Abstract
This paper proposes a Environmental Map Building in unknown environments for mobile robots. One of the important intelligent capabilities is to build an environmental map and to estimate and correct the self-location because the robot cannot know the environment beforehand. Map building by mobile robots has a long history. The measured distance is used for matching with an environmental map, but this map should be also generated by the robot itself. This problem is well known as a simultaneous localization and mapping(SLAM). Environmental map should shows various kind of information in environment such as temperature, humidity, barometric pressure, etc.. Basic distance map is made by laser range finder. When the difference between the measured distance and its corresponding map data is large, the robot updates the self-location by using the steady-state genetic algorithm, and updates the map by using topological approach. We propose map building methods based on a topological map based on growing topological neural network. Finally we discuss the effectiveness of the proposed methods through several experimental results and comparison results.
