Real-time self-reaction of mobile robot with genetic fuzzy neural network in unknown environment

Abstract

Presents an intelligent control method for real-time self-reaction of a mobile robot in an unknown environment, it is called a genetic fuzzy neural network. It is used to control a mobile robot according to sensing different information, which includes the different direction distances between the obstacles and robot sensed by ultrasonic sensors, the target orientation sensed by a simple optical range-finder and the robot's direction of movement. In the paper, the distances d/sub r/, d/sub c/, and d/sub l/ between the robot and the obstacles with respect to the right, front and left sensors, as well as the angle t/sub r/, between the target orientation and the robot's direction of movement are taken as the inputs of the intelligent controller. The output of the intelligent controller is the steering angle s/sub a/. A genetic fuzzy neural network is presented to describe the fuzzy reasoning relationship between the inputs and the output of the system. It has a few advantages, such as higher learning speed and easier ensuring convergence. Simulation results of mobile robot collision avoidance in an unknown environment show the method presented is feasible.