Abstract

Modelling and reducing uncertainty are two essential problems with mobile robot localisation. In this paper, a new robot position estimator, the Gaussian mixture of Bayes �GMB) which utilises a density estimation technique, is introduced in particular. The proposed system, namely the GMB robot position estimator, which allows a robot's position to be modelled as a probability distribution, and uses Bayes ' theorem to reduce the uncertainty of its location. In addition, we describe, in this paper, how our proposed system is capable of dealing with multiple sensors, as well as a single sensor only. Nevertheless, it is known that such multiple sensors could be used to raise more robust than the single sensor, in terms of obtaining accurate estimate over a robot's position. The GMB position estimator mainly consists of four modules such as sonar-based, sensor selection, sensor fusion, and sensor selection improved by combining it with sensor fusion. The proposed system is also illustrated with respect to minimising the uncertainty of a

Citations