1 - Fusion de données capteurs en vue de la localisation absolue d'un robot mobile par une méthode basée sur la théorie des possibilités. Comparaison avec le filtre de Kalman

摘要

In order to improve autonomous system, it is necessary to determine accurately its position . In this paper, a method based on possibility theory has been developed in the experimental framework of the localisation of a miniature mobile robot from odometry reading and exteroceptive sensors into an environment equipped with beacons . The data are modelled in the setting of the possibility theory which provides interesting tools of representing imprecision and uncertainty . A comparison with a classical method (Kalman filter) taken as a reference is realised . Basically, the fusion procedure by Kalman filtering method can be seen as a weighted average by the information uncertainties . Its principle is to favour the information with low uncertainty (i.e. low variance) and to realise a fusion by minimisation of variance . On the other hand, the adaptive combination rule used in the possibilistic method takes into account the level of conflict between the sources and favour the redundancy of the information which are in . Then, it is rather a fusion by agreement . In spite of these fundamental discrepancies, the outcomes of simulation and/or of experiences on the real robot, obtained by both methods are satisfactory and quite close .