@inproceedings{AlhwarinFerreinScholl2019,
  author    = {Alhwarin, Faraj and Ferrein, Alexander and Scholl, Ingrid},
  title     = {An Efficient Hashing Algorithm for NN Problem in HD Spaces},
  series = {Lecture Notes in Computer Science},
  booktitle = {Lecture Notes in Computer Science},
  isbn      = {978-303005498-4},
  doi       = {10.1007/978-3-030-05499-1_6},
  pages     = {101 -- 115},
  year      = {2019},
  language  = {en}
}
@inproceedings{SchollBartellaMoluluoetal.2019,
  author    = {Scholl, Ingrid and Bartella, Alex and Moluluo, Cem and Ertural, Berat and Laing, Frederic and Suder, Sebastian},
  title     = {MedicVR : Acceleration and Enhancement Techniques for Direct Volume Rendering in Virtual Reality},
  series = {Bildverarbeitung f{\"u}r die Medizin 2019 : Algorithmen - Systeme - Anwendungen},
  booktitle = {Bildverarbeitung f{\"u}r die Medizin 2019 : Algorithmen - Systeme - Anwendungen},
  publisher = {Springer Vieweg},
  address   = {Wiesbaden},
  isbn      = {978-3-658-25326-4},
  doi       = {10.1007/978-3-658-25326-4_32},
  pages     = {152 -- 157},
  year      = {2019},
  language  = {en}
}
@inproceedings{WiesenEngemannLimpertetal.2018,
  author    = {Wiesen, Patrick and Engemann, Heiko and Limpert, Nicolas and Kallweit, Stephan},
  title     = {Learning by Doing - Mobile Robotics in the FH Aachen ROS Summer School},
  series = {European Robotics Forum 2018, TRROS18 Workshop},
  booktitle = {European Robotics Forum 2018, TRROS18 Workshop},
  pages     = {47 -- 58},
  year      = {2018},
  language  = {en}
}
@inproceedings{FerreinSchollNeumannetal.2019,
  author    = {Ferrein, Alexander and Scholl, Ingrid and Neumann, Tobias and Kr{\"u}ckel, Kai and Schiffer, Stefan},
  title     = {A system for continuous underground site mapping and exploration},
  doi       = {10.5772/intechopen.85859},
  pages     = {16 Seiten},
  year      = {2019},
  language  = {en}
}
@inproceedings{FerreinBharatheeshaSchifferetal.2019,
  author    = {Ferrein, Alexander and Bharatheesha, Mukunda and Schiffer, Stefan and Corbato, Carlos Hernandez},
  title     = {TRROS 2018 : Teaching Robotics with ROS Workshop at ERF 2018; Proceedings of the Workshop on Teaching Robotics with ROS (held at ERF 2018), co-located with European Robotics Forum 2018 (ERF 2018), Tampere, Finland, March 15th, 2018},
  series = {CEUR Workshop Proceedings},
  booktitle = {CEUR Workshop Proceedings},
  number    = {Vol-2329},
  issn      = {1613-0073},
  pages     = {68 Seiten},
  year      = {2019},
  language  = {en}
}
@article{ClaerFerreinSchiffer2019,
  author    = {Claer, Mario and Ferrein, Alexander and Schiffer, Stefan},
  title     = {Calibration of a Rotating or Revolving Platform with a LiDAR Sensor},
  series = {Applied Sciences},
  volume    = {Volume 9},
  journal   = {Applied Sciences},
  number    = {issue 11, 2238},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2076-3417},
  doi       = {10.3390/app9112238},
  pages     = {18 Seiten},
  year      = {2019},
  language  = {en}
}
@inproceedings{SteinbauerFerrein2019,
  author    = {Steinbauer, Gerald and Ferrein, Alexander},
  title     = {CogRob 2018 : Cognitive Robotics Workshop. Proceedings of the 11th Cognitive Robotics Workshop 2018 co-located with 16th International Conference on Principles of Knowledge Representation and Reasoning (KR 2018). Tempe, AZ, USA, October 27th, 2018.},
  series = {CEUR workshop proceedings},
  booktitle = {CEUR workshop proceedings},
  number    = {Vol-2325},
  issn      = {1613-0073},
  pages     = {46 Seiten},
  year      = {2019},
  language  = {en}
}
@article{FrankoDuKallweitetal.2020,
  author    = {Franko, Josef and Du, Shengzhi and Kallweit, Stephan and Duelberg, Enno Sebastian and Engemann, Heiko},
  title     = {Design of a Multi-Robot System for Wind Turbine Maintenance},
  series = {Energies},
  volume    = {13},
  journal   = {Energies},
  number    = {10},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1996-1073},
  doi       = {10.3390/en13102552},
  pages     = {Article 2552},
  year      = {2020},
