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About

Lukas Grundel is a robotics and manufacturing researcher whose work centers on advancing industrial robot capabilities for precision machining applications, automation, and dynamic system identification. His research addresses one of the most persistent challenges in the field: enabling industrial robots to perform high-quality milling and machining tasks despite their inherent limitations in stiffness and absolute accuracy. Grundel's most influential contribution, "Model-based process planning for milling operations using industrial robots" (2018, 12 citations), established a systematic framework for leveraging robots' cost and flexibility advantages while compensating for their mechanical shortcomings. Building on this, his 2019 work on feed-forward control for process force compensation (8 citations) proposed intelligent control strategies to counteract the large dynamic forces that conventional robot controllers struggle to manage during milling. Beyond machining, Grundel has contributed to flexible automation for small and medium-sized manufacturing enterprises (6 citations) and has pursued deeper understanding of robot dynamics through friction modeling and frequency-based inertial parameter identification — work that lays the groundwork for more accurate robot control. His cumulative research, totaling over 37 citations, reflects a coherent research vision: making industrial robots genuinely viable for demanding manufacturing environments through smarter modeling, identification, and control.