@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}
}
@inproceedings{UlmerBraunChengetal.2020,
  author    = {Ulmer, Jessica and Braun, Sebastian and Cheng, Chi-Tsun and Dowey, Steve and Wollert, J{\"o}rg F.},
  title     = {Gamified Virtual Reality Training Environment for the Manufacturing Industry},
  series = {Proceedings of the 2020 19th International Conference on Mechatronics - Mechatronika (ME)},
  booktitle = {Proceedings of the 2020 19th International Conference on Mechatronics - Mechatronika (ME)},
  publisher = {IEEE},
  address   = {New York, NY},
  doi       = {10.1109/ME49197.2020.9286661},
  pages     = {1 -- 6},
  year      = {2020},
  abstract  = {Industry 4.0 imposes many challenges for manufacturing companies and their employees. Innovative and effective training strategies are required to cope with fast-changing production environments and new manufacturing technologies. Virtual Reality (VR) offers new ways of on-the-job, on-demand, and off-premise training. A novel concept and evaluation system combining Gamification and VR practice for flexible assembly tasks is proposed in this paper and compared to existing works. It is based on directed acyclic graphs and a leveling system. The concept enables a learning speed which is adjustable to the users' pace and dynamics, while the evaluation system facilitates adaptive work sequences and allows employee-specific task fulfillment. The concept was implemented and analyzed in the Industry 4.0 model factory at FH Aachen for mechanical assembly jobs.},
  language  = {de}
}
@incollection{NiemuellerLakemeyerReuteretal.2017,
  author    = {Niemueller, T. and Lakemeyer, G. and Reuter, S. and Jeschke, S. and Ferrein, Alexander},
  title     = {Benchmarking of Cyber-Physical Systems in Industrial Robotics: The RoboCup Logistics League as a CPS Benchmark Blueprint},
  series = {Cyber-Physical Systems: Foundations, Principles and Applications},
  booktitle = {Cyber-Physical Systems: Foundations, Principles and Applications},
  publisher = {Academic Press},
  address   = {London},
  doi       = {10.1016/B978-0-12-803801-7.00013-4},
  pages     = {193 -- 207},
  year      = {2017},
  abstract  = {In the future, we expect manufacturing companies to follow a new paradigm that mandates more automation and autonomy in production processes. Such smart factories will offer a variety of production technologies as services that can be combined ad hoc to produce a large number of different product types and variants cost-effectively even in small lot sizes. This is enabled by cyber-physical systems that feature flexible automated planning methods for production scheduling, execution control, and in-factory logistics. During development, testbeds are required to determine the applicability of integrated systems in such scenarios. Furthermore, benchmarks are needed to quantify and compare system performance in these industry-inspired scenarios at a comprehensible and manageable size which is, at the same time, complex enough to yield meaningful results. In this chapter, based on our experience in the RoboCup Logistics League (RCLL) as a specific example, we derive a generic blueprint for how a holistic benchmark can be developed, which combines a specific scenario with a set of key performance indicators as metrics to evaluate the overall integrated system and its components.},
  language  = {de}
}
@article{NiemuellerKarrasFerrein2017,
  author    = {Niemueller, Tim and Karras, Ulrich and Ferrein, Alexander},
  title     = {Meisterschaft der Maschinen: Die Industrial Logistic Liga},
  series = {C´t Magazin f{\"u}r Computertechnik},
  journal   = {C´t Magazin f{\"u}r Computertechnik},
  number    = {26},
  year      = {2017},
  language  = {de}
}
@inproceedings{Ferrein2015,
  author    = {Ferrein, Alexander},
  title     = {3D-Mapping von Straßentunneln},
  series = {16. Geokinematischer Tag, 07. und 08. Mai 2015 : Tagungsband / Technische Universit{\"a}t Bergakademie Freiberg},
  booktitle = {16. Geokinematischer Tag, 07. und 08. Mai 2015 : Tagungsband / Technische Universit{\"a}t Bergakademie Freiberg},
  pages     = {31 -- 40},
  year      = {2015},
  language  = {de}
}