The accelerating transition away from fossil energy sources is driving the electrification in all sectors. Producing renewable energy is very resource-intensive and therefore requires a massive expansion of the mineral resource extraction. One way to meet the increasing demand for mineral resources is to automate the processes involved in the extraction. A large proportion of the time is spent on the material transport, mostly carried out by dump trucks. The challenges of autonomous driving in surface mines require the development of new navigation and control approaches for transport fleet automation. Vehicle control requires continuous information about the vehicle’s position, velocity speed and attitude. One of the most common methods of navigation is the use of signals from Global Navigation Satellite Systems (GNSS), such as GPS. However, due to the structure of open-pit mines, characterized by benches with steep slopes, and sometimes great pit depths, the reception of the signals cannot be guaranteed. This thesis investigates how a robust provision of the information is nevertheless possible. The navigation filter presented in this thesis is based on an Unscented Kalman Filter and fuses data of different sensors mounted on the vehicle. These include wheel speed sensors, an optical speed sensor, an inertial measurement unit and a barometer. In addition to the various measurements, the non-holonomic vehicle constraints are also considered in the navigation filter. Several proofs are provided, that the developments presented enable driving in areas without GNSS reception for more than 90 seconds. The maximum position deviation remains below 0.8 meter. A further increase in navigation performance can be achieved by adaptive algorithms. These algorithms permanently adapt the parameterization of the state estimation to the currently prevailing conditions. This thesis provides evidence that adaptive parameterization improves position and orientation estimation. For the final integration of the different developments, different integration architectures are presented and discussed. To validate the methods, a prototype of an autonomous articulated transport vehicle was developed and built. Data for the algorithm development was recorded and the final system validated in a regional gravel pit.
Robuste Zustandsschätzung für die Navigation von autonom fahrenden Transporteinheiten in der Rohstoffindustrie
Robust state estimation for navigation of autonomous transport units in the extractive industry
2023-01-01
1 Online-Ressource : Illustrationen pages
Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2023; Aachen : RWTH Aachen University 1 Online-Ressource : Illustrationen (2023). = Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2023
Miscellaneous
Electronic Resource
German
TIBKAT | 2023
|European Patent Office | 2021
|Verwaltungsvorrichtung eines autonom fahrenden Fahrzeugs
European Patent Office | 2022
|