To build such a mechanism, new approaches for vision-guided mobile robot navigation have to be found. We show that this can be achieved by means of mixture models within an appearance-based paradigm. Mixture models are more useful in practice than other pattern recognition methods such as PCA (Principal Component Analysis) or FDA (Fisher Discriminant Analysis - also known as Linear Discriminant Analysis, LDA), because they can represent non-linear sub-spaces.
However, given the fact that mixture models are usually learned using the EM (Expectation-Maximization) algorithm which is a gradient ascent technique, the system cannot always converge to a desired final solution, due to the local maxima problem. To resolve this, a genetic version of the EM algorithm is used. We then show the capabilities of this latest approach on a navigation task that uses the above describe ``annotations".