Application of a possibilitic-based approach to mobile robotics
Possibility theory offers a nice setting for information combination or data fusion. This attractiveness arises from the elastic constraints that govern the basic concepts pertaining to this theory. Consequently, many combination modes are available ranging from the conjunctive to the disjunctive passing through the compromise mode. Therefore the problem of what is the suitable combination mode for a given situation is still open. The adaptive rule proposed by Dubois and Prade contributes partly to this problem, and has been successfully employed in several applications like robotics. In this paper we apply the recently new combination rule referred to as progressive rule, which permits us to handle robustness with respect to shape modelling and takes account for a possible presence of erroneous information, to mobile robotics context. The rule explicitly accounts for the distance between each alternative and the consensus zone. The rule is then incorporated into a general scheme of fusion methodology, which allows a transformation of raw inputs into meaningful and homogeneous information that will be refined by the progressive rule. A robotics application corresponding to mobile robot localization in a structured environment is carried out. The feasibility of the possibilistic approach is demonstrated by a comparison with a standard method based on Kalman filter.
mobile robotics possibility theory data fusion progressive rule modelling