanNI Ospina2023-03-31T16:32:58Z2023-03-31T16:32:58Z2023-03-08https://repositorio.unal.edu.co/handle/unal/83682Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, fotografías a colorThis thesis proposes a solution to the problem of autonomous vehicle driving in a road environment, specifically in the presence of agent-driven vehicles with selfish decisions and aggressive maneuvers. The controller tries to solve the optimization problem using a \textit{Model Predictive Control}(MPC). Taking advantage of the previous technique for trajectory prediction, the controller uses this to better predict the neighbors' position and plan its trajectory. In addition, the predictive model can solve the \textit{Optimal Control Problem} by complying with security restrictions, avoiding obstacles, and achieving its primary objective. The \textit{Optimal Control Problem} has non-convex constraints due to its based on mixed-integer variables. By creating non-linear MPC that can deal with the problem of hybrid variables, it is sought to solve the problem of driving vehicles against aggressive and non-cooperative decisions for the network. Furthermore, all agents in the system can be controlled by creating local controllers based on \textit{Game Theory}. We analyzed two methods to find an optimal solution: centralized and decentralized. The most effective and viable controller is chosen after objective research and comparison of all others. Since the centralized MPC provides the best solution for the entire plant, it is used as a benchmark. The first decomposed algorithm is centralized MPC, in which the neighboring subsystems give the information to the central node, calculate the new routes and transmit in each iteration of the MPC. The second approach is based on optimal distributed decentralized MPC. The cars are based on the \textit{Generalized Potential Game theory} in both cases. Each agent solves its problem sequentially and shares its next move with neighbors, looking for a $\epsilon$-Nash equilibrium. Both drivers can feasibly calculate their trajectory by relying on additional constraints while avoiding other vehicles. Distributed controllers are evaluated in three different scenarios, using three criteria: the efficiency of the global controller, the time it takes for each controller to find an answer, and the feasibility of the controller with the increase in steps that the controller must predict. The first scenario gives an idea of the controller's behavior against agents with unknown maneuvers; the second shows the controller's behavior against increased constraints and connections with neighbors, and the third tests the controller by reducing its environmental variables. (Texto tomado de la fuente)Esta tesis propone una solución al problema de la conducción autónoma de vehículos en un entorno vial, concretamente en presencia de vehículos conducidos por agentes con decisiones egoístas y maniobras agresivas. El controlador trata de resolver el problema de optimización basado en un Control Predictivo de Modelo (MPC). Aprovechando la técnica anterior de predicción de trayectoria, el controlador la utiliza para predecir mejor la posición de los vecinos y planificar su trayectoria. Además, el modelo predictivo puede resolver el problema de control óptimo al cumplir con las restricciones de seguridad, evitar obstáculos y lograr su objetivo principal. El problema de control óptimo tiene restricciones no convexas debido a las variables enteras mixtas en las que se basa. Mediante la creación de MPC no lineales que puedan lidiar con el problema de las variables híbridas, se busca resolver el problema de conducción de vehículos frente a decisiones agresivas y no cooperativas para la red. Además, todos los agentes del sistema pueden controlarse mediante la creación de controladores locales basados en \textit{Teoría de Juegos}. Analizamos dos métodos para encontrar una solución óptima: centralizado y descentralizado. El controlador más eficaz y viable se elige después de una investigación objetiva y la comparación de todos los demás. Dado que el MPC proporciona la mejor solución para toda la planta, se utiliza como punto de referencia. El primer algoritmo descompuesto es MPC