TY - CHAP A1 - Hofmann, Till A1 - Mataré, Victor A1 - Schiffer, Stefan A1 - Ferrein, Alexander A1 - Lakemeyer, Gerhard T1 - Constraint-based online transformation of abstract plans into executable robot actions T2 - Proceedings of the 2018 AAAI Spring Symposium on Integrating Representation, Reasoning, Learning, and Execution for Goal Directed Autonomy Y1 - 2018 SP - 549 EP - 553 ER - TY - CHAP A1 - Ferrein, Alexander A1 - Schiffer, Stefan A1 - Kallweit, Stephan T1 - The ROSIN Education Concept - Fostering ROS Industrial-Related Robotics Education in Europe T2 - ROBOT 2017: Third Iberian Robotics Conference Y1 - 2018 SN - 978-3-319-70836-2 U6 - http://dx.doi.org/10.1007/978-3-319-70836-2_31 N1 - Advances in Intelligent Systems and Computing, vol 694; (AISC, volume 694) SP - 370 EP - 381 PB - Springer CY - Cham ER - TY - CHAP A1 - Mataré, Victor A1 - Schiffer, Stefan A1 - Ferrein, Alexander ED - Steinbauer, Gerald ED - Ferrein, Alexander T1 - golog++ : An integrative system design T2 - 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 Y1 - 2019 SN - 1613-0073 SP - 29 EP - 35 ER - TY - CHAP A1 - Ferrein, Alexander A1 - Scholl, Ingrid A1 - Neumann, Tobias A1 - Krückel, Kai A1 - Schiffer, Stefan T1 - A system for continuous underground site mapping and exploration Y1 - 2019 U6 - http://dx.doi.org/10.5772/intechopen.85859 ER - TY - CHAP A1 - Ferrein, Alexander A1 - Bharatheesha, Mukunda A1 - Schiffer, Stefan A1 - Corbato, Carlos Hernandez T1 - TRROS 2018 : Teaching Robotics with ROS Workshop at ERF 2018; Proceedings of the Workshop on Teaching Robotics with ROS (held at ERF 2018), co-located with European Robotics Forum 2018 (ERF 2018), Tampere, Finland, March 15th, 2018 T2 - CEUR Workshop Proceedings Y1 - 2019 SN - 1613-0073 IS - Vol-2329 ER - TY - CHAP A1 - Alhwarin, Faraj A1 - Schiffer, Stefan A1 - Ferrein, Alexander A1 - Scholl, Ingrid T1 - An Optimized Method for 3D Body Scanning Applications Based on KinectFusion T2 - Communications in Computer and Information Science Y1 - 2019 U6 - http://dx.doi.org/10.1007/978-3-030-29196-9_6 SN - 1865-0929 N1 - 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018; Funchal; Portugal; 19 January 2018 through 21 January 2018 VL - 1024 SP - 100 EP - 113 PB - Springer ER - TY - CHAP A1 - Eltester, Niklas Sebastian A1 - Ferrein, Alexander A1 - Schiffer, Stefan T1 - A smart factory setup based on the RoboCup logistics league T2 - 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS) N2 - 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. Y1 - 2020 U6 - http://dx.doi.org/10.1109/ICPS48405.2020.9274766 N1 - 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS), 10-12 June 2020, Tampere, Finland. SP - 297 EP - 302 PB - IEEE ER - TY - CHAP A1 - Kirsch, Maximilian A1 - Mataré, Victor A1 - Ferrein, Alexander A1 - Schiffer, Stefan T1 - Integrating golog++ and ROS for Practical and Portable High-level Control T2 - 12th International Conference on Agents and Artificial Intelligence Y1 - 2020 U6 - http://dx.doi.org/10.5220/0008984406920699 ER - TY - CHAP A1 - Reke, Michael A1 - Peter, Daniel A1 - Schulte-Tigges, Joschua A1 - Schiffer, Stefan A1 - Ferrein, Alexander A1 - Walter, Thomas A1 - Matheis, Dominik T1 - A Self-Driving Car Architecture in ROS2 T2 - 2020 International SAUPEC/RobMech/PRASA Conference, Cape Town, South Africa Y1 - 2020 SN - 978-1-7281-4162-6 U6 - http://dx.doi.org/10.1109/SAUPEC/RobMech/PRASA48453.2020.9041020 SP - 1 EP - 6 ER - TY - CHAP A1 - Nikolovski, Gjorgji A1 - Reke, Michael A1 - Elsen, Ingo A1 - Schiffer, Stefan T1 - Machine learning based 3D object detection for navigation in unstructured environments T2 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) N2 - 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. KW - 3D object detection KW - LiDAR KW - autonomous driving KW - Deep learning KW - Three-dimensional displays Y1 - 2021 SN - 978-1-6654-7921-9 U6 - http://dx.doi.org/10.1109/IVWorkshops54471.2021.9669218 N1 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops), 11-17 July 2021, Nagoya, Japan. SP - 236 EP - 242 PB - IEEE ER -