Skill-based multi-agent control for safe and effective human-robot collaboration
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Achim Wagner
,
Nigora Gafur
Prof. Dr.-Ing. Martin Ruskowski earned his doctorate in 2004 at the Institute of Mechanics at Leibniz University Hannover, focusing on active magnetic guides in machine tool construction. In 2005, he began working in various leadership positions at industrial companies such as Lenze Group, Carl Cloos Welding Technology, and KUKA Industries. Since 2017, he has been a Professor of Machine Tools, leading the “Machine Tools and Controls” chair at the Rhineland-Palatinate Technical University in Kaiserslautern (formerly TU Kaiserslautern). In the same year, he became Scientific Director of the Innovative Factory Systems research department at the German Research Center for Artificial Intelligence (DFKI). In 2019, Ruskowski took over as voluntary Chairman of SmartFactory-KL from Prof. Dr.-Ing. Detlef Zühlke. He further developed the Industry 4.0 concept in collaboration with researchers and association members. His “Production Level 4” (PL4) vision incorporates technical advancements and practical insights gained since 2011. He places particular emphasis on the role of humans in the future of production.
Abstract
Due to their complexity, various tasks in production technology are still carried out manually. Support by robotic systems often fails due to the high safety requirements and the associated integration costs. In this paper, a novel agent-based approach for the design and integration of human-robot teams is presented, which models the autonomous behavior and task sharing between humans and robots symmetrically and structures it on several levels. An essential component here is the uniform description of human and robotic context-specific autonomous skills and the software modules realized with them, which can be freely configured for the respective task, taking into account the respective safety conditions. The reusability of interoperable software modules saves costs and ensures interchangeability between different manufacturers and systems. The approach is implemented using the example of a collaborative assembly scenario and demonstrated for the assembly of a toy truck in the SmartFactory Kaiserslautern.
Zusammenfassung
Verschiedene Aufgaben in der Produktionstechnik werden aufgrund ihrer Komplexität noch manuell durchgeführt. Die Unterstützung durch Robotersysteme scheitert oft an den hohen Sicherheitsanforderungen und den damit verbundenen Integrationskosten. In dieser Arbeit wird ein neuartiger agentenbasierter Ansatz für die Gestaltung und Integration von Mensch-Roboter-Teams vorgestellt, der der das autonome Verhalten und die Aufgabenteilung zwischen Menschen und Robotern symmetrisch modelliert und auf mehreren Ebenen strukturiert. Eine wesentliche Komponente ist dabei die einheitliche Beschreibung der kontextspezifischen autonomen Fähigkeiten von Mensch und Roboter und der damit realisierten Softwaremodule, die für die jeweilige Aufgabe unter Berücksichtigung der jeweiligen Sicherheitsbedingungen frei konfiguriert werden können. Die Wiederverwendbarkeit interoperabler Softwaremodule spart Kosten und sichert die Austauschbarkeit zwischen verschiedenen Herstellern und Systemen. Der Ansatz wird am Beispiel eines kollaborativen Montageszenarios umgesetzt und am Beispiel der Montage eines Spielzeugtrucks in der SmartFactory Kaiserslautern demonstriert.
About the authors
Dr.-Ing. habil. Achim Wagner, DFKI Research Fellow (born 1968) is head of research in the Innovative Factory Systems department at the German Research Center for Artificial Intelligence. His current research focuses on dependable robotics and autonomous production systems. Previously, he worked in the research fields of electrical engineering materials, medical and rehabilitation robotics, autonomous mobile robots and human-technology interaction at Saarland University, University of Mannheim and Heidelberg University.
Dr.-Ing. Nigora Gafur is a research associate at the Chair of Machine Tools and Control Systems. She received her M.Sc. degree in Mechanical Engineering from the Karlsruhe Institute of Technology (KIT) and her Ph.D. from the University of Kaiserslautern-Landau. Her research focuses on optimal control, optimization-based task and motion planning, and model predictive control in robotics.
Dipl.-Ing. Aleksandr Sidorenko (born 1982) is a senior researcher in the Innovative Factory Systems department at the German Research Center for Artificial Intelligence. He focuses on the development of autonomous cyber-physical production systems, where he applies the methods from skill-based engineering and holonic production systems.
MSc. Parsha Pahlevannejad (born 1987) is a senior researcher in the Innovative Factory Systems department at the German Research Center for Artificial Intelligence. He received his M.Sc. degree in Computer Science in the Rhineland-Palatinate Technical University in Kaiserslautern (formerly TU Kaiserslautern). His current research area is on Human-Machine Interaction, Human-Robot Collaboration and Assistant Systems.
M.Sc. Khalil Abuibaid is a Research Assistant in the Department of Mechanical and Process Engineering at RPTU Kaiserslautern-Landau, affiliated with the Chair of Machine Tools and Control Systems. His research focuses on robot motion and interaction control, with a particular emphasis on model predictive control (MPC), robot learning, and the integration of reinforcement learning techniques to enhance the performance, adaptability, and intelligence of control strategies in robotic systems.
Prof. Dr.-Ing. Martin Ruskowski earned his doctorate in 2004 at the Institute of Mechanics at Leibniz University Hannover, focusing on active magnetic guides in machine tool construction. In 2005, he began working in various leadership positions at industrial companies such as Lenze Group, Carl Cloos Welding Technology, and KUKA Industries. Since 2017, he has been a Professor of Machine Tools, leading the “Machine Tools and Controls” chair at the Rhineland-Palatinate Technical University in Kaiserslautern (formerly TU Kaiserslautern). In the same year, he became Scientific Director of the Innovative Factory Systems research department at the German Research Center for Artificial Intelligence (DFKI). In 2019, Ruskowski took over as voluntary Chairman of SmartFactory-KL from Prof. Dr.-Ing. Detlef Zühlke. He further developed the Industry 4.0 concept in collaboration with researchers and association members. His “Production Level 4” (PL4) vision incorporates technical advancements and practical insights gained since 2011. He places particular emphasis on the role of humans in the future of production.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: All other authors state no conflict of interest.
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Research funding: The authors would like to thank for financial support within the projects “Building a collaborative and cooperative robotics platform – KoKoBot”, “Künstliche Intelligenz für Energietechnologien und Anwendungen in der Produktion – KI4ETA (03EN2053B)” and “Resilient and Adaptive Supply Chains for Capability-based Manufacturing as a Service Networks – RAASCEMAN (EU-RIA Program under the grant agreement No 101138782)”.
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Data availability: Not applicable.
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© 2025 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Methods
- Flachheitsanalyse nichtlinearer Systeme aus algebraischer Perspektive
- On using the output progression in unscented Kalman filtering
- Methodology for optimizing convolutional neural networks for fast production processes
- Skill-based multi-agent control for safe and effective human-robot collaboration
- Applications
- Advancements in intelligent wheelchairs: a scoping review
- Dissertations
- Industrie 4.0-angepasste Datenverkehrskonzepte für industrielle 5G-TSN-Netzwerke
- Modelling and Management of Wireless Communication Systems based on Digital Twins
Articles in the same Issue
- Frontmatter
- Methods
- Flachheitsanalyse nichtlinearer Systeme aus algebraischer Perspektive
- On using the output progression in unscented Kalman filtering
- Methodology for optimizing convolutional neural networks for fast production processes
- Skill-based multi-agent control for safe and effective human-robot collaboration
- Applications
- Advancements in intelligent wheelchairs: a scoping review
- Dissertations
- Industrie 4.0-angepasste Datenverkehrskonzepte für industrielle 5G-TSN-Netzwerke
- Modelling and Management of Wireless Communication Systems based on Digital Twins
