@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},
  address   = {New York, NY},
  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}
}
@article{ChengWollertChenetal.2023,
  author    = {Cheng, Chi-Tsun and Wollert, J{\"o}rg and Chen, Xi and Fapojuwo, Abraham O.},
  title     = {Guest Editorial : Circuits and Systems for Industry X.0 Applications},
  series = {IEEE Journal on Emerging and Selected Topics in Circuits and Systems},
  volume    = {13},
  journal   = {IEEE Journal on Emerging and Selected Topics in Circuits and Systems},
  edition   = {2},
  publisher = {IEEE},
  address   = {New York},
  issn      = {2156-3357 (Print)},
  doi       = {10.1109/JETCAS.2023.3278843},
  pages     = {457 -- 460},
  year      = {2023},
  language  = {en}
}
@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     = {Improvements for a robust production in the RoboCup logistics league 2016},
  series = {RoboCup 2016: Robot World Cup XX. RoboCup 2016.},
  booktitle = {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_49},
  pages     = {589 -- 600},
  year      = {2017},
  language  = {en}
}
@inproceedings{AlhwarinFerreinGebhardtetal.2015,
  author    = {Alhwarin, Faraj and Ferrein, Alexander and Gebhardt, Andreas and Kallweit, Stephan and Scholl, Ingrid and Tedjasukmana, Osmond Sanjaya},
  title     = {Improving additive manufacturing by image processing and robotic milling},
  series = {2015 IEEE International Conference on Automation Science and Engineering (CASE), Aug 24-28, 2015 Gothenburg, Sweden},
  booktitle = {2015 IEEE International Conference on Automation Science and Engineering (CASE), Aug 24-28, 2015 Gothenburg, Sweden},
  doi       = {10.1109/CoASE.2015.7294217},
  pages     = {924 -- 929},
  year      = {2015},
  language  = {en}
}
@inproceedings{KirschMatareFerreinetal.2020,
  author    = {Kirsch, Maximilian and Matar{\´e}, Victor and Ferrein, Alexander and Schiffer, Stefan},
  title     = {Integrating golog++ and ROS for Practical and Portable High-level Control},
  series = {Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2},
  booktitle = {Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2},
  publisher = {SciTePress},
  address   = {Set{\´u}bal, Portugal},
  doi       = {10.5220/0008984406920699},
  pages     = {692 -- 699},
  year      = {2020},
  abstract  = {The field of Cognitive Robotics aims at intelligent decision making of autonomous robots. It has matured over the last 25 or so years quite a bit. That is, a number of high-level control languages and architectures have emerged from the field. One concern in this regard is the action language GOLOG. GOLOG has been used in a rather large number of applications as a high-level control language ranging from intelligent service robots to soccer robots. For the lower level robot software, the Robot Operating System (ROS) has been around for more than a decade now and it has developed into the standard middleware for robot applications. ROS provides a large number of packages for standard tasks in robotics like localisation, navigation, and object recognition. Interestingly enough, only little work within ROS has gone into the high-level control of robots. In this paper, we describe our approach to marry the GOLOG action language with ROS. In particular, we present our architecture on inte grating golog++, which is based on the GOLOG dialect Readylog, with the Robot Operating System. With an example application on the Pepper service robot, we show how primitive actions can be easily mapped to the ROS ActionLib framework and present our control architecture in detail.},
  language  = {en}
}
@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}
}
@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}
}
@inproceedings{WiesenEngemannLimpertetal.2018,
  author    = {Wiesen, Patrick and Engemann, Heiko and Limpert, Nicolas and Kallweit, Stephan},
  title     = {Learning by Doing - Mobile Robotics in the FH Aachen ROS Summer School},
  series = {European Robotics Forum 2018, TRROS18 Workshop},
  booktitle = {European Robotics Forum 2018, TRROS18 Workshop},
  pages     = {47 -- 58},
  year      = {2018},
  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}
}
@article{FerreinSteinbauer2016,
  author    = {Ferrein, Alexander and Steinbauer, Gerald},
  title     = {Looking back on 20 Years of RoboCup},
  series = {KI - K{\"u}nstliche Intelligenz},
  volume    = {30},
  journal   = {KI - K{\"u}nstliche Intelligenz},
  number    = {3-4},
  publisher = {Springer},
  address   = {Berlin},
  issn      = {1610-1987},
  doi       = {10.1007/s13218-016-0443-y},
  pages     = {321 -- 323},
  year      = {2016},
  language  = {en}
}