Diseño y simulación de un controlador inteligente utilizando aprendizaje por refuerzo Q-learning para la navegación autónoma de dos robots móviles.

Trajectory planning in autonomous mobile robots is an open problem because, when working in dynamic environments, it is very expensive to program the entire navigation system for a particular application or, failing that, it would be very difficult for the programmer to correctly predict the changes...

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Detalles Bibliográficos
Autor principal:	Carreño Puentes, Sergio Manuel
Otros Autores:	Erazo Ordoñez, Christian
Formato:	Trabajo de grado (Pregrado y/o Especialización)
Lenguaje:	spa
Publicado:	Universidad Antonio Nariño 2022
Materias:	Aprendizaje por refuerzo, aprendizaje automático, aprendizaje profundo, neurona artificial, redes neuronales, robots autónomos Reinforcement learning, machine learning, deep learning, artificial neuron, neural networks, autonomous robots.
Acceso en línea:	http://repositorio.uan.edu.co/handle/123456789/7242
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Descripción
Sumario:	Trajectory planning in autonomous mobile robots is an open problem because, when working in dynamic environments, it is very expensive to program the entire navigation system for a particular application or, failing that, it would be very difficult for the programmer to correctly predict the changes in the environment. In order to contribute to this field, this document shows the process of designing an intelligent controller based on reinforced learning and more specifically using the Q-learning algorithm to drive two mobile robots through a simulated environment, and that autonomously manage to learn the trajectory that will take them to a target position without having prior knowledge about the work environment