@inproceedings{HueningStuettgen2021, author = {H{\"u}ning, Felix and St{\"u}ttgen, Marcel}, title = {Work in Progress: Interdisciplinary projects in times of COVID-19 crisis - challenges, risks and chances}, series = {2021 IEEE Global Engineering Education Conference (EDUCON)}, booktitle = {2021 IEEE Global Engineering Education Conference (EDUCON)}, doi = {10.1109/EDUCON46332.2021.9454006}, pages = {1175 -- 1179}, year = {2021}, language = {en} } @inproceedings{NikolovskiRekeElsenetal.2021, author = {Nikolovski, Gjorgji and Reke, Michael and Elsen, Ingo and Schiffer, Stefan}, title = {Machine learning based 3D object detection for navigation in unstructured environments}, series = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)}, booktitle = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)}, publisher = {IEEE}, isbn = {978-1-6654-7921-9}, doi = {10.1109/IVWorkshops54471.2021.9669218}, pages = {236 -- 242}, year = {2021}, abstract = {In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine.}, language = {en} } @inproceedings{RitschelStenzelCzarneckietal.2021, author = {Ritschel, Konstantin and Stenzel, Adina and Czarnecki, Christian and Hong, Chin-Gi}, title = {Realizing robotic process automation potentials: an architectural perspective on a real-life implementation case}, series = {GI Edition Proceedings Band 314 "INFORMATIK 2021" Computer Science \& Sustainability}, booktitle = {GI Edition Proceedings Band 314 "INFORMATIK 2021" Computer Science \& Sustainability}, editor = {Gesellschaft f{\"u}r Informatik e.V. (GI),}, publisher = {K{\"o}llen}, address = {Bonn}, isbn = {9783885797081}, issn = {1617-5468}, pages = {1303 -- 1311}, year = {2021}, abstract = {The initial idea of Robotic Process Automation (RPA) is the automation of business processes through a simple emulation of user input and output by software robots. Hence, it can be assumed that no changes of the used software systems and existing Enterprise Architecture (EA) is required. In this short, practical paper we discuss this assumption based on a real-life implementation project. We show that a successful RPA implementation might require architectural work during analysis, implementation, and migration. As practical paper we focus on exemplary lessons-learned and new questions related to RPA and EA.}, language = {en} } @inproceedings{FerreinMeessenLimpertetal.2021, author = {Ferrein, Alexander and Meeßen, Marcus and Limpert, Nicolas and Schiffer, Stefan}, title = {Compiling ROS Schooling Curricula via Contentual Taxonomies}, series = {Robotics in Education}, booktitle = {Robotics in Education}, editor = {Lepuschitz, Wilfried}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-67411-3}, doi = {10.1007/978-3-030-67411-3_5}, pages = {49 -- 60}, year = {2021}, language = {en} } @inproceedings{HeuermannHarzheimMuehmel2021, author = {Heuermann, Holger and Harzheim, Thomas and M{\"u}hmel, Marc}, title = {A maritime harmonic radar search and rescue system using passive and active tags}, series = {2020 17th European Radar Conference (EuRAD)}, booktitle = {2020 17th European Radar Conference (EuRAD)}, publisher = {IEEE}, isbn = {978-2-87487-061-3}, doi = {10.1109/EuRAD48048.2021.00030}, pages = {73 -- 76}, year = {2021}, language = {en} } @inproceedings{DeyElsenFerreinetal.2021, author = {Dey, Thomas and Elsen, Ingo and Ferrein, Alexander and Frauenrath, Tobias and Reke, Michael and Schiffer, Stefan}, title = {CO2 Meter: a do-it-yourself carbon dioxide measuring device for the classroom}, series = {PETRA 2021: The 14th PErvasive Technologies Related to Assistive Environments Conference}, booktitle = {PETRA 2021: The 14th PErvasive Technologies Related to Assistive Environments Conference}, editor = {Makedon, Fillia}, publisher = {Association for Computing Machinery}, address = {New York}, isbn = {9781450387927}, doi = {10.1145/3453892.3462697}, pages = {292 -- 299}, year = {2021}, abstract = {In this paper we report on CO2 Meter, a do-it-yourself carbon dioxide measuring device for the classroom. Part of the current measures for dealing with the SARS-CoV-2 pandemic is proper ventilation in indoor settings. This is especially important in schools with students coming back to the classroom even with high incidents rates. Static ventilation patterns do not consider the individual situation for a particular class. Influencing factors like the type of activity, the physical structure or the room occupancy are not incorporated. Also, existing devices are rather expensive and often provide only limited information and only locally without any networking. This leaves the potential of analysing the situation across different settings untapped. Carbon dioxide level can be used as an indicator of air quality, in general, and of aerosol load in particular. Since, according to the latest findings, SARS-CoV-2 can be transmitted primarily in the form of aerosols, carbon dioxide may be used as a proxy for the risk of a virus infection. Hence, schools could improve the indoor air quality and potentially reduce the infection risk if they actually had measuring devices available in the classroom. Our device supports schools in ventilation and it allows for collecting data over the Internet to enable a detailed data analysis and model generation. First deployments in schools at different levels were received very positively. A pilot installation with a larger data collection and analysis is underway.