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About

Yann Quinsat is a leading researcher in the field of automated dimensional metrology, specializing in the development of intelligent scanning strategies for industrial part inspection. His work primarily focuses on optimizing the path planning of laser-plane sensors and industrial robots to achieve high-precision, on-machine inspection. Quinsat’s major contributions include pioneering a novel approach to scan path planning that controls the overlap between successive scanning paths, significantly improving the accuracy and efficiency of 3D part inspection. His 2018 paper on this topic, which has garnered 34 citations, introduces the use of least-squares conformal maps to stretch 3D surfaces onto a 2D plane, enabling more effective path generation. This work, along with his 2019 study on optimal scanning strategies (19 citations), has established him as a key figure in advancing automated quality control for manufacturing. Quinsat’s research directly addresses the challenge of reducing inspection time while maintaining high measurement fidelity, making his contributions highly relevant for industries relying on robotic inspection systems. His notable achievement lies in bridging the gap between theoretical path planning and practical, real-world implementation, as demonstrated by his continued influence on subsequent studies in the field.