A classification tool, based on the SOC (self-organizing classifier) neural model, is presented as an alternative solution to the problem of world modeling, aimed at navigation planning of an autonomous mobile robot. Starting from rough sensorial data, the knowledge about the explored environment of a mobile robot can be incrementally organized by means of self-organizing maps and a set of heuristic rules, avoiding the computational overhead due to classical geometric approaches to world modeling. The classification strategy realized, called SON (self-organizing navigation), allows to map neural information into symbols: the authors called such emergent symbols 'navigation situations'. The prototype has been successfully tested both with simulated and real data.

Self-organizing navigation: From neural maps to navigation situations

P. Morasso;G. Vercelli;R. Zaccaria
1993

Abstract

A classification tool, based on the SOC (self-organizing classifier) neural model, is presented as an alternative solution to the problem of world modeling, aimed at navigation planning of an autonomous mobile robot. Starting from rough sensorial data, the knowledge about the explored environment of a mobile robot can be incrementally organized by means of self-organizing maps and a set of heuristic rules, avoiding the computational overhead due to classical geometric approaches to world modeling. The classification strategy realized, called SON (self-organizing navigation), allows to map neural information into symbols: the authors called such emergent symbols 'navigation situations'. The prototype has been successfully tested both with simulated and real data.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/392135
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