@inproceedings{RekePeterSchulteTiggesetal.2020, author = {Reke, Michael and Peter, Daniel and Schulte-Tigges, Joschua and Schiffer, Stefan and Ferrein, Alexander and Walter, Thomas and Matheis, Dominik}, title = {A Self-Driving Car Architecture in ROS2}, series = {2020 International SAUPEC/RobMech/PRASA Conference, Cape Town, South Africa}, booktitle = {2020 International SAUPEC/RobMech/PRASA Conference, Cape Town, South Africa}, publisher = {IEEE}, address = {New York, NY}, isbn = {978-1-7281-4162-6}, doi = {10.1109/SAUPEC/RobMech/PRASA48453.2020.9041020}, pages = {1 -- 6}, year = {2020}, abstract = {In this paper we report on an architecture for a self-driving car that is based on ROS2. Self-driving cars have to take decisions based on their sensory input in real-time, providing high reliability with a strong demand in functional safety. In principle, self-driving cars are robots. However, typical robot software, in general, and the previous version of the Robot Operating System (ROS), in particular, does not always meet these requirements. With the successor ROS2 the situation has changed and it might be considered as a solution for automated and autonomous driving. Existing robotic software based on ROS was not ready for safety critical applications like self-driving cars. We propose an architecture for using ROS2 for a self-driving car that enables safe and reliable real-time behaviour, but keeping the advantages of ROS such as a distributed architecture and standardised message types. First experiments with an automated real passenger car at lower and higher speed-levels show that our approach seems feasible for autonomous driving under the necessary real-time conditions.}, language = {en} } @article{NiemuellerKarrasFerrein2017, author = {Niemueller, Tim and Karras, Ulrich and Ferrein, Alexander}, title = {Meisterschaft der Maschinen: Die Industrial Logistic Liga}, series = {C´t Magazin f{\"u}r Computertechnik}, journal = {C´t Magazin f{\"u}r Computertechnik}, number = {26}, year = {2017}, language = {de} } @inproceedings{NiemuellerNeumannHenkeetal.2017, author = {Niemueller, Tim and Neumann, Tobias and Henke, Christoph and Sch{\"o}nitz, Sebastian and Reuter, Sebastian and Ferrein, Alexander and Jeschke, Sabina and Lakemeyer, Gerhard}, title = {International Harting Open Source Award 2016: Fawkes for the RoboCup Logistics League}, series = {RoboCup 2016: RoboCup 2016: Robot World Cup XX. RoboCup 2016}, booktitle = {RoboCup 2016: RoboCup 2016: Robot World Cup XX. RoboCup 2016}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-68792-6}, doi = {10.1007/978-3-319-68792-6_53}, pages = {634 -- 642}, year = {2017}, language = {en} } @incollection{NiemuellerZwillingLakemeyeretal.2017, author = {Niemueller, Tim and Zwilling, Frederik and Lakemeyer, Gerhard and L{\"o}bach, Matthias and Reuter, Sebastian and Jeschke, Sabina and Ferrein, Alexander}, title = {Cyber-Physical System Intelligence}, series = {Industrial Internet of Things}, booktitle = {Industrial Internet of Things}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-42559-7}, doi = {10.1007/978-3-319-42559-7_17}, pages = {447 -- 472}, year = {2017}, abstract = {Cyber-physical systems are ever more common in manufacturing industries. Increasing their autonomy has been declared an explicit goal, for example, as part of the Industry 4.0 vision. To achieve this system intelligence, principled and software-driven methods are required to analyze sensing data, make goal-directed decisions, and eventually execute and monitor chosen tasks. In this chapter, we present a number of knowledge-based approaches to these problems and case studies with in-depth evaluation results of several different implementations for groups of autonomous mobile robots performing in-house logistics in a smart factory. We focus on knowledge-based systems because besides providing expressive languages and capable reasoning techniques, they also allow for explaining how a particular sequence of actions came about, for example, in the case of a failure.