Symbol grounding and a Neuro-Fuzzy architecture for multisensor fusion

The fusion of multisensorial data is a common practice to identify a world model when data coming from a single sensor is unreliable. A possible approach to multisensor data fusion consists in identifying common interpretations of data coming from different sensors, and to integrate the information represented as symbols related to the possible interpretations. In this paper, we present an architecture of integrated instances of Fuzzy ARTMAP, a fuzzy neural network system, able to learn to classify numerical input vectors into fuzzy classes, i.e., to build symbolic interpretations. We have successfully applied this architecture on a navigation task in an unstructured, unknown environment for a mobile robot, integrating data from sensors, omnatidia, bumpers, and the odometric system.