Michiel Vlaeyen

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About

Michiel Vlaeyen is a leading researcher at the intersection of robotic vision, non-destructive testing, and industrial metrology. His work focuses on solving critical challenges in robot-guided X-ray computed tomography (CT) and range sensor planning for automated inspection systems. Vlaeyen’s major contributions include pioneering methods for geometric qualification of robot CT with flexible trajectories, enabling high-quality volumetric reconstruction from non-circular scanning paths—a key advancement for inspecting complex, large, or irregularly shaped parts. His research on viewpoint planning for range sensors introduces the concept of feature cluster constrained spaces, allowing robot vision systems to efficiently compute optimal sensor positions for multi-feature inspection tasks. With over 7,000 citations across his body of work, Vlaeyen’s impact is substantial, particularly in industrial automation and quality control. Notable achievements include developing calibration frameworks that address the limitations of conventional methods, which assume rigid circular paths, thereby unlocking the full flexibility of robotic CT systems. His work is essential reading for researchers and engineers advancing adaptive, high-precision inspection technologies in manufacturing and robotics.