Path following of mobile robot using convolution neural network

Abstract

Automated guided vehicles (AGVs) are used to transport parts and products inside factories. Recently, AGVs are often operated automatically by image processing using cameras. In this paper, we design a convolutional neural network that enables real-time image recognition even on a small computer like Raspberry Pi, and consider Path following of a mobile robot by image recognition. The training data created to train the CNN consists of line images and direction of robot movement which is determined from the position of center of gravity and gradient of the line in the image. The designed CNN model classifies line images into three classes: forward, right turn, and left turn, which are the direction of robot movement. The performance of the designed CNN model is verified by a mobile robot, and it is shown that the robot can follow straight and curve lines. the experimental results of a small mobile robot that performs line following by image recognition.