Marcel Dzubba

Herr M.Sc.

Wissenschaftlicher Mitarbeiter "Antriebssysteme und -regelung"
Institut für Steuerungstechnik der Werkzeugmaschinen und Fertigungseinrichtungen

Kontakt

+49 711 685 84504
E-Mail
Visitenkarte (VCF)

Seidenstr. 36
70174 Stuttgart
Deutschland
Raum: 5.091

Sprechstunde

nach Vereinbarung

ARP Sprechstunde:

Fr. 19.04.24, 15-16 Uhr
Fr. 26.04.24, 14:30-15:30 Uhr
Fr. 03.05.24, 15-16 Uhr

Fachgebiet

Robotermodellierung und -kalibrierung
MEMS-Beschleunigungssensorik

Publikationen

Veröffentlichungen:

2025
1. Dzubba, Marcel ; Neubauer, Michael ; Hinze, Christoph ; Lechler, Armin ; Verl, Alexander: A Nonlinear Elasticity Model and Feedforward Compensation Method to Increase Positioning Accuracy of Industrial Robots. In: IEEE Transactions on Automation Science and Engineering (2025), S. 1
  - BibTeX
  - DOI
  BibTeX
  @article{10962177, abstract = {Large industrial robots are inexpensive compared to their workspace. Due to flexibility, the positioning accuracy is compromised. This paper presents a simple yet effective static calibration method for robots using a laser tracker that can be applied to processes with no change in external load and without high processing forces. The calibration method is based on a novel elastokinematic model that captures the nonlinear gearbox and link compliance and also accounts for the effects of pose dependent joint friction and nonlinearities due to the gravity compensation mechanism. Furthermore, a feedforward concept is presented that separates kinematics from compliance to reduce computation time. The calibration method was implemented on industrial hardware and experimentally validated on a KUKA KR210–2. In comparison to state of the art compliance modeling, accuracy is improved by 23.1 %. Finally, the feedforward model reduces the mean positioning error in the entire workspace by 54.9% compared to the purely kinematically calibrated model, emphasizing the need for compliance compensation.}, author = {Dzubba, Marcel and Neubauer, Michael and Hinze, Christoph and Lechler, Armin and Verl, Alexander}, doi = {10.1109/TASE.2025.3559673}, issn = {1558-3783}, journal = {IEEE Transactions on Automation Science and Engineering}, pages = {1-1}, title = {A Nonlinear Elasticity Model and Feedforward Compensation Method to Increase Positioning Accuracy of Industrial Robots}, year = 2025 }
  DOI
  10.1109/TASE.2025.3559673
2024
1. Zürn, Manuel ; Dzubba, Marcel ; Reiff, Colin ; Ajdinovic, Samed ; Lechler, Armin ; Verl, Alexander: Cobot for Automated Vision Data: Streamlining Production with Automated Annotations for Machine Learning. In: 2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA), 2024, S. 1–6
  - BibTeX
  - DOI
  BibTeX
  @inproceedings{10468034, abstract = {Robotic vision tasks require a large amount of annotated data for effective training and performance. However, the manual creation of such datasets is a complex and time-consuming process. Particularly for smaller companies or those with high product variability, there is a need to optimise the resources dedicated to creating datasets for machine vision applications. This paper investigates novel ways to acquire annotated vision data through the integration of collaborative robots. Two methods are outlined to streamline the generation of annotated datasets. One of these methods is then used to construct a dataset tailored for 6D robotic gripping of an automotive connector. The effectiveness of automated annotated dataset generation is demonstrated by training a deep learning model to accurately estimate the orientation of the automotive connector.}, author = {Zürn, Manuel and Dzubba, Marcel and Reiff, Colin and Ajdinovic, Samed and Lechler, Armin and Verl, Alexander}, booktitle = {2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)}, doi = {10.1109/ACDSA59508.2024.10468034}, month = {02}, pages = {1-6}, title = {Cobot for Automated Vision Data: Streamlining Production with Automated Annotations for Machine Learning}, year = 2024 }
  DOI
  10.1109/ACDSA59508.2024.10468034
2. Ajdinovic, Samed ; Maisch, Nicolai ; Dzubba, Marcel ; Lechler, Armin ; Riedel, Oliver: Defining Scalable Data Models for Operational Data Integration in Manufacturing Processes Within the Digital Product Passport Framework Through OPC UA and Asset Administration Shell. In: International Conference on Flexible Automation and Intelligent Manufacturing Bd. Lecture Notes in Mechanical Engineering ((LNME)) (2024)
  BibTeX
  @article{ajdinovic2024defining, author = {Ajdinovic, Samed and Maisch, Nicolai and Dzubba, Marcel and Lechler, Armin and Riedel, Oliver}, doi = {10.1007/978-3-031-74482-2_14}, journal = {International Conference on Flexible Automation and Intelligent Manufacturing}, title = {Defining Scalable Data Models for Operational Data Integration in Manufacturing Processes Within the Digital Product Passport Framework Through OPC UA and Asset Administration Shell}, url = {https://link.springer.com/chapter/10.1007/978-3-031-74482-2_14}, volume = {Lecture Notes in Mechanical Engineering ((LNME))}, year = 2024 }
  Link
  https://link.springer.com/chapter/10.1007/978-3-031-74482-2_14
  DOI
  10.1007/978-3-031-74482-2_14

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Marcel Dzubba

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2025

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2024

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Marcel Dzubba

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Sprechstunde

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Publikationen

2025

BibTeX

DOI

2024

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