Learning goal seeking and obstacle avoidance using the FQL algorithm

In this article, we are interested in the reactive behaviours navigation training of a mobile robot in an unknown environment. The method we will suggest ensures navigation in unknown environments with presence off different obstacles shape and consists in bringing the robot in a goal position, avoiding obstacles and releasing it from the tight corners and deadlock obstacles shape. This is difficult to do manually in the case of FIS with a large rule base. In this framework, we use the reinforcement learning algorithm called Fuzzy Q-learning, based on temporal difference prediction method. The application was tested in our experimental PIONEER II platform.