A Probabilistic Fuzzy Controller with Operant Learning for Robot Navigation

Fuzzy logic system (FLS) promises an efficient way for obstacle avoidance. However, it is difficult to maintain the correctness, consistency, and completeness of a fuzzy rule base tuned by a human expert. In this paper, a novel approach termed probabilistic fuzzy controller with operant learning (PFCOL) for robot navigation is presented. Operant learning (OL) is a form animal learning way. The key feature of this approach is that it combines a probabilistic stage and a stochastic perturbation generator module into FLS to handle problems. At last, the ultimate output is determined by these two uncertain stages. This imitates animal learning method of generating stochastic behavior in the complex and uncertain environment. The simulation results show that the proposed PFCOL method can automatically generate approximate actor to adapt complex circumstances. Through studies on obstacle avoidance and goal seeking tasks by a mobile robot verify the approach is superior in generating efficient fuzzy inference systems.