@article{LeingartnerMaurerFerreinetal.2016, author = {Leingartner, Max and Maurer, Johannes and Ferrein, Alexander and Steinbauer, Gerald}, title = {Evaluation of Sensors and Mapping Approaches for Disasters in Tunnels}, series = {Journal of Field Robotics}, volume = {33}, journal = {Journal of Field Robotics}, number = {8}, publisher = {Wiley-VCH}, address = {Weinheim}, issn = {1556-4967}, doi = {10.1002/rob.21611}, pages = {1037 -- 1057}, year = {2016}, abstract = {Ground or aerial robots equipped with advanced sensing technologies, such as three-dimensional laser scanners and advanced mapping algorithms, are deemed useful as a supporting technology for first responders. A great deal of excellent research in the field exists, but practical applications at real disaster sites are scarce. Many projects concentrate on equipping robots with advanced capabilities, such as autonomous exploration or object manipulation. In spite of this, realistic application areas for such robots are limited to teleoperated reconnaissance or search. In this paper, we investigate how well state-of-the-art and off-the-shelf components and algorithms are suited for reconnaissance in current disaster-relief scenarios. The basic idea is to make use of some of the most common sensors and deploy some widely used algorithms in a disaster situation, and to evaluate how well the components work for these scenarios. We acquired the sensor data from two field experiments, one from a disaster-relief operation in a motorway tunnel, and one from a mapping experiment in a partly closed down motorway tunnel. Based on these data, which we make publicly available, we evaluate state-of-the-art and off-the-shelf mapping approaches. In our analysis, we integrate opinions and replies from first responders as well as from some algorithm developers on the usefulness of the data and the limitations of the deployed approaches, respectively. We discuss the lessons we learned during the two missions. These lessons are interesting for the community working in similar areas of urban search and rescue, particularly reconnaissance and search.}, language = {en} } @inproceedings{NiemuellerReuterFerreinetal.2016, author = {Niemueller, Tim and Reuter, Sebastian and Ferrein, Alexander and Jeschke, Sabina and Lakemeyer, Gerhard}, title = {Evaluation of the RoboCup Logistics League and Derived Criteria for Future Competitions}, series = {RoboCup 2015: Robot World Cup XIX}, booktitle = {RoboCup 2015: Robot World Cup XIX}, editor = {Almeida, Luis}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-29339-4}, doi = {10.1007/978-3-319-29339-4_3}, pages = {31 -- 43}, year = {2016}, language = {en} } @inproceedings{NiemuellerReuterFerrein2016, author = {Niemueller, Tim and Reuter, Sebastian and Ferrein, Alexander}, title = {Fawkes for the RoboCup Logistics League}, series = {RoboCup 2015: Robot World Cup XIX}, booktitle = {RoboCup 2015: Robot World Cup XIX}, editor = {Almeida, Luis}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-29339-4}, doi = {10.1007/978-3-319-29339-4_31}, pages = {365 -- 373}, year = {2016}, language = {en} } @article{UlmerBraunChengetal.2022, author = {Ulmer, Jessica and Braun, Sebastian and Cheng, Chi-Tsun and Dowey, Steve and Wollert, J{\"o}rg}, title = {Gamification of virtual reality assembly training: Effects of a combined point and level system on motivation and training results}, series = {International Journal of Human-Computer Studies}, volume = {165}, journal = {International Journal of Human-Computer Studies}, number = {Art. No. 102854}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1071-5819}, doi = {10.1016/j.ijhcs.2022.102854}, year = {2022}, abstract = {Virtual Reality (VR) offers novel possibilities for remote training regardless of the availability of the actual equipment, the presence of specialists, and the training locations. Research shows that training environments that adapt to users' preferences and performance can promote more effective learning. However, the observed results can hardly be traced back to specific adaptive measures but the whole new training approach. This study analyzes the effects of a combined point and leveling VR-based gamification system on assembly training targeting specific training outcomes and users' motivations. The Gamified-VR-Group with 26 subjects received the gamified training, and the Non-Gamified-VR-Group with 27 subjects received the alternative without gamified elements. Both groups conducted their VR training at least three times before assembling the actual structure. The study found that a level system that gradually increases the difficulty and error probability in VR can significantly lower real-world error rates, self-corrections, and support usages. According to our study, a high error occurrence at the highest training level reduced the Gamified-VR-Group's feeling of competence compared to the Non-Gamified-VR-Group, but at the same time also led to lower error probabilities in real-life. It is concluded that a level system with a variable task difficulty should be combined with carefully balanced positive and negative feedback messages. This way, better learning results, and an improved self-evaluation can be achieved while not causing significant impacts on the participants' feeling of competence.}, language = {en} } @inproceedings{UlmerBraunChengetal.2020, author = {Ulmer, Jessica and Braun, Sebastian and Cheng, Chi-Tsun and Dowey, Steve and Wollert, J{\"o}rg}, title = {Gamified Virtual Reality Training Environment for the Manufacturing Industry}, doi = {10.1109/ME49197.2020.9286661}, pages = {1 -- 6}, year = {2020}, language = {de} } @inproceedings{MatareSchifferFerrein2019, author = {Matar{\´e}, Victor and Schiffer, Stefan and Ferrein, Alexander}, title = {golog++ : An integrative system design}, series = {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}, booktitle = {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}, editor = {Steinbauer, Gerald and Ferrein, Alexander}, issn = {1613-0073}, pages = {29 -- 35}, year = {2019}, 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} } @article{ChengWollertChenetal.2023, author = {Cheng, Chi-Tsun and Wollert, J{\"o}rg and Chen, Xi and Fapojuwo, Abraham O.}, title = {Guest Editorial : Circuits and Systems for Industry X.0 Applications}, series = {IEEE Journal on Emerging and Selected Topics in Circuits and Systems}, volume = {13}, journal = {IEEE Journal on Emerging and Selected Topics in Circuits and Systems}, edition = {2}, publisher = {IEEE}, address = {New York}, issn = {2156-3357 (Print)}, doi = {10.1109/JETCAS.2023.3278843}, pages = {457 -- 460}, year = {2023}, language = {en} } @inproceedings{NiemuellerNeumannHenkeetal.2017, author = {Niemueller, Tim and Neumann, Tobias and Henke, Christoph and Sch{\"o}nitz, Sebastian and Reuter, Sebastian and Ferrein, Alexander and Jeschke, Sabina and Lakemeyer, Gerhard}, title = {Improvements for a robust production in the RoboCup logistics league 2016}, series = {RoboCup 2016: Robot World Cup XX. RoboCup 2016.}, booktitle = {RoboCup 2016: Robot World Cup XX. RoboCup 2016.}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-68792-6}, doi = {10.1007/978-3-319-68792-6_49}, pages = {589 -- 600}, year = {2017}, language = {en} } @inproceedings{AlhwarinFerreinGebhardtetal.2015, author = {Alhwarin, Faraj and Ferrein, Alexander and Gebhardt, Andreas and Kallweit, Stephan and Scholl, Ingrid and Tedjasukmana, Osmond Sanjaya}, title = {Improving additive manufacturing by image processing and robotic milling}, series = {2015 IEEE International Conference on Automation Science and Engineering (CASE), Aug 24-28, 2015 Gothenburg, Sweden}, booktitle = {2015 IEEE International Conference on Automation Science and Engineering (CASE), Aug 24-28, 2015 Gothenburg, Sweden}, doi = {10.1109/CoASE.2015.7294217}, pages = {924 -- 929}, year = {2015}, language = {en} }