@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} } @article{SchulteTiggesFoersterNikolovskietal.2022, author = {Schulte-Tigges, Joschua and F{\"o}rster, Marco and Nikolovski, Gjorgji and Reke, Michael and Ferrein, Alexander and Kaszner, Daniel and Matheis, Dominik and Walter, Thomas}, title = {Benchmarking of various LiDAR sensors for use in self-driving vehicles in real-world environments}, series = {Sensors}, volume = {22}, journal = {Sensors}, number = {19}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {10.3390/s22197146}, pages = {20 Seiten}, year = {2022}, abstract = {Abstract In this paper, we report on our benchmark results of the LiDAR sensors Livox Horizon, Robosense M1, Blickfeld Cube, Blickfeld Cube Range, Velodyne Velarray H800, and Innoviz Pro. The idea was to test the sensors in different typical scenarios that were defined with real-world use cases in mind, in order to find a sensor that meet the requirements of self-driving vehicles. For this, we defined static and dynamic benchmark scenarios. In the static scenarios, both LiDAR and the detection target do not move during the measurement. In dynamic scenarios, the LiDAR sensor was mounted on the vehicle which was driving toward the detection target. We tested all mentioned LiDAR sensors in both scenarios, show the results regarding the detection accuracy of the targets, and discuss their usefulness for deployment in self-driving cars.}, language = {en} } @inproceedings{SchulteTiggesMatheisRekeetal.2023, author = {Schulte-Tigges, Joschua and Matheis, Dominik and Reke, Michael and Walter, Thomas and Kaszner, Daniel}, title = {Demonstrating a V2X enabled system for transition of control and minimum risk manoeuvre when leaving the operational design domain}, series = {HCII 2023: HCI in Mobility, Transport, and Automotive Systems}, booktitle = {HCII 2023: HCI in Mobility, Transport, and Automotive Systems}, editor = {Kr{\"o}mker, Heidi}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-35677-3 (Print)}, doi = {10.1007/978-3-031-35678-0_12}, pages = {200 -- 210}, year = {2023}, abstract = {Modern implementations of driver assistance systems are evolving from a pure driver assistance to a independently acting automation system. Still these systems are not covering the full vehicle usage range, also called operational design domain, which require the human driver as fall-back mechanism. Transition of control and potential minimum risk manoeuvres are currently research topics and will bridge the gap until full autonomous vehicles are available. The authors showed in a demonstration that the transition of control mechanisms can be further improved by usage of communication technology. Receiving the incident type and position information by usage of standardised vehicle to everything (V2X) messages can improve the driver safety and comfort level. The connected and automated vehicle's software framework can take this information to plan areas where the driver should take back control by initiating a transition of control which can be followed by a minimum risk manoeuvre in case of an unresponsive driver. This transition of control has been implemented in a test vehicle and was presented to the public during the IEEE IV2022 (IEEE Intelligent Vehicle Symposium) in Aachen, Germany.}, language = {en} } @article{SteinbauerFerrein2016, author = {Steinbauer, Gerald and Ferrein, Alexander}, title = {20 Years of RoboCup}, series = {KI - K{\"u}nstliche Intelligenz}, volume = {30}, journal = {KI - K{\"u}nstliche Intelligenz}, number = {3-4}, publisher = {Springer}, address = {Berlin}, issn = {1610-1987}, doi = {10.1007/s13218-016-0442-z}, pages = {221 -- 224}, year = {2016}, 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} } @inproceedings{StopforthDavrajhFerrein2017, author = {Stopforth, Riaan and Davrajh, Shaniel and Ferrein, Alexander}, title = {South African robotics entity for a collaboration initiative}, series = {Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech), 2016}, booktitle = {Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech), 2016}, publisher = {IEEE}, isbn = {978-1-5090-3335-5}, doi = {10.1109/RoboMech.2016.7813144}, pages = {1 -- 6}, year = {2017}, language = {en} } @inproceedings{StopforthDavrajhFerrein2017, author = {Stopforth, Riaan and Davrajh, Shaniel and Ferrein, Alexander}, title = {Design considerations of the duo fugam dual rotor UAV}, series = {2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech)}, booktitle = {2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech)}, isbn = {978-1-5386-2314-5}, doi = {10.1109/RoboMech.2017.8261115}, pages = {7 -- 13}, year = {2017}, language = {en} } @article{UlmerBraunChengetal.2023, author = {Ulmer, Jessica and Braun, Carsten and Cheng, Chi-Tsun and Dowey, Steve and Wollert, J{\"o}rg}, title = {A human factors-aware assistance system in manufacturing based on gamification and hardware modularisation}, series = {International Journal of Production Research}, journal = {International Journal of Production Research}, publisher = {Taylor \& Francis}, issn = {0020-7543 (Print)}, doi = {10.1080/00207543.2023.2166140}, year = {2023}, abstract = {Assistance systems have been widely adopted in the manufacturing sector to facilitate various processes and tasks in production environments. However, existing systems are mostly equipped with rigid functional logic and do not provide individual user experiences or adapt to their capabilities. This work integrates human factors in assistance systems by adjusting the hardware and instruction presented to the workers' cognitive and physical demands. A modular system architecture is designed accordingly, which allows a flexible component exchange according to the user and the work task. Gamification, the use of game elements in non-gaming contexts, has been further adopted in this work to provide level-based instructions and personalised feedback. The developed framework is validated by applying it to a manual workstation for industrial assembly routines.}, 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} } @inproceedings{UlmerBraunChengetal.2022, author = {Ulmer, Jessica and Braun, Sebastian and Cheng, Chi-Tsun and Dowey, Steve and Wollert, J{\"o}rg}, title = {Usage of digital twins for gamification applications in manufacturing}, series = {Procedia CIRP}, volume = {107}, booktitle = {Procedia CIRP}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2212-8271}, doi = {10.1016/j.procir.2022.05.044}, pages = {675 -- 680}, year = {2022}, abstract = {Gamification applications are on the rise in the manufacturing sector to customize working scenarios, offer user-specific feedback, and provide personalized learning offerings. Commonly, different sensors are integrated into work environments to track workers' actions. Game elements are selected according to the work task and users' preferences. However, implementing gamified workplaces remains challenging as different data sources must be established, evaluated, and connected. Developers often require information from several areas of the companies to offer meaningful gamification strategies for their employees. Moreover, work environments and the associated support systems are usually not flexible enough to adapt to personal needs. Digital twins are one primary possibility to create a uniform data approach that can provide semantic information to gamification applications. Frequently, several digital twins have to interact with each other to provide information about the workplace, the manufacturing process, and the knowledge of the employees. This research aims to create an overview of existing digital twin approaches for digital support systems and presents a concept to use digital twins for gamified support and training systems. The concept is based upon the Reference Architecture Industry 4.0 (RAMI 4.0) and includes information about the whole life cycle of the assets. It is applied to an existing gamified training system and evaluated in the Industry 4.0 model factory by an example of a handle mounting.}, language = {en} }