Navigering, sensorfusion och styrning för autonom markfarkost
Kalman filteringParticle filteringNavigationPotential fieldVirtual force fieldsGuidance.AutonomyUGVAutonomitetUGVKalmanfilterPartikelfilterSensorfusionNavigeringVektorfältVirtuella kraftfält.
The aim of the Master?s Thesis work is to study and develop algorithms for autonomous travel of a UGV (Unmanned Ground Vehicle). A vehicle for the mounting of sensors has been constructed in order to perform the work. Since the UGV is to be used outdoor in urban areas, GPS can be used. To improve precision and robustness, inertial navigation is used in addition to GPS, since GPS reception is likely to be diminished in such areas. The sensors used for navigation are consequently GPS, magnetometers, accelerometers, gyroscopes, tachometers and ultra sonic sensors measuring distance to be used in detection of obstacles. The system has been implemented in Matlab. Two alternative methods of navigation with sensor fusion have been developed; one is a decentralized method with Kalman filtering using an error model and the other is a centralized particle filter using an all-embracing model of the vehicle. The two methods have been evaluated and compared. Test results show that the two methods perform equivalently.The autonomous travel is undertaken between predetermined waypoints. In order to steer the vehicle a PID-controller based on the error between heading and its reference value is used. The computation of the reference value is based on position and heading in comparison to the desired path. The system has been tested using different routes and the results show an evident improvement of the precision in navigation compared to using only GPS-data. This holds for both navigation methods. Simulation of collision avoidance using virtual force fields shows satisfying results as well as terrain navigation with coordinate map referencing.