The integration of fuzzy logic and artificial neural network methods for mobile robot obstacle avoidance in a static environment

Abstract

This paper describes the development of E-Puck mobile robot obstacle avoidance controller using fuzzy logic control and artificial neural network. Fuzzy logic control is used to collect data from the environment based on infrared sensor and then fed them into artificial neural network for training process. The simulation softwares used in this research are Webots PRO and MATLAB. The mobile robot is expected to start moving and then exploring the environment from starting point without hitting any obstacles. The obstacles are set to be static in the environment. The mobile robot's performance based on specific rules created was recorded and validated. Overall performance shows that these approaches are efficient to avoid few numbers and shapes of static obstacles in the environment.