Marc Becquet

Université Libre de Bruxelles, Joint Research Centre

Papers

Total Citations

200

H-Index

About

Marc Becquet is a pioneering figure in the field of robotic calibration and precision engineering. His primary research areas center on kinematic modeling, geometrical parameter identification, and performance measurement for industrial robots. Becquet’s most influential contribution is his 1991 paper on "Kinematic calibration and geometrical parameter identification for robots," which has garnered 193 citations. In this seminal work, he introduced a maximum-likelihood approach for identifying geometrical errors and developed a novel experimental setup for measuring end-reflector position errors—a technique that significantly advanced the accuracy of robotic systems. He further explored the use of neural network techniques for identifying non-geometrical parameters, demonstrating an early adoption of machine learning in robotics. Additionally, his 1988 work on static and dynamic performance measurements established a comprehensive test bench for evaluating robot resolution, repeatability, accuracy, and trajectory fidelity. Becquet’s contributions have provided foundational methodologies for improving robot precision, influencing both academic research and industrial applications. His work remains a key reference for engineers and researchers seeking to enhance robotic performance through systematic calibration.