@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{ViehmannLimpertHofmannetal.2023,
  author    = {Viehmann, Tarik and Limpert, Nicolas and Hofmann, Till and Henning, Mike and Ferrein, Alexander and Lakemeyer, Gerhard},
  title     = {Winning the RoboCup logistics league with visual servoing and centralized goal reasoning},
  series = {RoboCup 2022},
  booktitle = {RoboCup 2022},
  editor    = {Eguchi, Amy and Lau, Nuno and Paetzel-Pr{\"u}smann, Maike and Wanichanon, Thanapat},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-031-28468-7 (Print)},
  doi       = {https://doi.org/10.1007/978-3-031-28469-4_25},
  pages     = {300 -- 312},
  year      = {2023},
  abstract  = {The RoboCup Logistics League (RCLL) is a robotics competition in a production logistics scenario in the context of a Smart Factory. In the competition, a team of three robots needs to assemble products to fulfill various orders that are requested online during the game. This year, the Carologistics team was able to win the competition with a new approach to multi-agent coordination as well as significant changes to the robot's perception unit and a pragmatic network setup using the cellular network instead of WiFi. In this paper, we describe the major components of our approach with a focus on the changes compared to the last physical competition in 2019.},
  language  = {en}
}
@inproceedings{ArndtConzenElsenetal.2023,
  author    = {Arndt, Tobias and Conzen, Max and Elsen, Ingo and Ferrein, Alexander and Galla, Oskar and K{\"o}se, Hakan and Schiffer, Stefan and Tschesche, Matteo},
  title     = {Anomaly detection in the metal-textile industry for the reduction of the cognitive load of quality control workers},
  series = {PETRA '23: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments},
  booktitle = {PETRA '23: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments},
  publisher = {ACM},
  isbn      = {9798400700699},
  doi       = {10.1145/3594806.3596558},
  pages     = {535 -- 542},
  year      = {2023},
  abstract  = {This paper presents an approach for reducing the cognitive load for humans working in quality control (QC) for production processes that adhere to the 6σ -methodology. While 100\% QC requires every part to be inspected, this task can be reduced when a human-in-the-loop QC process gets supported by an anomaly detection system that only presents those parts for manual inspection that have a significant likelihood of being defective. This approach shows good results when applied to image-based QC for metal textile products.},
  language  = {en}
}