A Hierarchical Self-Organizing Controller for Navigation of Mobile Robots

In this work an autonomous navigation system based in a modular neuro-fuzzy network for controlling mobile robots is proposed. Based on this system the robot is able to reach goals avoiding collisions against obstacles in an unknown environment. The system architecture belongs to the reactive paradigm. A reinforcement learning mechanism balanced with two innate behaviors, which are to avoid obstacles and seek to goals, guides the robot from an initial point to the goal. The validation of the proposal system has been done by using the Saphira simulator. The results obtained in the tests performed on Saphira simulator and on the Pioneer robot show the efficiency and learning capabilities of this system.