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Institut für Automatisierungstechnik und Softwaresysteme
Institut
Team
Artelt, Maurice

Maurice Artelt

M.Sc.

Akademischer Mitarbeiter
Institut für Automatisierungstechnik und Softwaresysteme

Kontakt

+49 711 685 67304
+49 711 685 67302
E-Mail
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Pfaffenwaldring 47
70550 Stuttgart
Raum: 1.135

Publikationen

Zeitschriften und Konferenzen:

2023
1. M. Artelt, D. Dittler, G. Hildebrandt, D. Braun, N. Jazdi, and M. Weyrich, “Dynamic Production Scheduling with Intelligent Products in a Modular Production System,” 2023, pp. 1–4.
  - BibTeX
  BibTeX
  @inproceedings{inproceedings, author = {Artelt, Maurice and Dittler, Daniel and Hildebrandt, Gary and Braun, Dominik and Jazdi, Nasser and Weyrich, Michael}, doi = {10.1109/ETFA54631.2023.10275613}, month = {09}, pages = {1-4}, title = {Dynamic Production Scheduling with Intelligent Products in a Modular Production System}, year = 2023 }
2. A. Aghaei Attar, T. Fabarisov, A. Morozov, M. Artelt, and I. Mamaev, “Hybrid Lightweight Deep Learning-Based Error Detection Model on Edge Computing Devices,” 2023, pp. 1–4.
  - BibTeX
  BibTeX
  @inproceedings{inproceedings, author = {Aghaei Attar, Arman and Fabarisov, Tagir and Morozov, Andrey and Artelt, Maurice and Mamaev, Ilshat}, doi = {10.1109/ETFA54631.2023.10275725}, month = {09}, pages = {1-4}, title = {Hybrid Lightweight Deep Learning-Based Error Detection Model on Edge Computing Devices}, year = 2023 }
2022
1. A. Löcklin, M. Artelt, T. Ruppert, H. Vietz, N. Jazdi, and 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.
  - Zusammenfassung
  - BibTeX
  Zusammenfassung
  In 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.
  BibTeX
  @conference{locklin2022trajectory, abstract = {In 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.}, author = {Löcklin, Andreas and Artelt, Maurice and Ruppert, Tamás and Vietz, Hannes and Jazdi, Nasser and Weyrich, Michael}, booktitle = {2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), September 2022}, doi = {10.1109/ETFA52439.2022.9921493}, title = {Trajectory Prediction of Moving Workers for Autonomous Mobile Robots on the Shop Floor}, year = 2022 }
2. A. Löcklin, F. Dettinger, M. Artelt, N. Jazdi, and M. Weyrich, “Trajectory Prediction of Workers to Improve AGV and AMR Operation based on the Manufacturing Schedule,” Procedia CIRP, Vol. 107, pp. 283-288, Mai, 2022, 2022.
  Zusammenfassung
  In semi-automated manufacturing, an increasing amount of intelligent mobile robots operate in close proximity to human workers. Considering future positions of humans allows to further improve the efficiency in terms of throughput of Autonomous Mobile Robots (AMR) and Automated Guided Vehicles (AGV). The longer the prediction horizon, i.e. the more position values of humans can be predicted in the near and distant future, the more a robot can adjust its route accordingly and optimize the process. This paper discusses the challenges of human motion trajectory prediction in manufacturing and presents a schedule-based approach that uses real-time schedule data obtained from Manufacturing Execution Systems (MES). Schedule-awareness in human motion trajectory prediction extends semantic mapping approaches and effectively reduces the number of probable destinations by considering which process steps are next for the currently produced goods. With a reduced set of destinations, the performance of forward-planning trajectory prediction can be improved. For evaluation, a commercial MES is used together with an Ultra-wideband-based Real-Time Locating System (RTLS) for obtaining position data of humans. On this basis, a naive Bayes classifier utilizes MES-schedule and real-time position data to predict human motion intentions. Abstract activity modeling ensures that only a few training data sets are required for deployment, thus making this approach suitable for rapidly changing manufacturing environments such as in flexible manufacturing.
  BibTeX
  @article{locklin2022trajectory, abstract = {In semi-automated manufacturing, an increasing amount of intelligent mobile robots operate in close proximity to human workers. Considering future positions of humans allows to further improve the efficiency in terms of throughput of Autonomous Mobile Robots (AMR) and Automated Guided Vehicles (AGV). The longer the prediction horizon, i.e. the more position values of humans can be predicted in the near and distant future, the more a robot can adjust its route accordingly and optimize the process. This paper discusses the challenges of human motion trajectory prediction in manufacturing and presents a schedule-based approach that uses real-time schedule data obtained from Manufacturing Execution Systems (MES). Schedule-awareness in human motion trajectory prediction extends semantic mapping approaches and effectively reduces the number of probable destinations by considering which process steps are next for the currently produced goods. With a reduced set of destinations, the performance of forward-planning trajectory prediction can be improved. For evaluation, a commercial MES is used together with an Ultra-wideband-based Real-Time Locating System (RTLS) for obtaining position data of humans. On this basis, a naive Bayes classifier utilizes MES-schedule and real-time position data to predict human motion intentions. Abstract activity modeling ensures that only a few training data sets are required for deployment, thus making this approach suitable for rapidly changing manufacturing environments such as in flexible manufacturing.}, author = {Löcklin, Andreas and Dettinger, Falk and Artelt, Maurice and Jazdi, Nasser and Weyrich, Michael}, booktitle = {Procedia CIRP, Vol. 107, pp. 283-288, Mai, 2022}, doi = {10.1016/j.procir.2022.04.046}, issn = {2212-8271}, title = {Trajectory Prediction of Workers to Improve AGV and AMR Operation based on the Manufacturing Schedule}, url = {https://www.ias.uni-stuttgart.de/dokumente/publikationen/2022_Trajectory_Prediction_of_Workers_to_Improve_AGV_and_AMR_Operation_based_on_the_Manufacturing_Schedule.pdf}, year = 2022 }
  Link
  https://www.ias.uni-stuttgart.de/dokumente/publikationen/2022_Trajectory_Prediction_of_Workers_to_Improve_AGV_and_AMR_Operation_based_on_the_Manufacturing_Schedule.pdf

