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Biologically Inspired Controller for the Autonomous Navigation of a Mobile Robot in an Evasion Task

Authors: Dejanira Araiza-Illan, Tony J. Dodd


A novel biologically inspired controller for the autonomous navigation of a mobile robot in an evasion task is proposed. The controller takes advantage of the environment by calculating a measure of danger and subsequently choosing the parameters of a reinforcement learning based decision process. Two different reinforcement learning algorithms were used: Qlearning and Sarsa (λ). Simulations show that selecting dynamic parameters reduce the time while executing the decision making process, so the robot can obtain a policy to succeed in an escaping task in a realistic time.

Keywords: Mobile Robots, Reinforcement Learning, autonomous navigation

Digital Object Identifier (DOI):

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