Papers
5
Total Citations
24
H-Index
2
About
Lars Klingel is a rising researcher at the forefront of intelligent automation, specializing in simulation-based engineering, robotic manipulation, and human-robot coexistence. His work addresses critical challenges in modern manufacturing, from collision avoidance to the handling of complex, deformable materials. Klingel’s most cited paper, “Simulation-Based Predictive Real-Time Collision Avoidance for Automated Production Systems” (12 citations), establishes a novel framework that leverages established simulation technology to preemptively detect and avoid robot collisions before systems go live. He further advances robotic dexterity with his work on deep learning-based instance segmentation for branched deformable linear objects (7 citations), a key enabler for automating the assembly of wire harnesses in the electrical industry. Klingel also contributes to open, vendor-independent control and simulation platforms, and has developed dynamic safety distance determination methods for human-robot coexistence. His most recent work (2025) pushes toward enhanced virtual commissioning for dynamic sheet metal handling in automotive manufacturing. With a clear trajectory from foundational simulation to adaptive, real-time control, Klingel is shaping the next generation of flexible, safe, and efficient automated production systems.
Research Focus
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Top Papers
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