Tomas Germann
Technische Universität Braunschweig, Institute of Automation
Papers
2
Total Citations
158
H-Index
2
About
Tomas Germann is a leading researcher in the field of human-robot collaboration (HRC), with a primary focus on enhancing safety and operational reliability in shared industrial workspaces. His work addresses the fundamental challenge of enabling robots to move safely alongside humans in unstructured environments, where unpredictable human behavior and variable workspaces create complex safety requirements. Germann’s most influential contribution is his 2018 paper on a "Machine Learning-Enhanced Digital Twin Approach for Human-Robot-Collaboration," which has garnered 126 citations. This work pioneered the integration of digital twin technology with machine learning to create real-time, adaptive safety systems that can predict and respond to dynamic human movements. His earlier foundational research, "Simulation Platform to Investigate Safe Operation of Human-Robot Collaboration Systems" (2016, 32 citations), established critical simulation tools for assessing HRC systems before physical deployment, addressing a key industry bottleneck. Germann’s research has significantly advanced the practical implementation of safe, efficient human-robot teams in manufacturing, bridging the gap between theoretical safety models and real-world industrial applications.
Research Focus
Key Achievements
Top Papers
- 1A Machine Learning-Enhanced Digital Twin Approach for Human-Robot-Collaboration126 citations · 2018
- 2