Abstract This Bachelor thesis presents an approach for the complete coverage path planning (CCPP) problem which occurs for different robotic applications, such as autonomous lawn mowers or vaccuum cleaners. Methods used for localization [27], map representation [10] and planning [14] are discussed under consideration of sensor noise and uncertainty about the own position induced by the movement of the robot. An efficient method to solve the CCPP problem under uncertainty is proposed and evaluated due to simulations.
Abstract The present bachelor thesis presents the necessary methods for an exact selflocalization by using an Inertial Mesurment Unit (IMU) and the odometry of an autonomous lawn mower. This self-localization shall be used in later work together with a localization of a particle filter. The required standard models [12] for the individual sensor systems were examined and the required parameters determined. Measurements were taken with the autonomous lawn mower to develop a Kalman filter [15] based on the data obtained.