Niklas Dausend
Papers
1
Total Citations
12
H-Index
1
About
Niklas Dausend’s research focuses on the intersection of simulation technology and automated production systems, with a particular emphasis on real-time collision avoidance. His most-cited work, “Simulation-Based Predictive Real-Time Collision Avoidance for Automated Production Systems” (2023, 12 citations), addresses a critical challenge in industrial automation: preventing collisions between moving kinematics, such as robots, before they occur. By leveraging simulation tools already established in system development and commissioning, Dausend’s approach enables predictive detection of impending collisions, enhancing safety and efficiency without requiring physical prototypes. This contribution is especially valuable for complex, high-speed production environments where traditional reactive safety measures fall short. While his citation count is still growing, the practical relevance of his work—bridging simulation and real-time control—positions him as an emerging voice in automation engineering. His research not only advances theoretical frameworks but also offers tangible solutions for reducing downtime and operational risks in smart factories. For students and researchers, Dausend’s work exemplifies how established simulation methods can be innovatively repurposed to solve pressing industrial problems, making him a researcher to watch in the evolving landscape of cyber-physical production systems.
Research Focus
Key Achievements
Top Papers
- 1