}, language = {en} } @inproceedings{KrueckelNoldenFerreinetal.2015, author = {Kr{\"u}ckel, Kai and Nolden, Florian and Ferrein, Alexander and Scholl, Ingrid}, title = {Intuitive visual teleoperation for UGVs using free-look augmented reality displays}, series = {2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA}, booktitle = {2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA}, doi = {10.1109/ICRA.2015.7139809}, pages = {4412 -- 4417}, year = {2015}, language = {en} } @article{SchulteTiggesFoersterNikolovskietal.2022, author = {Schulte-Tigges, Joschua and F{\"o}rster, Marco and Nikolovski, Gjorgji and Reke, Michael and Ferrein, Alexander and Kaszner, Daniel and Matheis, Dominik and Walter, Thomas}, title = {Benchmarking of various LiDAR sensors for use in self-driving vehicles in real-world environments}, series = {Sensors}, volume = {22}, journal = {Sensors}, number = {19}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {10.3390/s22197146}, pages = {20 Seiten}, year = {2022}, abstract = {Abstract In this paper, we report on our benchmark results of the LiDAR sensors Livox Horizon, Robosense M1, Blickfeld Cube, Blickfeld Cube Range, Velodyne Velarray H800, and Innoviz Pro. The idea was to test the sensors in different typical scenarios that were defined with real-world use cases in mind, in order to find a sensor that meet the requirements of self-driving vehicles. For this, we defined static and dynamic benchmark scenarios. In the static scenarios, both LiDAR and the detection target do not move during the measurement. In dynamic scenarios, the LiDAR sensor was mounted on the vehicle which was driving toward the detection target. We tested all mentioned LiDAR sensors in both scenarios, show the results regarding the detection accuracy of the targets, and discuss their usefulness for deployment in self-driving cars.}, language = {en} } @inproceedings{Ferrein2015, author = {Ferrein, Alexander}, title = {3D-Mapping von Straßentunneln}, series = {16. Geokinematischer Tag, 07. und 08. Mai 2015 : Tagungsband / Technische Universit{\"a}t Bergakademie Freiberg}, booktitle = {16. Geokinematischer Tag, 07. und 08. Mai 2015 : Tagungsband / Technische Universit{\"a}t Bergakademie Freiberg}, pages = {31 -- 40}, year = {2015}, language = {de} } @inproceedings{AlhwarinFerreinScholl2019, author = {Alhwarin, Faraj and Ferrein, Alexander and Scholl, Ingrid}, title = {An Efficient Hashing Algorithm for NN Problem in HD Spaces}, series = {Lecture Notes in Computer Science}, booktitle = {Lecture Notes in Computer Science}, isbn = {978-303005498-4}, doi = {10.1007/978-3-030-05499-1_6}, pages = {101 -- 115}, year = {2019}, language = {en} } @inproceedings{SchleupenEngemannBagherietal.2017, author = {Schleupen, Josef and Engemann, Heiko and Bagheri, Mohsen and Kallweit, Stephan and Dahmann, Peter}, title = {Developing a climbing maintenance robot for tower and rotor blade service of wind turbines}, series = {Advances in Robot Design and Intelligent Control : Proceedings of the 25th Conference on Robotics in Alpe-Adria-Danube Region (RAAD16)}, booktitle = {Advances in Robot Design and Intelligent Control : Proceedings of the 25th Conference on Robotics in Alpe-Adria-Danube Region (RAAD16)}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-49058-8}, doi = {10.1007/978-3-319-49058-8_34}, pages = {310 -- 319}, year = {2017}, language = {en} } @inproceedings{MarcoFerrein2017, author = {Marco, Heather G. and Ferrein, Alexander}, title = {AGNES: The African-German Network of Excellence in Science}, series = {Proceedings of the 2nd Developing World Robotics Forum, Workshop at IEEE AFRICON 2017}, booktitle = {Proceedings of the 2nd Developing World Robotics Forum, Workshop at IEEE AFRICON 2017}, pages = {1 -- 2}, year = {2017}, language = {en} }