@inproceedings{DinghoferHartung2020,
  author    = {Dinghofer, Kai and Hartung, Frank},
  title     = {Analysis of Criteria for the Selection of Machine Learning Frameworks},
  series = {2020 International Conference on Computing, Networking and Communications (ICNC)},
  booktitle = {2020 International Conference on Computing, Networking and Communications (ICNC)},
  publisher = {IEEE},
  address   = {New York, NY},
  doi       = {10.1109/ICNC47757.2020.9049650},
  pages     = {373 -- 377},
  year      = {2020},
  abstract  = {With the many achievements of Machine Learning in the past years, it is likely that the sub-area of Deep Learning will continue to deliver major technological breakthroughs [1]. In order to achieve best results, it is important to know the various different Deep Learning frameworks and their respective properties. This paper provides a comparative overview of some of the most popular frameworks. First, the comparison methods and criteria are introduced and described with a focus on computer vision applications: Features and Uses are examined by evaluating papers and articles, Adoption and Popularity is determined by analyzing a data science study. Then, the frameworks TensorFlow, Keras, PyTorch and Caffe are compared based on the previously described criteria to highlight properties and differences. Advantages and disadvantages are compared, enabling researchers and developers to choose a framework according to their specific needs.},
  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},
  address   = {New York, NY},
  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{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}
}