Fuzzy reinforcement learning and its application in robot navigation

This paper focused on the problem of the intelligent mobile robot navigation under the unknown and changing environment. The fuzzy logic controller (FLC) is applied to the reactive robot control system. Without sufficient expert knowledge can be available, the fuzzy inference system (FIS) and reinforcement learning (RL) are integrated. The consequence of fuzzy rules is refined through Q (λ)-learning. Then, the fuzzy reinforcement learning is employed to design controller of the robot system. The scheme of switching behavior-based FLC was presented, which includes avoidance obstacles behavior and wall-following behavior. This scheme can effectively solve the problem of navigation under complicated environment, which contains the concave obstacles. Experiment results indicate that efficiency and effectiveness of the proposed approach. Furthermore, the FLC learned by RL has robust and adaptability, and can be applied to the different environments.