BP neural network based localization for a front-wheel drive and differential steering mobile robot

This paper presents a new BP-neural-network-based localization algorithm for a wheeled agricultural mobile robot, which is front-wheel drive and differential steering. Training the BP neural network is the first step of the localization algorithm. During this process, the drive pulse number is regarded as the input; the length of the left or right wheel´s trajectory is regarded as the output; and Bayesian rule is used to generate the training function. Inducing the displacement of the robot´s geometric center is the second step, in which the trajectories of the left and right wheels are assumed as two concentric circular arces. In the contrast experiments with the least square localization algorithm, the new proposed BP-neural-network based algorithm shows high accuracy and feasibility.