centralizado, en el que los subsistemas vecinos entregan la información al nodo central, calculan las nuevas rutas y transmiten en cada iteración del controlador por MPC. El segundo enfoque se basa en MPC descentralizado distribuido óptimo. Los coches se basan en la teoría del Juego de Potencial Generalizado en ambos casos. Cada agente resuelve su problema secuencialmente y comparte su próximo movimiento con los vecinos, buscando un equilibrio $\epsilon$-Nash. Ambos conductores pueden calcular su trayectoria de manera factible confiando en restricciones adicionales mientras evitan otros vehículos. Los controladores distribuidos se evalúan en tres escenarios diferentes, utilizando tres criterios: la eficiencia del controlador global, el tiempo que tarda cada controlador en encontrar una respuesta y la viabilidad del controlador con el aumento de pasos que el controlador debe predecir. El primer escenario da una idea del comportamiento del controlador frente a agentes con maniobras desconocidas; el segundo muestra el comportamiento del controlador frente a mayores restricciones y conexiones con vecinos, y el tercero prueba el controlador reduciendo sus variables ambientales.MaestríaMagíster en Ingeniería - Automatización IndustrialControlRoboticsxiv, 87 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Automatización IndustrialFacultad de AdministraciónBogotá,ColombiaUniversidad Nacional de Colombia - Sede Bogotá620 - Ingeniería y operaciones afines620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaConducción de automóvilesIngeniería del tránsitoAutomobile drivingTraffic engineeringGeneralized Mixed-Integer Potential GameOptimal ControlModel Predictive ControlAutonomous DrivingDecentralized NetworkTeoría de juegos potenciales con enteros mixtosControl optimoControl predictivo de modeloConducción autónomaRed descentralizadaMultiagent Control of Autonomous Vehicles in Presence of Non-Cooperative Agents using Game TheoryControl multiagente de vehículos autónomos en presencia de agentes no cooperativos utilizando teoría de juegosTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionDataPaperTexthttp://purl.org/redcol/resource_type/TMAlrifaee, Bassam: Networked Model Predictive Control for Vehicle Collision Avoidance, Tesis de Grado, 05 2017Alrifaee, Bassam ; Ghanbarpour, Masoumeh ; Abel, Dirk: Centralized Non-Convex Model Predictive Control for Cooperative Collision Avoidance of Networked Vehicles, 2014Alrifaee, Bassam ; Maczijewski, Janis ; Abel, Dirk: Sequential Convex Programming MPC for Dynamic Vehicle Collision Avoidance, 2017Bauso, Dario: Game Theory: Models, Numerical Methods and Applications. En:Foundations and Trends in Systems and Control 1 (2014), 10, p. 379–522Bemporad, Alberto ; Morari, Manfred: Control of systems integrating logic, dynamics, and constraints. En: Automatica 35 (1999), Nr. 3, p. 407–427.Bennett, S.: A brief history of automatic control. En: IEEE Control Systems Magazine 16 (1996), Nr. 3, p. 17–25Brock, Oliver ; Khatib, Oussama: High-Speed Navigation Using the Global Dynamic Window Approach. (2000), 01Carona, Ricardo ; Aguiar, A P. ; Gaspar, Jose: Control of unicycle type robots tracking, path following and point stabilization. (2008)Cesa-Bianchi, Nicol`o ; Lugosi, G ́abor: Prediction, Learning, and Games. 2006Cheng, Shuo ; Li, Liang ; Chen, Xiang ; nan Fang, Sheng ; yu Wang, Xiang ; heng Wu, Xiu ; bing Li, Wei: Longitudinal autonomous driving based on game theory for intelligent hybrid electric vehicles with connectivity. En: Applied Energy 268 (2020).Dogra, Anutusha ; Jha, Rakesh K. ; Jain, Shubha: A Survey on Beyond 5G Network With the Advent of 6G: Architecture and Emerging Technologies.Dunbar, William: Distributed Receding Horizon Control of Multiagent Systems. (2004)Dunbar, William ; Murray, Richard: Distributed receding horizon control for multivehicle formation stabilization. En: Automatica 42 (2006)abiani, Filippo ; Grammatico, Sergio: Multi-Vehicle Automated Driving as a Generalized Mixed-Integer Potential Game. En: IEEE Transactions on Intelligent Transportation Systems PP (2019)Facchinei, F. ; Kanzow, Christian: Generalized Nash equilibrium problems. En: Annals of Operations Research 175 (2010)Facchinei, Francisco ; Pang, Jong-Shi: Nash equilibria: The variational approach. En: Convex Optimization in Signal Processing and Communications (2009Facchinei, Francisco ; Piccialli, Veronica: Decomposition algorithms for generalized potential games. En: Computational Optimization and Applications 50 (2011)Fudenberg, Drew ; Tirole, Jean: Game Theory. Cambridge, MA : MIT Press, 1991.