H. Vietz, A. Löcklin, H. Ben Haj Ammar, und M. Weyrich, „Deep learning-based 5G indoor positioning in a manufacturing environment“, in 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), September 2022, 2022.
ZusammenfassungIndoor positioning systems are an enabling technology for many current developments in the manufacturing field like digital twins and robot fleet management. Utilizing 5G for positioning promises high accuracy, reliability, and costefficiency due to shared hardware usage for communication and positioning. Which positioning technique suits 5G-bases positioning best for manufacturing is still an open research question. This paper presents a deep learning approach for 5Gbased positioning. The first results of our research work in progress obtained at the research factory ARENA 2036 indicate a positioning accuracy in the centimeter range.
B. Maschler, T. Hasan, C. Bitter, H. Vietz, T. Meisen, und M. Weyrich, „Industrielles Transfer-Lernen - Von der Wissenschaft in die Praxis“, atp-Magazin, August 2022, pp.86-93, Juni 2022, 2022.
ZusammenfassungTrotz hoher Lösungspotentiale des maschinellen Lernens für gängige Probleme der Automatisierungstechnik, finden sich in der Praxis wenig Anwendungsbeispiele. Um der Ursache hierfür auf den Grund zu gehen, zeigen die Autoren anhand von vier beispielhaften Anwendungsfällen die Hürden für konventionelles maschinelles Lernen auf und benennen Lösungsansätze mittels industriellem Transfer-Lernen. Zum großflächigen Einsatz derartiger Ansätze, fehlt es an Voraussetzungen, deren Schaffung jedoch anders als beim konventionellen maschinellen Lernen grundsätzlich möglich ist. Der Artikel schließt mit einer Betrachtung dieser Voraussetzungen und macht Vorschläge, wie sie zu erfüllen sind.
B. Maschler, H. Vietz, H. Tercan, C. Bitter, T. Meisen, und M. Weyrich, „Insights and Example Use Cases on Industrial Transfer Learning“, in 55th CIRP Conference on Manufacturing Systems (CIRP CMS), Lugano, Switzerland, 29. Juni 2022, 2022.
ZusammenfassungDespite the high solution potential of machine learning for common problems in automation technology, there are only few examples of its application in real-world manufacturing practice. In order to find the reason for this phenomenon, the authors identify the hurdles for conventional machine learning using four exemplary use cases namely self-learning robots, wear prediction, visual object detection, and predictive quality in manufacturing. While these use-cases differ in principle, the problems engineers face when using conventional machine learning approaches to solve them are related, such as the lack of manifold training data or high dynamics of industrial processes. The authors showcase that utilizing deep transfer learning and continual learning approaches in the industrial context – subsumed under the term industrial transfer learning – can overcome these hurdles. Even for industrial transfer learning, there is a deficiency regarding preconditions for the large-scale deployment of such approaches, but unlike in conventional machine learning, it is principally possible to establish those. The article concludes with a discussion of these prerequisites and makes suggestions as to how they could be fulfilled.
H. Vietz, T. Rauch, und M. Weyrich, „Synthetic Training Data Generation for Convolutional Neural Networks in Vision Applications“, in 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), 2022.
ZusammenfassungVision applications are becoming increasingly important for product quality surveillance in manufacturing. Training consistently well-performing visual detection algorithms based on convolutional neural networks is very challenging. Typically, there is too much training data for engineers to keep track of possible gaps in it. But even small cases of missing training data e.g. certain viewing angles can lead to trained CNNs that are unable to detect objects, that seem obvious to engineers i.e. cognition gaps. This paper presents how synthetic training data can be created in a targeted manner to close cognitive gaps of a CNN for specific use-cases. The proposed methodology uses 3D rendering to create new image data by variating scene parameters. The created data is used to reveal a cognition gap of a CNN. We show that by using this created synthetic data to train the CNN the cognition gap can be successfully closed. This is evaluated with the well-known AlexNet CNN used as a visual bicycle detector. The bicycle example is used as a stand-in for a geometrically interesting, but simple product, that is manufactured in large and growing amounts.
A. Löcklin, M. Artelt, T. Ruppert, H. Vietz, N. Jazdi, und M. Weyrich, „Trajectory Prediction of Moving Workers for Autonomous Mobile Robots on the Shop Floor“, in 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), September 2022, 2022.
ZusammenfassungIn partially automated manufacturing, humans work together with mobile robots. Trajectory prediction, i.e. predicting future positions of human workers, improves collaboration and coexistence between humans and robots on the shop floor. In this paper, we discuss the interrelated research questions of how human motion trajectories can be predicted and how mobile robots such as Autonomous Mobile Robots and Automated Guided Vehicles can take such predictions into account in their pathfinding and navigation. On the robot side, advanced D* pathfinding algorithms allow robots to take dynamic obstacles into account. For trajectory prediction, the position of human workers is determined by an Ultra-Wideband-based Real-Time Locating System. A trajectory prediction framework is introduced to support the implementation and use of pattern- and planning-based trajectory prediction algorithms. The evaluation is based on scenarios from the addressed problem area of manufacturing.