Obstacle avoidance for mobile robots: A Hybrid Intelligent System based on Fuzzy Logic and Artificial Neural Network

Abstract

Obstacle avoidance is one of the most important aspects of autonomous mobile robots. This task is composed by two phases. First, the robot must detect obstacles in the environment with its sensors. Then, it must choose an appropriate movement to go through the environment without colliding. However, the noise produced during the sensors reading can lead the robot to take wrong decisions. This paper presents the development of an E-Puck mobile robot obstacle avoidance controller using a Hybrid Intelligent System (HIS) based on Fuzzy Logic (FL) and Artificial Neural Networks (ANN). The FL treats the data of infrared sensors and then feeds an ANN that decides which movement the robot must perform. The HIS was compared to another approach in which the data of infrared sensors are used directly in an ANN. The empirical results show that HIS avoids more collisions and improves the smoothness of navigation.