@inproceedings{NikolovskiLimpertNessauetal.2023,
  author    = {Nikolovski, Gjorgji and Limpert, Nicolas and Nessau, Hendrik and Reke, Michael and Ferrein, Alexander},
  title     = {Model-predictive control with parallelised optimisation for the navigation of autonomous mining vehicles},
  series = {2023 IEEE Intelligent Vehicles Symposium (IV)},
  booktitle = {2023 IEEE Intelligent Vehicles Symposium (IV)},
  publisher = {IEEE},
  isbn      = {979-8-3503-4691-6 (Online)},
  doi       = {10.1109/IV55152.2023.10186806},
  pages     = {6 Seiten},
  year      = {2023},
  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.},
  language  = {en}
}
@inproceedings{EichenbaumNikolovskiMuelhensetal.2023,
  author    = {Eichenbaum, Julian and Nikolovski, Gjorgji and M{\"u}lhens, Leon and Reke, Michael and Ferrein, Alexander and Scholl, Ingrid},
  title     = {Towards a lifelong mapping approach using Lanelet 2 for autonomous open-pit mine operations},
  series = {2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)},
  booktitle = {2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)},
  publisher = {IEEE},
  isbn      = {979-8-3503-2069-5 (Online)},
  doi       = {10.1109/CASE56687.2023.10260526},
  pages     = {8 Seiten},
  year      = {2023},
  abstract  = {Autonomous agents require rich environment models for fulfilling their missions. High-definition maps are a well-established map format which allows for representing semantic information besides the usual geometric information of the environment. These are, for instance, road shapes, road markings, traffic signs or barriers. The geometric resolution of HD maps can be as precise as of centimetre level. In this paper, we report on our approach of using HD maps as a map representation for autonomous load-haul-dump vehicles in open-pit mining operations. As the mine undergoes constant change, we also need to constantly update the map. Therefore, we follow a lifelong mapping approach for updating the HD maps based on camera-based object detection and GPS data. We show our mapping algorithm based on the Lanelet 2 map format and show our integration with the navigation stack of the Robot Operating System. We present experimental results on our lifelong mapping approach from a real open-pit mine.},
  language  = {en}
}
@inproceedings{SchulteTiggesMatheisRekeetal.2023,
  author    = {Schulte-Tigges, Joschua and Matheis, Dominik and Reke, Michael and Walter, Thomas and Kaszner, Daniel},
  title     = {Demonstrating a V2X enabled system for transition of control and minimum risk manoeuvre when leaving the operational design domain},
  series = {HCII 2023: HCI in Mobility, Transport, and Automotive Systems},
  booktitle = {HCII 2023: HCI in Mobility, Transport, and Automotive Systems},
  editor    = {Kr{\"o}mker, Heidi},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-031-35677-3 (Print)},
  doi       = {10.1007/978-3-031-35678-0_12},
  pages     = {200 -- 210},
  year      = {2023},
  abstract  = {Modern implementations of driver assistance systems are evolving from a pure driver assistance to a independently acting automation system. Still these systems are not covering the full vehicle usage range, also called operational design domain, which require the human driver as fall-back mechanism. Transition of control and potential minimum risk manoeuvres are currently research topics and will bridge the gap until full autonomous vehicles are available. The authors showed in a demonstration that the transition of control mechanisms can be further improved by usage of communication technology. Receiving the incident type and position information by usage of standardised vehicle to everything (V2X) messages can improve the driver safety and comfort level. The connected and automated vehicle's software framework can take this information to plan areas where the driver should take back control by initiating a transition of control which can be followed by a minimum risk manoeuvre in case of an unresponsive driver. This transition of control has been implemented in a test vehicle and was presented to the public during the IEEE IV2022 (IEEE Intelligent Vehicle Symposium) in Aachen, Germany.},
  language  = {en}
}