Model-predictive Control with Parallelised Optimisation for the Navigation of Autonomous Mining Vehicles

Model-predictive Control with Parallelised Optimisation for the Navigation of Autonomous Mining Vehicles. Nikolovski, G., Limpert, N., Nessau, H., Reke, M., & Ferrein, A. In 2023 IEEE Intelligent Vehicles Symposium (IV), pages 1–6, June, 2023. IEEE.

Ieeexpl doi abstract bibtex

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.

@InProceedings{Nikolovski-etAl_IV2023_MPC-Nav-Mining,
  author       = {Nikolovski, Gjorgji and Limpert, Nicolas and Nessau, Hendrik and Reke, Michael and Ferrein, Alexander},
  booktitle    = {2023 IEEE Intelligent Vehicles Symposium (IV)}, 
  title        = {Model-predictive Control with Parallelised Optimisation for the Navigation of Autonomous Mining Vehicles}, 
  year         = {2023},
  month        = {June},
  day          = {04-07},
  pages        = {1--6},
  location     = {Anchorage, AK, USA},
  doi          = {10.1109/IV55152.2023.10186806},
  url_ieeexpl  = {https://ieeexplore.ieee.org/abstract/document/10186806},
  publisher    = {IEEE},
  ISSN         = {2642-7214},
  keywords     = {Navigation;Intelligent vehicles;Hydraulic
                  drives;Steering systems;Transportation;Hydraulic
                  systems;Minimization;mpc;control;path-following;navigation;automation},
  abstract     = {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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