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dc.contributor.advisorReger, Johann
dc.contributor.authorGrebner, Anna-Maria Stephaniees_ES
dc.description.abstractNavigation and obstacle avoidance are important tasks in the research field of au- tonomous mobile robots. The challenge tackled in this work is the navigation of a 4- wheeled car-type robot to a desired parking position while avoiding obstacles on the way. The taken approach to solve this problem is based on neural fuzzy techniques. Earlier works resulted in a controller to navigate the robot in a clear environment. It is extended by considering additional parameters in the training process. The learning method used in this training is dynamic backpropagation. For the obstacle avoidance problem an additional neuro-fuzzy controller is set up and trained. It influences the results from the navigation controller to avoid collisions with objects blocking the path. The controller is trained with dynamic backpropagation and a reinforcement learning algorithm called deep deterministic policy gradient.es_ES
dc.publisherPontificia Universidad Católica del Perúes_ES
dc.rightsAtribución-NoComercial-SinDerivadas 2.5 Perú*
dc.subjectRobots móvileses_ES
dc.subjectControladores programableses_ES
dc.subjectRedes neuronales (Computación)es_ES
dc.subjectSistemas difusoses_ES
dc.titleAutonomous obstacle avoidance and positioning control of mobile robots using fuzzy neural networkses_ES
dc.typeinfo:eu-repo/semantics/masterThesises_ESíster en Ingeniería de Control y Automatizaciónes_ESíaes_ES Universidad Católica del Perú. Escuela de Posgradoes_ESía de Control y Automatizaciónes_ES
dc.type.otherTesis de maestría

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Atribución-NoComercial-SinDerivadas 2.5 Perú
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 2.5 Perú