Obstacle Avoidance Method for Wheeled Mobile Robots Using Interval Type-2 Fuzzy Neural Network

This paper proposes an obstacle avoidance method in the position stabilization of the wheeled mobile robots using interval type-2 fuzzy neural network (IT2FNN). Previously, we have proposed the unified strategies of obstacle avoidance and shooting method of the robot soccer system using type-1 fuzzy neural network (T1FNN). Even though the previous T1FNN method can achieve the required tasks, the performance of the previous T1FNN method is not satisfactory in the following sense. The previous T1FNN cannot reduce the influence of uncertainties effectively because it uses the crisp set as the membership values. In addition, it can result in the large oscillation behavior during the obstacle avoidance. Accordingly, we should design the IT2FNN method to improve the performance with smoother behavior as well as improved obstacle avoidance. The proposed IT2FNN method has the fuzzy neural network structure different from the T1FNN. Since the IT2FNN uses the fuzzy set instead of the crisp set as the membership values and it is robust against uncertainties, the performance of the robot behavior can be significantly improved especially in the presence of obstacles. Both simulation and experimental results using the actual wheeled mobile robot with the vision information are provided to show the validity and the advantages of the proposed method.