Manuel Amersdorfer
Christian-Albrechts-Universität zu Kiel, Karlsruhe Institute of Technology
Papers
5
Total Citations
145
H-Index
4
About
Manuel Amersdorfer is a leading researcher in robotic manipulation and manufacturing, specializing in the control and trajectory planning of industrial robots for complex tasks. His work bridges the gap between theoretical control strategies and practical applications, particularly in machining, material handling, and human-robot collaboration. Amersdorfer’s most significant contributions include pioneering real-time freeform surface tracking for force-controlled robotic tooling, a method that enables precise machining of curved surfaces with 60 citations. He also developed an equidistant tool path planning strategy for robotic machining, which improves accuracy on freeform geometries (42 citations). In the domain of dynamic optimization, his sloshing-free transport of liquid-filled containers using industrial robots (26 citations) offers a time-optimal trajectory planning approach that enhances safety in manufacturing. More recently, Amersdorfer has advanced human-robot collaboration through a digital twin and virtual reality framework for disassembly tasks (14 citations), and he is exploring occlusion learning to prevent robots from self-hiding during operation (3 citations). His work is widely cited for its practical impact on industrial automation, and he is recognized for integrating cutting-edge technologies like VR and digital twins into robotic systems.
Research Focus
Key Achievements
Top Papers
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