@inproceedings{FerreinMaierMuehlbacheretal.2015, author = {Ferrein, Alexander and Maier, Christopher and M{\"u}hlbacher, Clemens and Niemueller, Tim and Steinbauer, Gerald and Vassos, Stravros}, title = {Controlling Logistics Robots with the Action-based Language YAGI}, series = {Proceedings of the 2015 IROS Workshop on Workshop on Task Planning for Intelligent Robots in Service and Manufacturing}, booktitle = {Proceedings of the 2015 IROS Workshop on Workshop on Task Planning for Intelligent Robots in Service and Manufacturing}, year = {2015}, language = {en} } @inproceedings{FerreinLakemeyerSchiffer2006, author = {Ferrein, Alexander and Lakemeyer, Gerhard and Schiffer, Stefan}, title = {AllemaniACs@ home 2006 team description}, pages = {1 -- 6}, year = {2006}, language = {en} } @inproceedings{FerreinKallweitScholletal.2015, author = {Ferrein, Alexander and Kallweit, Stephan and Scholl, Ingrid and Reichert, Walter}, title = {Learning to Program Mobile Robots in the ROS Summer School Series}, series = {Proceedings 6th International Conference on Robotics in Education (RiE 15)}, booktitle = {Proceedings 6th International Conference on Robotics in Education (RiE 15)}, pages = {6 S.}, year = {2015}, abstract = {The main objective of our ROS Summer School series is to introduce MA level students to program mobile robots with the Robot Operating System (ROS). ROS is a robot middleware that is used my many research institutions world-wide. Therefore, many state-of-the-art algorithms of mobile robotics are available in ROS and can be deployed very easily. As a basic robot platform we deploy a 1/10 RC cart that is wquipped with an Arduino micro-controller to control the servo motors, and an embedded PC that runs ROS. In two weeks, participants get to learn the basics of mobile robotics hands-on. We describe our teaching concepts and our curriculum and report on the learning success of our students.}, language = {en} } @inproceedings{FerreinBharatheeshaSchifferetal.2019, author = {Ferrein, Alexander and Bharatheesha, Mukunda and Schiffer, Stefan and Corbato, Carlos Hernandez}, title = {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}, series = {CEUR Workshop Proceedings}, booktitle = {CEUR Workshop Proceedings}, number = {Vol-2329}, issn = {1613-0073}, pages = {68 Seiten}, year = {2019}, language = {en} } @inproceedings{Ferrein2015, author = {Ferrein, Alexander}, title = {Robots: challenges, chances and risks for solving 21st century problems}, series = {President's Invitation Lecture / South African Institute of Electrical Engineers : May 21 \& 22, 2015, University of Johannesburg}, booktitle = {President's Invitation Lecture / South African Institute of Electrical Engineers : May 21 \& 22, 2015, University of Johannesburg}, organization = {South African Institute of Electrical Engineers}, pages = {1 -- 45}, year = {2015}, 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{EichenbaumNikolovskiMuelhensetal.2023, author = {Eichenbaum, Julian and Nikolovski, Gjorgji and M{\"u}lhens, Leon and Reke, Michael and Ferrein, Alexander and Scholl, Ingrid}, title = {Towards a lifelong mapping approach using Lanelet 2 for autonomous open-pit mine operations}, series = {2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)}, booktitle = {2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)}, publisher = {IEEE}, isbn = {979-8-3503-2069-5 (Online)}, doi = {10.1109/CASE56687.2023.10260526}, pages = {8 Seiten}, year = {2023}, abstract = {Autonomous agents require rich environment models for fulfilling their missions. High-definition maps are a well-established map format which allows for representing semantic information besides the usual geometric information of the environment. These are, for instance, road shapes, road markings, traffic signs or barriers. The geometric resolution of HD maps can be as precise as of centimetre level. In this paper, we report on our approach of using HD maps as a map representation for autonomous load-haul-dump vehicles in open-pit mining operations. As the mine undergoes constant change, we also need to constantly update the map. Therefore, we follow a lifelong mapping approach for updating the HD maps based on camera-based object detection and GPS data. We show our mapping algorithm based on the Lanelet 2 map format and show our integration with the navigation stack of the Robot Operating System. We present experimental results on our lifelong mapping approach from a real open-pit mine.}, language = {en} } @inproceedings{DyllaFerreinLakemeyer2003, author = {Dylla, Frank and Ferrein, Alexander and Lakemeyer, Gerhard}, title = {AllemaniACs 2003 team description}, series = {RoboCup 2003 : Robot Soccer World Cup VII}, booktitle = {RoboCup 2003 : Robot Soccer World Cup VII}, pages = {1 -- 3}, year = {2003}, 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{ChajanSchulteTiggesRekeetal.2021, author = {Chajan, Eduard and Schulte-Tigges, Joschua and Reke, Michael and Ferrein, Alexander and Matheis, Dominik and Walter, Thomas}, title = {GPU based model-predictive path control for self-driving vehicles}, series = {IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, isbn = {978-1-7281-5394-0}, doi = {10.1109/IV48863.2021.9575619}, pages = {1243 -- 1248}, year = {2021}, abstract = {One central challenge for self-driving cars is a proper path-planning. Once a trajectory has been found, the next challenge is to accurately and safely follow the precalculated path. The model-predictive controller (MPC) is a common approach for the lateral control of autonomous vehicles. The MPC uses a vehicle dynamics model to predict the future states of the vehicle for a given prediction horizon. However, in order to achieve real-time path control, the computational load is usually large, which leads to short prediction horizons. To deal with the computational load, the control algorithm can be parallelized on the graphics processing unit (GPU). In contrast to the widely used stochastic methods, in this paper we propose a deterministic approach based on grid search. Our approach focuses on systematically discovering the search area with different levels of granularity. To achieve this, we split the optimization algorithm into multiple iterations. The best sequence of each iteration is then used as an initial solution to the next iteration. The granularity increases, resulting in smooth and predictable steering angle sequences. We present a novel GPU-based algorithm and show its accuracy and realtime abilities with a number of real-world experiments.}, language = {en} }