Learning of Robot Navigation Tasks by Probabilistic Neural Network

Abstract

This paper reports results of artificial neural network for robot navigation tasks. Machine learning methods have proven usability in many complex problems concerning mobile robots control. In particular we deal with the well-known strategy of navigating by "wall-following". In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks. The PNN result was compared with the results of the Logistic Perceptron, Multilayer Perceptron, Mixture of Experts and Elman neural networks and the results of the previous studies reported focusing on robot navigation tasks and using same dataset. It was observed the PNN is the best classification accuracy with 99,635% accuracy using same dataset.