Steady-State Genetic Algorithm for Self-localization in Illuminance Measurement of A Mobile Robot

This paper proposes a steady-state genetic algorithm for self-localization and map building for illuminance measurement of a mobile robot. The map is represented by 2 dimensional discrete cell space. According to the measured distance by laser range finder, the map is updated sequentially. When the difference between the measured distance and the map data is large, a steady-state genetic algorithm corrects the self-location. Finally we show computer simulation and experimental results of the proposed method.