TY - CHAP A1 - Schiemann, Markus A1 - Hodapp, Jan A1 - Berger, Ulrich T1 - Collaboration-Gap: A bus-modular architecture for human-robot-collaboration systems in production environments T2 - International Symposium on Robotics, ISR 2018, June 20-21, 2018 Messe München, Entrance East, Munich, Germany N2 - Due to the extensive changes along with Industry 4.0 many established barriers drop. One of those barriers which increasingly disappears separates the workspace between human beings and robots and thus prevents a versatile collaborating environment. This change has led to the development of a wide field of research focusing on the prevention of human-robot impacts and/or the minimization of related risks. This paper is focused on safety regulations introduced as a consequence of those developments over the last ten years and particularly on an approach for demonstrating the unintended gap arising between human and robot due to those regulations. Modularity and integrability as two of the key characteristics in the concept of Reconfigurable Manufacturing Systems (RMS), serve as the comparative parameters. KW - Human-Robot-Collaboration KW - Robot Safety Y1 - 2018 SN - 978-3-8007-4699-6 SP - 450 EP - 454 PB - VDE Verlag CY - Berlin [u.a.] ER - TY - GEN A1 - Schiemann, Markus A1 - Berger, Ulrich A1 - Zürn, Michael A1 - Reichenbach, Matthias T1 - Mensch-Roboter-Kollaboration im Spannungsfeld von Rekonfigurierbarkeit, Sicherheit und Effizienz T2 - 20. Leitkongress der Mess- und Automatisierungstechnik ; AUTOMATION 2019 KW - Mensch-Roboter-Kollaboration KW - Sicherheitstechnik Y1 - 2019 SN - 978-3-18-092351-2 SP - 5 EP - 14 PB - VDI CY - Baden-Banden ER - TY - GEN A1 - Schiemann, Markus A1 - Berger, Ulrich T1 - Mensch-Roboter-Kollaboration in einem vernetzten Produktionsumfeld, SmartSafety: Ein beschleunigter Inbetriebnahmeprozess T2 - Zeitschrift für wirtschaftlichen Fabrikbetrieb : ZWF Y1 - 2019 U6 - https://doi.org/10.3139/104.112108 SN - 2511-0896 VL - 114 IS - 6 SP - 364 EP - 366 ER - TY - GEN A1 - Hodapp, Jan A1 - Schiemann, Markus A1 - Bilous, Vadym A1 - Arcidiacono, Claudio Salvatore A1 - Reichenbach, Matthias T1 - Advances in Automated Generation of Convolutional Neural Networks from Synthetic Data in Industrial Environment T2 - 53rd Hawaii International Conference on System Sciences: Maui, Hawaii, United States of America, Jan 07 - 10, 2020 N2 - The usage of convolutional neural networks has revolutionized data processing and its application in the industry during the last few years. Especially detection in images, a historically hard task to automate is now available on every smart phone. Nonetheless, this technology has not yet spread in the industry of car production, where lots of visual tests and quality checks are still performed manually. Even though the vision capabilities convolutional neural networks can give machines are already respectable, they still need well prepared training data that is costly and time-consuming to produce. This paper describes our effort to test and improve a system to automatically synthesize training images. This existing system renders computer aided design models into scenes and out of that produces realistic images and corresponding labels. Two new models, Single Shot Detector and RetinaNet are retrained under the use of distractors and then tested against each other. The better performing RetinaNet is then tested for performance under training with a variety of datasets from different domains in order to observe the models strength and weakness under domain shifts. These domains are real photographs, rendered models and images of objects cut and pasted into different backgrounds. The results show that the model trained with a mixture of all domains performs best. Y1 - 2020 UR - https://hdl.handle.net/10125/64307 SN - 978-0-9981331-3-3 U6 - https://doi.org/10.24251/HICSS.2020.565 ER - TY - GEN A1 - Schiemann, Markus A1 - Berger, Ulrich A1 - Hodapp, Jan A1 - Zürn, Michael T1 - Roboskin: Increased Robot Working Speed Within Human-Robot-Collaboration Safety Regulations T2 - Conference Proceedings (IEEE Xplore) KW - Human-Robot-Collaboration KW - Safety Y1 - 2019 SN - 978-1-7281-3325-6 U6 - https://doi.org/10.1109/ICCAR.2019.8813448 SN - 2251-2446 ER -