Robot positioning by supervised and unsupervised odometry correction

Abstract

The aim of this thesis is to perform robot positioning, based on an odometry which is continuously corrected by different landmark detection systems demanding as less modifications as possible for the environment. Two independent correction systems (a supervised and an unsupervised) were implemented into two different experiences which represent the subject of this thesis. The supervised experiment uses grid lines painted on the floor which are detected by a single light sensor underneath the robot which cannot distinguish between horizontal and vertical lines. The robot knows the geometry of the grid lines and its estimated position which is calculated by odometry. A new position probability model calculates the assumed robot position and transforms this single sensor information into a reliable position and orientation indication of the robot. The intended trajectory is slightly modified in order to optimize the correction algorithm by guiding the robot more efficiently over close grid lines. The theory was implemented and tested on a real Khepera robot. Investigation and modeling of the odometry error is the main subject of this first experiment. The second experiment demonstrates continuous odometry correction by an unsupervised correction system. Different kind of unsupervised neural networks classify the robot’s rough sensor signals. A statistical