An application of a backpropagation network for the control of a tracking behavior

Abstract

The problem of correctly evaluating noisy and incorrect data for the interpretation of ultrasonic sensor signals is addressed. Neural networks, with their inherent characteristics of adaptivity and high fault and noise tolerance, are well suited for such tasks. A backpropagation algorithm is described for the control of the tracking behavior of an autonomous mobile robot. Input data are provided by three ultrasonic sensors mounted on the front of the vehicle. For more flexibility the behavior and learning capability of the tracking algorithm have been improved using different networks.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>