A navigation method for mobile robots using interval type-2 fuzzy neural network fitting Q-learning in unknown environments

Abstract

This paper presents a navigation algorithm based on interval type-2 fuzzy neural network fitting Q-learning (IT2FNN-Q), and succeeds in providing a solution for mobile robot navigation in complex environments. The algorithm utilizes the fuzzy reasoning adaptive ability and extensive functional approximation features of IT2FNN to solve this problem, mapped from state space to action space, of the Q-learning algorithm in unknown environments. Compared with the BP fitting Q-learning algorithm (BP-Q), IT2FNN-Q endows the robot with better adaptive and real-time decision-making abilities and solves the slow convergence and nonconvergence problems, through its local approximation. By comparison with the fuzzy neural network fitting Q-learning algorithm (FNN-Q), this proposed algorithm has more advantages for dealing with the external uncertainty, enabling the robot to complete a better path with less fuzzy rules. The results of the simulation and comparison of the proposed method with FNN-Q and BP-Q revealed that the mobile robot can navigate itself in complex environments with fewer steps, obtaining more reward values by adopting the algorithm presented in this paper.