Baigiamajame magistro darbe atliekamas sklandytuvo atakos ir slydimo kampo matavimo metodų tyrimas. Pirmoje darbo dalyje apžvelgiami atakos kampo matuokliai: virvutė, pritvirtinta ant stiklinio gaubto, atakos – slydimo kampo matuoklis su vėjarodžiu ir Pitoto vamzdelio tipo daviklis. Davikliai palyginami, aprašomi jų privalumai ir trūkumai lyginant su kitais davikliais. Antroje dalyje aprašomi alfa ir beta kampų matavimo metodai: matavimas vamzdelio tipo davikliu ir metodas, kai nenaudojami specialūs atakos, slydimo kampo davikliai. Toliau apžvelgiami veiksniai, turintys įtakos matavimo tikslumui. Pateikiamos kelių vamzdelio tipo daviklių kalibravimo kreivės. Paskutinėje dalyje programa Matlab kuriamas matematinis-dinaminis sklandytuvo modelis. Modeliu, pagal nustatytas sąlygas, skaičiuojami atakos ir slydimo kampai, analizuojami grafikai. / The thesis made the glider’s attack and slip angle measurement methods for the investigation. The first part gives an overview of measuring devices of angle of attack and slip angle: The side string, attached to the side of the canopy, vane mounted AOA sensor, Pitot-tube type sensor. The sensors are compared, describes their advantages and disadvantages compared with other sensors. The second part describes the alpha and beta angle measurement methods: measurement with the tube-type sensor, and the method without using the specific attack, slip angle sensors. The following gives an overview of factors affecting the measurement accuracy. Several tube-type sensor calibration curves are presented. The last part of thesis presents development of mathematical – dynamic model of the glider using Matlab software. The model calculates the angle of attack and slip using set conditions of flight.

During space missions, real time tele-operation of a rover is not practical because of significant signal latencies associated with inter planetary distances, making some degree of autonomy in rover control desirable. One of the challenges to achieving autonomy is the determination of terrain traversability. As part of this field, the determination of motion state of a rover on rough terrain via the estimation of wheel-terrain contact angles is proposed. This thesis investigates the feasibility of estimating the contact angles from the kinematics of the rover system and measurements from the onboard inertial measurement unit (IMU), joint angle sensors and wheel encoders. This approach does not rely on any knowledge of the terrain geometry or terrain mechanical properties. An existing framework of rover velocity and wheel slip estimation for flat terrain has been extended to additionally estimate the wheel-terrain contact angle along with a side slip angle for each individual wheel, for rough terrain drive. A random walk and a damped model are used to describe the evolution of the contact angle and side slip angle over an unknown terrain. A standard strapdown algorithm for the estimation of attitude and velocity from IMU measurements, is modified to incorporate the 3D kinematics of the rover in the implementation of a nonlinear Kalman filter to estimate the motion states. The estimation results from the filter are verified using tests performed on the ExoMars BB2. The obtained contact angle estimates are found to be consistent with the reference values.

Being able to safely control autonomous heavy machinery is of uttermost importance for the conversion of traditional machines to autonomous machines. With the continuous growth of autonomous vehicles around the globe, an increasing effort has been put into certifying autonomous vehicles in terms of reliability and safety. In this thesis, we will investigate the problem with a deviation from the planned path for an autonomous hauler from Volvo Construction Equipment. The autonomous hauler has an error within the kinematic model, the feed-forward curvature-steering controller, due to a slip-effect that comes with the third wheel-axle. The deviation can especially be seen in sharp curves, where the deviation needs to be decreased in order to make the autonomous hauler more dependable and achieve an increased accuracy when following any given path. The aim of the thesis is to develop a fully functional Artificial Neural Network that has a new steering angle as output. The hypothesis for this thesis is to use an ANN to mimic the steering of a human driver, since a real driver compensates for the slipping behavior; both because the operator knows where on the road the machine is and also in the way that a human thinks many steps ahead whilst driving. This proposed ANN will have a monitor function which ensures that the steering angle command operates within its boundaries. Hence this thesis implies that it is indeed possible to ensure that the ANN performs reliably with the help of a monitor function in a simulated environment and can thus be used in dependable systems.