– Translated into Chinesse by Renin University Press, Bejing: China.Garcia, Carlos E. ; Prett, David M. ; Morari, Manfred: Model predictive control: Theory and practice—A survey. En: Automatica 25 (1989), Nr. 3, p. 335–348.Garrido-Jurado, Sergio ; Mu ̃noz-Salinas, Rafael ; Madrid-Cuevas, Francisco J. ; Mar ́ın-Jim ́enez, Manuel J.: Automatic generation and detection of highly reliable fiducial markers under occlusion.Gehrig, S. K. ; Stein, F. J.: Dead reckoning and cartography using stereo vision for an autonomous car. En: Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289) Vol. 3, 1999Hespanha, Joao: Noncooperative Game Theory: An Introduction for Engineers and Computer Scientists. 2017.Hu, J. ; Bhowmick, P. ; Arvin, F. ; Lanzon, A. ; Lennox, B.: Cooperative Control of Heterogeneous Connected Vehicle Platoons: An Adaptive Leader Following Approach. En: IEEE Robotics and Automation Letters 5 (2020)Jr, John ; Nash, John: Two-Person Cooperative Game. En: Econometrica 21 (1953), 02, p. 128–140Leyton-Brown, Kevin ; Shoham, Yoav: Essentials of Game Theory: A Concise Multidisciplinary Introduction. Vol. 2. 2008.Maestre, J.M.: Distributed Model Predictive Control Based on Game Theory, Tesis de Grado, 10 2010Marden, Jason R. ; Shamma, Jeff S.: Game Theory and Control. En: Annual Review of Control, Robotics, and Autonomous Systems 1 (2018), Nr. 1, p. 105–134.Mayne, D.Q. ; Rawlings, J.B. ; Rao, C.V. ; Scokaert, P.O.M.: Constrained model predictive control: Stability and optimality. En: Automatica 36 (2000), Nr. 6, p. 789–814.Mohseni, Fatemeh ; Frisk, Erik ; ̊Aslund, Jan ; Nielsen, Lars: Distributed Model Predictive Control for Highway Maneuvers. En: IFAC-PapersOnLine 50 (2017Moler., Clever: Matlab Optimizacion Toolbox,. (2022)MYERSON, ROGER B.: Game Theory: Analysis of Conflict. Harvard University Press, 1991Nash, John: Equilibrium Points in N-Person Games. En: Proceedings of the National Academy of Sciences of the United States of America 36 (1950)Nisan, Noam ; Roughgarden, Tim ; Tardos, ́Eva ; Vazirani, Vijay: Algorithmic Game Theory. 2007Optimization., LLC G.: gurobi optimizer reference manual,. (2021)Osborne, Martin ; Rubinstein, Ariel: A course in Game Theory. Vol. 63. 1994Ospina Gaitan, Nestor ; Mojica-Nava, Eduardo ; Jaimes, L.G. ; Calderon, Juan: ARGroHBotS: An Affordable and Replicable Ground Homogeneous Robot Swarm Testbed. En: IFAC-PapersOnLine 54 (2021), 01, p. 256–261Sagratella, Simone: Algorithms for generalized potential games with mixed-integer variables. En: Computational Optimization and Applications 68 (2017)sar, Tamer ; Olsder, G.J.: Dynamic Noncooperative Game Theory. 1995Shakey, Peter H.: the world’s first mobile, intelligent robot. 2015Taeihagh, Araz ; Lim, Hazel Si M.: Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks. En: Transport Reviews 39 (2018), Jul, Nr. 1, p. 103–128Thrun, S.: Toward robotic cars. En: Commun. ACM 53 (2010), p. 99–106Valencia, Felipe ; Pati ̃no, Julian: Game Theory Based Distributed Model Predictive Control for a Hydro-Power Valley Control, 2013.Worthmann, Karl ; Mehrez, Mohamed ; Zanon, Mario ; Mann, G.K.I. ; Gosine, Ray ; Diehl, Moritz: Model Predictive Control of Nonholonomic Mobile Robots Without Stabilizing Constraints and Costs.Yang, Xue ; Liu, Jie ; Zhao, Feng ; Vaidya, Nitin: A Vehicle-to-Vehicle Communication Protocol for Cooperative Collision Warning., 2004, p. 114–123EstudiantesInvestigadoresORIGINAL1012417916.2023.pdf1012417916.2023.pdfTesis de Maestria en Ingenieria - Automatizacion Industrialapplication/pdf39287761https://repositorio.unal.edu.co/bitstream/unal/83682/4/1012417916.2023.pdfded0fbeedd91173c47cbb2b20310ec12MD54LICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/83682/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53THUMBNAIL1012417916.2023.pdf.jpg1012417916.2023.pdf.jpgGenerated 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