@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}
}
@incollection{FerreinNikolovskiLimpertetal.2023,
  author    = {Ferrein, Alexander and Nikolovski, Gjorgji and Limpert, Nicolas and Reke, Michael and Schiffer, Stefan and Scholl, Ingrid},
  title     = {Controlling a Fleet of Autonomous LHD Vehicles in Mining Operation},
  series = {Multi-Robot Systems - New Advances},
  booktitle = {Multi-Robot Systems - New Advances},
  editor    = {K{\"u}{\c{c}}{\"u}k, Serdar},
  publisher = {Intech Open},
  address   = {London},
  isbn      = {978-1-83768-290-4},
  doi       = {10.5772/intechopen.113044},
  pages     = {21 Seiten},
  year      = {2023},
  abstract  = {In this chapter, we report on our activities to create and maintain a fleet of autonomous load haul dump (LHD) vehicles for mining operations. The ever increasing demand for sustainable solutions and economic pressure causes innovation in the mining industry just like in any other branch. In this chapter, we present our approach to create a fleet of autonomous special purpose vehicles and to control these vehicles in mining operations. After an initial exploration of the site we deploy the fleet. Every vehicle is running an instance of our ROS 2-based architecture. The fleet is then controlled with a dedicated planning module. We also use continuous environment monitoring to implement a life-long mapping approach. In our experiments, we show that a combination of synthetic, augmented and real training data improves our classifier based on the deep learning network Yolo v5 to detect our vehicles, persons and navigation beacons. The classifier was successfully installed on the NVidia AGX-Drive platform, so that the abovementioned objects can be recognised during the dumper drive. The 3D poses of the detected beacons are assigned to lanelets and transferred to an existing map.},
  language  = {de}
}
@incollection{HarlacherAltepostElsenetal.2024,
  author    = {Harlacher, Markus and Altepost, Andrea and Elsen, Ingo and Ferrein, Alexander and Hansen-Ampah, Adjan and Merx, Wolfgang and Niehues, Sina and Schiffer, Stefan and Shahinfar, Fatemeh Nasim},
  title     = {Approach for the identification of requirements on the design of AI-supported work systems (in problem-based projects)},
  series = {AI in Business and Economics},
  booktitle = {AI in Business and Economics},
  editor    = {Lausberg, Isabel and Vogelsang, Michael},
  publisher = {De Gruyter},
  address   = {Berlin},
  isbn      = {9783110790320},
  doi       = {10.1515/9783110790320},
  pages     = {87 -- 99},
  year      = {2024},
  abstract  = {To successfully develop and introduce concrete artificial intelligence (AI) solutions in operational practice, a comprehensive process model is being tested in the WIRKsam joint project. It is based on a methodical approach that integrates human, technical and organisational aspects and involves employees in the process. The chapter focuses on the procedure for identifying requirements for a work system that is implementing AI in problem-driven projects and for selecting appropriate AI methods. This means that the use case has already been narrowed down at the beginning of the project and must be completely defined in the following. Initially, the existing preliminary work is presented. Based on this, an overview of all procedural steps and methods is given. All methods are presented in detail and good practice approaches are shown. Finally, a reflection of the developed procedure based on the application in nine companies is given.},
  language  = {en}
}