Analysis of artificial intelligence application using back propagation neural network and fuzzy logic controller on wall-following autonomous mobile robot

Abstract

This paper presents a comparison of two methods of artificial intelligence which applied in Wall following Autonomous Mobile Robot; both of them are Neural Network Back propagation and Fuzzy Logic. The robot has three input variables and two output variables. The inputs are distance between the robot and the wall which is sensed by HC-SR04 ultrasonic sensors. The output variables are the speed of the two wheels which is driving by 12 Volt DC motor. In this case mobile robot is designed to avoid the collision with any obstacles like wall or other mobile robots. In this implementation mobile robot is designed with a numbers of ultrasonic sensors and placed on certain position like center front, left front and left back. The sensor will send the data in real time. After being processed, the input produces output in form of speed value governing motor rotation mounted on both wheels of the robot to find the optimum point. In this comparison, both methods Backpropagation Neural Network and Fuzzy Logic are treated the same. Wall following Autonomous Mobile Robot is using Atmega2560 microcontroller. The logic is uploaded to the microcontroller. The result of the comparison of these two methods when applied in Wall-following Autonomous Mobile Robot is the movement of the robot using Neural Network Back propagation is faster than using Fuzzy Logic Controller.