  abstract  = {The maintenance of wind turbines is of growing importance considering the transition to renewable energy. This paper presents a multi-robot-approach for automated wind turbine maintenance including a novel climbing robot. Currently, wind turbine maintenance remains a manual task, which is monotonous, dangerous, and also physically demanding due to the large scale of wind turbines. Technical climbers are required to work at significant heights, even in bad weather conditions. Furthermore, a skilled labor force with sufficient knowledge in repairing fiber composite material is rare. Autonomous mobile systems enable the digitization of the maintenance process. They can be designed for weather-independent operations. This work contributes to the development and experimental validation of a maintenance system consisting of multiple robotic platforms for a variety of tasks, such as wind turbine tower and rotor blade service. In this work, multicopters with vision and LiDAR sensors for global inspection are used to guide slower climbing robots. Light-weight magnetic climbers with surface contact were used to analyze structure parts with non-destructive inspection methods and to locally repair smaller defects. Localization was enabled by adapting odometry for conical-shaped surfaces considering additional navigation sensors. Magnets were suitable for steel towers to clamp onto the surface. A friction-based climbing ring robot (SMART— Scanning, Monitoring, Analyzing, Repair and Transportation) completed the set-up for higher payload. The maintenance period could be extended by using weather-proofed maintenance robots. The multi-robot-system was running the Robot Operating System (ROS). Additionally, first steps towards machine learning would enable maintenance staff to use pattern classification for fault diagnosis in order to operate safely from the ground in the future.},
  language  = {en}
}
@inproceedings{ChajanSchulteTiggesRekeetal.2021,
  author    = {Chajan, Eduard and Schulte-Tigges, Joschua and Reke, Michael and Ferrein, Alexander and Matheis, Dominik and Walter, Thomas},
  title     = {GPU based model-predictive path control for self-driving vehicles},
  series = {IEEE Intelligent Vehicles Symposium (IV)},
  booktitle = {IEEE Intelligent Vehicles Symposium (IV)},
  publisher = {IEEE},
  isbn      = {978-1-7281-5394-0},
  doi       = {10.1109/IV48863.2021.9575619},
  pages     = {1243 -- 1248},
  year      = {2021},
  abstract  = {One central challenge for self-driving cars is a proper path-planning. Once a trajectory has been found, the next challenge is to accurately and safely follow the precalculated path. The model-predictive controller (MPC) is a common approach for the lateral control of autonomous vehicles. The MPC uses a vehicle dynamics model to predict the future states of the vehicle for a given prediction horizon. However, in order to achieve real-time path control, the computational load is usually large, which leads to short prediction horizons. To deal with the computational load, the control algorithm can be parallelized on the graphics processing unit (GPU). In contrast to the widely used stochastic methods, in this paper we propose a deterministic approach based on grid search. Our approach focuses on systematically discovering the search area with different levels of granularity. To achieve this, we split the optimization algorithm into multiple iterations. The best sequence of each iteration is then used as an initial solution to the next iteration. The granularity increases, resulting in smooth and predictable steering angle sequences. We present a novel GPU-based algorithm and show its accuracy and realtime abilities with a number of real-world experiments.},
  language  = {en}
}
@inproceedings{UlmerBraunChengetal.2021,
  author    = {Ulmer, Jessica and Braun, Sebastian and Cheng, Chi-Tsun and Dowey, Steve and Wollert, J{\"o}rg},
  title     = {Adapting Augmented Reality Systems to the users' needs using Gamification and error solving methods},
  series = {Procedia CIRP},
  volume    = {104},
  booktitle = {Procedia CIRP},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2212-8271},
  doi       = {10.1016/j.procir.2021.11.024},
  pages     = {140 -- 145},
  year      = {2021},
  abstract  = {Animations of virtual items in AR support systems are typically predefined and lack interactions with dynamic physical environments. AR applications rarely consider users' preferences and do not provide customized spontaneous support under unknown situations. This research focuses on developing adaptive, error-tolerant AR systems based on directed acyclic graphs and error resolving strategies. Using this approach, users will have more freedom of choice during AR supported work, which leads to more efficient workflows. Error correction methods based on CAD models and predefined process data create individual support possibilities. The framework is implemented in the Industry 4.0 model factory at FH Aachen.},
  language  = {en}
}