Neural networks for mobile robot localisation using infra-red range sensing

For a robot to be fully autonomous whilst mobile, it is necessary for it to be able to determine its position in its environment. Most of the work on this problem has concentrated on using geometrical techniques which are typically implemented as part of a Kalman filter cycle. This paper examines the possibility of using a neural network to assist in the task of estimating the position of the robot. This is beneficial because it does not require beacons to be placed in the environment or the use of an explicit map of the environment. It does not require knowledge of the previous estimate of the robot's position. In this paper, Radial Basis Function networks and Multi-Layer Perceptrons are trained to estimate the functional relationship between preprocessed range sensor data and the position of the robot. This approach is assessed using both simulated and real range data.