Neural networks for mobile robot navigation: A survey

Abstract

Nowadays, mobile robots have attracted more and more attention from researchers due to their extensive applications. Mobile robots need to have the capabilities of autonomy and intelligence, and they pose a challenge to researchers, which is to design algorithms that allow the robots to function autonomously in unstructured, dynamic, partially observable, and uncertain environments [1]. Navigation is the key to the relative technologies of mobile robots and neural networks are widely used in the field of mobile robot navigation due to their properties such as nonlinear mapping, ability to learn from examples, good generalization performance, massively parallel processing, and capability to approximate an arbitrary function given sufficient number of neurons. This paper surveys the developments in the last few years of the neural networks with applications to mobile robot navigation. © Springer-Verlag Berlin Heidelberg 2006.