Forschung

Forschungsschwerpunkt: - KI-basierten hybriden Modellen zur Lebensdauerabschätzung von komplexen elektronischen Systemen

Beschreibung: - Elektronische Systeme ermöglichen den Komfort unseres modernen Lebens. Dementsprechend stellt sich bei jeder Investition, in der elektronische Systeme enthalten sind, die Frage, ob das Gerät die anvisierte Lebensdauer erreichen wird. Beim Einsatz von Automatisierungstechnik ist dies auch immer mit dem Gedanken der Ausfallzeitminimierung verknüpft. Eine Lebensdauerabschätzung ermöglicht es die Planbarkeit zu erhöhen. Für die Lebensdauer des Systems spielen dabei mannigfaltige Faktoren eine Rolle. Die Zusammenhänge dieser Faktoren sind für den Menschen nicht analytisch zu kombinieren. Dank des industriellen Internet of Things stehen umfassende Daten über die Betriebs- und Umgebungsbedingungen bereit, um Machine-Learning-Algorithmen trainieren zu können. Klassische auf Stochastik beruhende Lebensdauermodelle erlauben eine erste Einschätzung, sie lassen jedoch noch viel Spielraum, ob die Elektronik eher zu den Frühausfällen oder den Spätausfällen gehört. Diese Abweichungen können für den Betreiber zu kostspieligen Stillständen führen. Datengetriebene Modelle allein verkennen schnell die physikalischen Begebenheiten beispielsweise, wenn eine Elektronik in anderen Temperaturbereichen betrieben wird als für das Training des Modelles verwendet wurden. Durch künstliche Intelligenz basierte hybride Modelle soll der eingesetzte Algorithmus deutlich zutreffender sein und auch bei multiplen Eingangsparametern die gewünschte Performance erzielen. Dieses Forschungsvorhaben beschäftigt sich mit dem Einsatz von Machine Learning Algorithmen wie beispielsweise Long Short-Term Memory Netzen in Kombination mit physikalischen Modellen zur Lebensdauerabschätzung.

Forschungsportal:
https://www.researchgate.net/profile/Maurice-Artelt
https://scholar.google.com/citations?user=meFa3FIAAAAJ&hl=de&oi=ao