TY  - CHAP
A1  - Nikolovski, Gjorgji
A1  - Limpert, Nicolas
A1  - Nessau, Hendrik
A1  - Reke, Michael
A1  - Ferrein, Alexander
T1  - Model-predictive control with parallelised optimisation for the navigation of autonomous mining vehicles
T2  - 2023 IEEE Intelligent Vehicles Symposium (IV)
N2  - The work in modern open-pit and underground mines requires the transportation of large amounts of resources between fixed points. The navigation to these fixed points is a repetitive task that can be automated. The challenge in automating the navigation of vehicles commonly used in mines is the systemic properties of such vehicles. Many mining vehicles, such as the one we have used in the research for this paper, use steering systems with an articulated joint bending the vehicle’s drive axis to change its course and a hydraulic drive system to actuate axial drive components or the movements of tippers if available. To address the difficulties of controlling such a vehicle, we present a model-predictive approach for controlling the vehicle. While the control optimisation based on a parallel error minimisation of the predicted state has already been established in the past, we provide insight into the design and implementation of an MPC for an articulated mining vehicle and show the results of real-world experiments in an open-pit mine environment.
KW  - Mpc
KW  - Control
KW  - Path-following
KW  - Navigation
KW  - Automation
Y1  - 2023
SN  - 979-8-3503-4691-6 (Online)
SN  - 979-8-3503-4692-3 (Print)
U6  - http://dx.doi.org/10.1109/IV55152.2023.10186806
N1  - IEEE Symposium on Intelligent Vehicle, 4.-7. June 2023, Anchorage, AK, USA.
PB  - IEEE
ER  -