Low cost robots, such as vacuum cleaners or lawn mowers, employ simplistic and often random navigation policies. Although a large number of sophisticated localization and planning approaches exist, they require additional sensors like LIDAR sensors, …
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 To make it easier for people to work in the lawn care, there is a long list of robotic lawnmowers. The navigation is a big problem, since the application is usually limited by a perimeter wire. This process means a time as well as financial expense and must be changed. For this purpose, a new method for lawn detection was developed at the Institute of Robotics and Cognitive Systems.
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.
Abstract Low cost robots, such as vacuum cleaners or lawn mowers employ simplistic and often random navigation policies. Although a large number of sophisticated mapping and planning approaches exist, they require additional sensors like LIDAR sensors, cameras or time of flight sensors. In this work, we investigate SLAM techniques for efficient mapping and localization with limited sensing capabilities.