@inproceedings{NiemuellerReuterEwertetal.2016, author = {Niemueller, Tim and Reuter, Sebastian and Ewert, Daniel and Ferrein, Alexander and Jeschke, Sabina and Lakemeyer, Gerhard}, title = {The Carologistics Approach to Cope with the Increased Complexity and New Challenges of the RoboCup Logistics League 2015}, 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_4}, pages = {47 -- 59}, 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} } @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{NiemuellerEwertReuteretal.2013, author = {Niem{\"u}ller, Tim and Ewert, Daniel and Reuter, Sebastian and Ferrein, Alexander and Jeschke, Sabina and Lakemeyer, Gerhard}, title = {The Carologistics RoboCup Logistics Team 2013}, series = {RoboCup 2013 : Eindhoven}, booktitle = {RoboCup 2013 : Eindhoven}, organization = {Robocup <2013, Eindhoven>}, pages = {1 -- 8}, year = {2013}, language = {en} } @inproceedings{NiemuellerEwertReuteretal.2013, author = {Niem{\"u}ller, Tim and Ewert, Daniel and Reuter, Sebastian and Karras, Ulrich and Ferrein, Alexander}, title = {Towards benchmarking cyber-physical systems in factory automation scenarios}, series = {KI 2013: advances in artificial intelligence : 36th Annual German Conference on AI, Koblenz, Germany, September 16-20, 2013 ; proceedings / Ingo J. Timm ... (ed.). (Lecture notes in computer science ; 8077)}, booktitle = {KI 2013: advances in artificial intelligence : 36th Annual German Conference on AI, Koblenz, Germany, September 16-20, 2013 ; proceedings / Ingo J. Timm ... (ed.). (Lecture notes in computer science ; 8077)}, publisher = {Springer}, address = {Berlin [u.a.]}, isbn = {978-3-642-40941-7}, pages = {296 -- 299}, year = {2013}, language = {en} } @inproceedings{NiemuellerLakemeyerFerrein2013, author = {Niem{\"u}ller, Tim and Lakemeyer, Gerhard and Ferrein, Alexander}, title = {Aspects of integrating diverse software into robotic systems extended abstract}, series = {ICRA 2013 - 8th Workshop on Software Development and Integration in Robotics (SDIR), Karlsruhe, Germany}, booktitle = {ICRA 2013 - 8th Workshop on Software Development and Integration in Robotics (SDIR), Karlsruhe, Germany}, pages = {1 -- 2}, year = {2013}, language = {en} } @inproceedings{NiemuellerLakemeyerFerrein2013, author = {Niem{\"u}ller, Tim and Lakemeyer, Gerhard and Ferrein, Alexander}, title = {Incremental task-level reasoning in a competitive factory automation scenario}, series = {Designing intelligent robots : reintegrating AI II ; papers from the AAAI spring symposium ; [held March 25 - 27, 2013 in Palo Alto, California, USA, on the campus of Stanford University]. (Technical Report / Association for the Advancement of Artificial Intelligence ; 2013,4)}, booktitle = {Designing intelligent robots : reintegrating AI II ; papers from the AAAI spring symposium ; [held March 25 - 27, 2013 in Palo Alto, California, USA, on the campus of Stanford University]. (Technical Report / Association for the Advancement of Artificial Intelligence ; 2013,4)}, editor = {Boots, Byron}, organization = {American Association for Artificial Intelligence}, isbn = {9781577356011}, pages = {43 -- 48}, year = {2013}, language = {en} } @inproceedings{NierlePieper2023, author = {Nierle, Elisabeth and Pieper, Martin}, title = {Measuring social impacts in engineering education to improve sustainability skills}, series = {European Society for Engineering Education (SEFI)}, booktitle = {European Society for Engineering Education (SEFI)}, doi = {10.21427/QPR4-0T22}, pages = {9 Seiten}, year = {2023}, abstract = {In times of social climate protection movements, such as Fridays for Future, the priorities of society, industry and higher education are currently changing. The consideration of sustainability challenges is increasing. In the context of sustainable development, social skills are crucial to achieving the United Nations Sustainable Development Goals (SDGs). In particular, the impact that educational activities have on people, communities and society is therefore coming to the fore. Research has shown that people with high levels of social competence are better able to manage stressful situations, maintain positive relationships and communicate effectively. They are also associated with better academic performance and career success. However, especially in engineering programs, the social pillar is underrepresented compared to the environmental and economic pillars. In response to these changes, higher education institutions should be more aware of their social impact - from individual forms of teaching to entire modules and degree programs. To specifically determine the potential for improvement and derive resulting change for further development, we present an initial framework for social impact measurement by transferring already established approaches from the business sector to the education sector. To demonstrate the applicability, we measure the key competencies taught in undergraduate engineering programs in Germany. The aim is to prepare the students for success in the modern world of work and their future contribution to sustainable development. Additionally, the university can include the results in its sustainability report. Our method can be applied to different teaching methods and enables their comparison.}, language = {en} } @inproceedings{NikolovskiLimpertNessauetal.2023, author = {Nikolovski, Gjorgji and Limpert, Nicolas and Nessau, Hendrik and Reke, Michael and Ferrein, Alexander}, title = {Model-predictive control with parallelised optimisation for the navigation of autonomous mining vehicles}, series = {2023 IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {2023 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, isbn = {979-8-3503-4691-6 (Online)}, doi = {10.1109/IV55152.2023.10186806}, pages = {6 Seiten}, year = {2023}, abstract = {The work in modern open-pit and underground mines requires the transportation of large amounts of resources between fixed points. The navigation to these fixed points is a repetitive task that can be automated. The challenge in automating the navigation of vehicles commonly used in mines is the systemic properties of such vehicles. Many mining vehicles, such as the one we have used in the research for this paper, use steering systems with an articulated joint bending the vehicle's drive axis to change its course and a hydraulic drive system to actuate axial drive components or the movements of tippers if available. To address the difficulties of controlling such a vehicle, we present a model-predictive approach for controlling the vehicle. While the control optimisation based on a parallel error minimisation of the predicted state has already been established in the past, we provide insight into the design and implementation of an MPC for an articulated mining vehicle and show the results of real-world experiments in an open-pit mine environment.}, 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} }