A Neuro-Fuzzy Approach to Autonomous Navigation for Mobile Robots

Autonomous, reliable navigation of mobile robots in unstructured environments is becoming a major issue for real-world applications. The main problem consists of matching the sensor data with an internal model used for planning and self-localization. We present an approach to autonomous navigation based on the definition and identification of grounded concepts by integrated instances of Fuzzy ART. This is a fuzzy neural network system, able to learn to classify numerical input vectors into fuzzy classes, i.e., to build symbolic interpretations of data. Our system is able to learn a topological map consisting of a sequence of landmarks conceptually identified, and derived from different sensors. We have successfully applied this approach with RAA, a mobile robot operating at our department, which can navigate in unstructured environments using the learnt map. Keywords: Neural Networks, Fuzzy Logic, Multisensor Data Fusion, Autonomous Agents, Symbol Grounding, Mobile robots, Sonar, Navigation. 1