Experimental evaluation of new navigator of mobile robot using fuzzy Q-learning

Abstract

In this paper, we propose an approach of fusing the fuzzy control actions of the obstacle avoidance and goal-seeking which utilises fuzzy logic and reinforcement learning for navigation of a mobile robot in unknown environments. The proposed reactive navigator consists of three modules: move to goal, obstacle avoidance, and fuzzy behaviour supervisor. The selection of the actions available in each fuzzy rule is learned through reinforcement learning (Q-learning algorithm). A new and powerful method is used to construct automatically these rules. The experiments carried out on the Pioneer 2P mobile robot have shown that the navigator is able to perform a successful navigation task in various unknown environments with smooth action and exceptionally good robustness.