Head of the Research Department Innovative Factory Systems
Prof. Dr.-Ing. Martin Ruskowski
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Contact
Phone (Kaiserslautern): +49 631 20575 3400
Martin.Ruskowski@dfki.de
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Address
(Kaiserslautern)
Trippstadter Straße 122
D-67663 Kaiserslautern
Deutschland
Publications
Profile
Short Biography
Prof. Dr.-Ing. Martin Ruskowski is Head of the Innovative Factory Systems research department at the German Research Center for Artificial Intelligence (DFKI) and is Chair of the “Department of Machine Tools and Control Systems” at the Technical University of Kaiserslautern and chairman of the board of the technology initiative SmartFactory KL. His major research focus is on the development of innovative control concepts for automation, artificial intelligence in automation technology and industrial robots as machine tools. Prior to these positions, Ruskowski held several management positions at industrial firms, most recently as Vice President for Global Research and Development at KUKA Industries Group.
STAR
Safe and Trusted Human Centric ARtificial Intelligence in Future Manufacturing Lines
STAR is a joint effort of AI and digital manufacturing experts towards enabling the deployment of standard-based secure, safe and reliable human-centric AI systems in real-life manufacturing…
PHYSICS
Optimized hybrid space-time service continuum in FAAS
PHYSICS’s vision is to address the aforementioned challenges and empower application developers, platform owners and infrastructure providers (e.g. Cloud, Edge etc). For the former, the goal is to…
ReCircE
Digital Lifecycle Record for the Circular Economy
Kunststoffmüll ist ein Problem. Weltweit. Der Grund: Kunststoff ist meist verbaut und Teil eines komplexen Produktes: Auto, Kühlschrank, Lederschuh oder Smartphone. Materialanalyse, -trennung und…
MAS4AI
Multi-Agent Systems for Pervasive Artificial Intelligence for assisting Humans in Modular Production Environments
Development of a multi-agent-based technology for the integration of AI algorithms in autonomous modular production with worker assistance.
Partners
- DMD Med Tech
- InTechCentras
- LMS
- tecnalia
- aimen
- TNO
- …
RACKET
Rare Class Learning and Unknown Events Detection for Flexible Production
The RACKET project addresses the problem of detecting rare and unknown faults by combining model-based and machine learning methods. The approach is based on the assumption that a physical or…
