Planning under uncertainty using a dynamical systems approach for autonomous vehicles

dc.contributorNiño Vásquez, Luis Fernando
dc.contributorBobadilla, Jaime Leonardo
dc.contributorlaboratorio de Investigación en Sistemas Inteligentes Lisi
dc.creatorBayuelo Sierra, Alfredo José
dc.date.accessioned2022-03-23T18:54:29Z
dc.date.available2022-03-23T18:54:29Z
dc.date.created2022-03-23T18:54:29Z
dc.date.issued2022-03
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/81329
dc.identifierUniversidad Nacional de Colombia
dc.identifierRepositorio Institucional Universidad Nacional de Colombia
dc.identifierhttps://repositorio.unal.edu.co/
dc.description.abstractLa planeación del movimiento para sistemas robóticos (o simplemente robots, o vehículos) es un problema bastante bien estudiado. Resultados significativos se han obtenido en la literatura y se han llevado a la práctica en la industria y otros usos comerciales. Sin embargo, altos costos computacionales y simplificaciones hechas en la formulación de los problemas presentan retos abiertos y oportunidades de investigación. Este trabajo presenta estrategias para ayudar en la solución del problema de navegación, y otros relacionados, en cuatro escenarios: Cuando no se conoce el Modelo que describe el vehículo, No se conoce la posición ni orientación del vehículo, no se conoce el Mapa del lugar, Y cuando no se conoce la intención (aliado/adversario) de otros robots en el ambiente. Primero, se presenta una estrategia que usa ambientes simulados realísticos para superar la falta de modelo del vehículo o las dificulatades que conlleven su cálculo. Los ambientes simulados se han beneficiado de las mejoras en los sistemas computarizados de la última década; por ejempo, los juegos de computadora han progresivamente mostrado ambientes más y más realistas, y estos han sido ya usados para entrenar robots al mostrarle a los sensores del robot esta información como cierta, de tal forma que se logra que los robots aprendan de secuencias del juego, de esta misma forma, en este trabajo se usan los simuladores para ayudar a resolver el problema de la navegación. También se presenta un esquema de planeación basado en la retro alimentación para un sencillo robot que rebota, mostrando cómo dicho robot puede navegar ambientes complejos sin saber su posición en todo momento. Por supuesto el mapa debe ser conocido para crear tal esquema de planeación, cuando no se conoce el mapa, la estrategia conocida como Localización y Mapeo Simultaneos, puede usarse para determinar el mapa alrededor y encontrarse en el mismo. Finalmente, cuando se consideran robots más simples, puede llegar a ser necesario usar más de un robot para cumplir una tarea, y puede que en el ambiente hayan robots adversarios, por lo tanto, se presenta una estrategia que permite comunicarse para evitar colisiones que mantiene la privacidad al mismo tiempo. (Texto tomado de la fuente)
dc.description.abstractThe problem of Motion Planning for Robotic Systems (in this work: robot or vehicles) has been well studied. Some significant outcomes have been accomplished, and good results demonstrated in practical situations in industry and other commercial uses. Nevertheless, high computational cost and several assumptions on the problems present open challenges and opportunities for research. This work presents strategies to help in the solution of the navigation and other related problems for four different scenarios: unknown vehicle model, unknown positions/orientation of the vehicle, unknown map to navigate and unknown intention of other vehicles in the same environment. First, realistic simulation is used to overcome the lack of a model, or the difficulties to calculate it. Simulated environments have taken advantage of the improvements in computer systems in the last decades; for example, computer games have progressively shown more realistic environments, these environments have already been used to train models by fooling the sensors of robots and making them to learn from gameplays, in this fashion, simulators are used here to help solving the navigation problem. It is also presented here a feedback-based motion planer for a simple bouncing robot, showing how it can navigate a complex world even if the current position is not know all the time. Of course the map must be known before hand to create such a plan, for the case where the map is not known a priori, a strategy for simultaneous localization and mapping is presented here to determine the world around and the position of the vehicle in such map. Finally, when considering simpler robots, it might be necessary to use multiple of them to succeed at a particular task, and they might also be in the presence of a third party robot, hence, a strategy is presented here to communicate and avoid collisions while preserving privacy.
dc.languageeng
dc.publisherUniversidad Nacional de Colombia
dc.publisherBogotá - Ingeniería - Doctorado en Ingeniería - Sistemas y Computación
dc.publisherDepartamento de Ingeniería de Sistemas e Industrial
dc.publisherFacultad de Ingeniería
dc.publisherBogotá, Colombia
dc.publisherUniversidad Nacional de Colombia - Sede Bogotá
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dc.rightsReconocimiento 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titlePlanning under uncertainty using a dynamical systems approach for autonomous vehicles
dc.typeTrabajo de grado - Doctorado


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