Place characterization for navigation via behaviour merging for an autonomous mobile robot

This paper presents a novel approach to characterize and recognize places in unknown environments for behaviour-based navigation purposes. This recognition technique classifies a given robot position in three categories, wall, corridor or door. The technique allows the robot not only to select the behaviour that better fits that situation, but also to fuse them in a seamless way. It is supported by a local grid built from sonar readings. The contour of this grid is extracted, represented by its FFT, and it is reduced to a short feature vector by using principal component analysis. The method has been successfully tested in real environments with a Pioneer robot equipped with 8 frontal sonar sensors, proving its feasibility and effectiveness.