Behavior Fusion of Robot Navigation Using a Fuzzy Neural Network

This paper presents a design of behavior-fusion architecture for mobile robot navigation. We first design three behaviors for robot navigation, including obstacle avoidance, wall following, and goal seeking. We implement these primitive behaviors by using fuzzy-logic control approaches. Then, the fusion weight of each behavior is determined by using the proposed behavior-fusion neural network. The neural network maps the current environment sensor data to suitable fusion weights. Both computer simulation and practical experiments verify the effectiveness of the method.