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About

Gregor Lux is a researcher specializing in industrial robotics, precision measurement, and automated inspection systems. His work sits at the critical intersection of robot kinematics, accuracy calibration, and quality control in manufacturing environments, addressing the growing demand for flexible, cost-effective alternatives to traditional coordinate measuring machines. Lux's most influential contribution, "Automated and Cycle Time Optimized Path Planning for Robot-Based Inspection Systems" (2016, 14 citations), demonstrates his focus on making robot-based optical 3D inspection both practical and efficient for in-line production use. Complementing this, his 2014 analysis of industrial robot positioning accuracy (8 citations) provided the manufacturing community with deeper insight into the kinematic and physical limitations that constrain robot precision — foundational knowledge for anyone working toward high-accuracy robotic applications. His 2015 work on automated self-calibration (5 citations) directly tackled the key barrier preventing robots from replacing coordinate measuring machines, while his 2017 investigation into non-geometric accuracy effects (3 citations) further refined understanding of articulated robot pose accuracy beyond standard calibration approaches. Collectively, Lux's research has meaningfully advanced the feasibility of deploying industrial robots in high-precision inspection roles, making him a notable contributor to smart manufacturing and automated quality assurance.