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Model-predictive control with parallelised optimisation for the navigation of autonomous mining vehicles

  • 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.

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Metadaten
Author:Gjorgji NikolovskiORCiD, Nicolas LimpertORCiD, Hendrik NessauORCiD, Michael RekeORCiD, Alexander FerreinORCiD
DOI:https://doi.org/10.1109/IV55152.2023.10186806
ISBN:979-8-3503-4691-6 (Online)
ISBN:979-8-3503-4692-3 (Print)
Parent Title (English):2023 IEEE Intelligent Vehicles Symposium (IV)
Publisher:IEEE
Document Type:Conference Proceeding
Language:English
Year of Completion:2023
Date of the Publication (Server):2023/08/03
Tag:Automation; Control; Mpc; Navigation; Path-following
Length:6 Seiten
Note:
IEEE Symposium on Intelligent Vehicle, 4.-7. June 2023, Anchorage, AK, USA.
Link:https://doi.org/10.1109/IV55152.2023.10186806
Zugriffsart:campus
Institutes:FH Aachen / Fachbereich Elektrotechnik und Informationstechnik
FH Aachen / MASKOR Institut für Mobile Autonome Systeme und Kognitive Robotik
collections:Verlag / IEEE