Illuminance Measurement and SLAM of A Mobile Robot based on Computational Intelligence

This paper proposes self-localization and map building methods based on a steady-state genetic algorithm and self organizing map for a mobile robot used for illuminance measurement. According to the measured distance by a laser range finder, the map is updated sequentially. When the difference between the self-position on the building map and the estimated self-position based on the measured distance is larger than the predefined threshold, the proposed method corrects the self-location and updates the map to be more accurate. Finally we show experimental results of the proposed method.