TY - CHAP A1 - Scholl, Ingrid A1 - Bartella, Alex A1 - Moluluo, Cem A1 - Ertural, Berat A1 - Laing, Frederic A1 - Suder, Sebastian T1 - MedicVR : Acceleration and Enhancement Techniques for Direct Volume Rendering in Virtual Reality T2 - Bildverarbeitung für die Medizin 2019 : Algorithmen – Systeme – Anwendungen Y1 - 2019 SN - 978-3-658-25326-4 U6 - https://doi.org/10.1007/978-3-658-25326-4_32 SP - 152 EP - 157 PB - Springer Vieweg CY - Wiesbaden ER - TY - JOUR A1 - Schulte-Tigges, Joschua A1 - Förster, Marco A1 - Nikolovski, Gjorgji A1 - Reke, Michael A1 - Ferrein, Alexander A1 - Kaszner, Daniel A1 - Matheis, Dominik A1 - Walter, Thomas T1 - Benchmarking of various LiDAR sensors for use in self-driving vehicles in real-world environments JF - Sensors N2 - 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. KW - Lidar KW - Benchmark KW - Self-driving Y1 - 2022 U6 - https://doi.org/10.3390/s22197146 SN - 1424-8220 N1 - This article belongs to the Special Issue "Sensor Fusion for Vehicles Navigation and Robotic Systems" VL - 22 IS - 19 PB - MDPI CY - Basel ER - TY - CHAP A1 - Schulte-Tigges, Joschua A1 - Matheis, Dominik A1 - Reke, Michael A1 - Walter, Thomas A1 - Kaszner, Daniel ED - Krömker, Heidi T1 - Demonstrating a V2X enabled system for transition of control and minimum risk manoeuvre when leaving the operational design domain T2 - HCII 2023: HCI in Mobility, Transport, and Automotive Systems N2 - 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. KW - V2X KW - Transiton of Control KW - Minimum Risk Manoeuvre KW - Operational Design Domain KW - Connected Automated Vehicle Y1 - 2023 SN - 978-3-031-35677-3 (Print) SN - 978-3-031-35678-0 (Online) U6 - https://doi.org/10.1007/978-3-031-35678-0_12 N1 - 5th International Conference, MobiTAS 2023, Held as Part of the 25th HCI International Conference, HCII 2023, Copenhagen, Denmark, July 23–28, 2023. SP - 200 EP - 210 PB - Springer CY - Cham ER - TY - JOUR A1 - Steinbauer, Gerald A1 - Ferrein, Alexander T1 - 20 Years of RoboCup JF - KI - Künstliche Intelligenz Y1 - 2016 U6 - https://doi.org/10.1007/s13218-016-0442-z SN - 1610-1987 VL - 30 IS - 3-4 SP - 221 EP - 224 PB - Springer CY - Berlin ER - TY - CHAP A1 - Steinbauer, Gerald A1 - Ferrein, Alexander T1 - 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. T2 - CEUR workshop proceedings Y1 - 2019 SN - 1613-0073 N1 - edited by Gerald Steinbauer, Alexander Ferrein IS - Vol-2325 ER - TY - CHAP A1 - Stopforth, Riaan A1 - Davrajh, Shaniel A1 - Ferrein, Alexander T1 - South African robotics entity for a collaboration initiative T2 - Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech), 2016 Y1 - 2017 SN - 978-1-5090-3335-5 U6 - https://doi.org/10.1109/RoboMech.2016.7813144 N1 - PRASA-RobMech, Nov. 30 2016-Dec. 2 2016, Stellenbosch, South Africa SP - 1 EP - 6 PB - IEEE ER - TY - CHAP A1 - Stopforth, Riaan A1 - Davrajh, Shaniel A1 - Ferrein, Alexander T1 - Design considerations of the duo fugam dual rotor UAV T2 - 2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech) Y1 - 2017 SN - 978-1-5386-2314-5 U6 - https://doi.org/10.1109/RoboMech.2017.8261115 SP - 7 EP - 13 ER - TY - CHAP A1 - Ulmer, Jessica A1 - Braun, Sebastian A1 - Cheng, Chi-Tsun A1 - Dowey, Steve A1 - Wollert, Jörg T1 - Adapting augmented reality systems to the users’ needs using gamification and error solving methods T2 - Procedia CIRP - 54th CIRP CMS 2021 - Towards Digitalized Manufacturing 4.0 N2 - 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. KW - Augmented Reality KW - Adaptive Systems KW - Gamification KW - Error Recovery Y1 - 2021 U6 - https://doi.org/10.1016/j.procir.2021.11.024 SN - 2212-8271 N1 - CIRP CMS 2021 - 54th CIRP Conference on Manufacturing Systems, September 22-24, 2021, online VL - 104 SP - 140 EP - 145 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Ulmer, Jessica A1 - Braun, Sebastian A1 - Cheng, Chi-Tsun A1 - Dowey, Steve A1 - Wollert, Jörg T1 - Gamification of virtual reality assembly training: Effects of a combined point and level system on motivation and training results JF - International Journal of Human-Computer Studies N2 - 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. KW - Gamification KW - Virtual reality KW - Assembly KW - User study KW - Level system Y1 - 2022 U6 - https://doi.org/10.1016/j.ijhcs.2022.102854 SN - 1071-5819 VL - 165 IS - Art. No. 102854 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Ulmer, Jessica A1 - Braun, Sebastian A1 - Cheng, Chi-Tsun A1 - Dowey, Steve A1 - Wollert, Jörg T1 - Gamified Virtual Reality Training Environment for the Manufacturing Industry T2 - Proceedings of the 2020 19th International Conference on Mechatronics – Mechatronika (ME) N2 - 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. Y1 - 2020 U6 - https://doi.org/10.1109/ME49197.2020.9286661 N1 - 2020 19th International Conference on Mechatronics – Mechatronika (ME), Prague, Czech Republic, December 2–4, 2020 SP - 1 EP - 6 PB - IEEE CY - New York, NY ER -