Abstract
In this paper a system for estimating position and orientation of a mobile robot in a non-structured environment, by means of self-organizing neural networks, is described. After perfonning an initiat learning phase, the system is able to incrementally build a representation of the environment. Position and orientation of the robot can then be obtained, upon data coming from the odometric system and sensors (ultrasound sensors, infrared sensors and 2D-Iaser sensor). The system has been implemented and tested on a NOMAD 200 robot. Experimental results are reported, which show the robot to be able to navigate without losing its way, and to adapt to changes in the environment.
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