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- W108376296 abstract "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." @default.
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- W108376296 modified "2023-09-27" @default.
- W108376296 title "A Neuro-fuzzy Approach to Autonomous Navigation for Mobile Robots." @default.
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