}, language = {en} } @inproceedings{EltesterFerreinSchiffer2020, author = {Eltester, Niklas Sebastian and Ferrein, Alexander and Schiffer, Stefan}, title = {A smart factory setup based on the RoboCup logistics league}, series = {2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS)}, booktitle = {2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS)}, publisher = {IEEE}, doi = {10.1109/ICPS48405.2020.9274766}, pages = {297 -- 302}, year = {2020}, abstract = {In this paper we present SMART-FACTORY, a setup for a research and teaching facility in industrial robotics that is based on the RoboCup Logistics League. It is driven by the need for developing and applying solutions for digital production. Digitization receives constantly increasing attention in many areas, especially in industry. The common theme is to make things smart by using intelligent computer technology. Especially in the last decade there have been many attempts to improve existing processes in factories, for example, in production logistics, also with deploying cyber-physical systems. An initiative that explores challenges and opportunities for robots in such a setting is the RoboCup Logistics League. Since its foundation in 2012 it is an international effort for research and education in an intra-warehouse logistics scenario. During seven years of competition a lot of knowledge and experience regarding autonomous robots was gained. This knowledge and experience shall provide the basis for further research in challenges of future production. The focus of our SMART-FACTORY is to create a stimulating environment for research on logistics robotics, for teaching activities in computer science and electrical engineering programmes as well as for industrial users to study and explore the feasibility of future technologies. Building on a very successful history in the RoboCup Logistics League we aim to provide stakeholders with a dedicated facility oriented at their individual needs.}, language = {en} } @inproceedings{LorenzAltherrPelz2020, author = {Lorenz, Imke-Sophie and Altherr, Lena and Pelz, Peter F.}, title = {Assessing and Optimizing the Resilience of Water Distribution Systems Using Graph-Theoretical Metrics}, series = {Operations Research Proceedings 2019}, booktitle = {Operations Research Proceedings 2019}, editor = {Neufeld, Janis S. and Buscher, Udo and Lasch, Rainer and M{\"o}st, Dominik and Sch{\"o}nberger, J{\"o}rn}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-48439-2}, doi = {10.1007/978-3-030-48439-2_63}, pages = {521 -- 527}, year = {2020}, abstract = {Water distribution systems are an essential supply infrastructure for cities. Given that climatic and demographic influences will pose further challenges for these infrastructures in the future, the resilience of water supply systems, i.e. their ability to withstand and recover from disruptions, has recently become a subject of research. To assess the resilience of a WDS, different graph-theoretical approaches exist. Next to general metrics characterizing the network topology, also hydraulic and technical restrictions have to be taken into account. In this work, the resilience of an exemplary water distribution network of a major German city is assessed, and a Mixed-Integer Program is presented which allows to assess the impact of capacity adaptations on its resilience.}, language = {en} } @inproceedings{LeiseSimonAltherr2020, author = {Leise, Philipp and Simon, Nicolai and Altherr, Lena}, title = {Comparison of Piecewise Linearization Techniques to Model Electric Motor Efficiency Maps: A Computational Study}, series = {Operations Research Proceedings 2019}, booktitle = {Operations Research Proceedings 2019}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-48439-2}, doi = {10.1007/978-3-030-48439-2_55}, pages = {457 -- 463}, year = {2020}, abstract = {To maximize the travel distances of battery electric vehicles such as cars or buses for a given amount of stored energy, their powertrains are optimized energetically. One key part within optimization models for electric powertrains is the efficiency map of the electric motor. The underlying function is usually highly nonlinear and nonconvex and leads to major challenges within a global optimization process. To enable faster solution times, one possibility is the usage of piecewise linearization techniques to approximate the nonlinear efficiency map with linear constraints. Therefore, we evaluate the influence of different piecewise linearization modeling techniques on the overall solution process and compare the solution time and accuracy for methods with and without explicitly used binary variables.}, language = {en} } @inproceedings{LeiseBreuerAltherretal.2020, author = {Leise, Philipp and Breuer, Tim and Altherr, Lena and Pelz, Peter F.}, title = {Development, validation and assessment of a resilient pumping system}, series = {Proceedings of the Joint International Resilience Conference, JIRC2020}, booktitle = {Proceedings of the Joint International Resilience Conference, JIRC2020}, isbn = {978-90-365-5095-6}, pages = {97 -- 100}, year = {2020}, abstract = {The development of resilient technical systems is a challenging task, as the system should adapt automatically to unknown disturbances and component failures. To evaluate different approaches for deriving resilient technical system designs, we developed a modular test rig that is based on a pumping system. On the basis of this example system, we present metrics to quantify resilience and an algorithmic approach to improve resilience. This approach enables the pumping system to automatically react on unknown disturbances and to reduce the impact of component failures. In this case, the system is able to automatically adapt its topology by activating additional valves. This enables the system to still reach a minimum performance, even in case of failures. Furthermore, timedependent disturbances are evaluated continuously, deviations from the original state are automatically detected and anticipated in the future. This allows to reduce the impact of future disturbances and leads to a more resilient system behaviour.}, language = {en} }