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About

Eric Vasselin is a researcher specializing in robotic precision, repeatability modeling, and performance evaluation of industrial and medical robot manipulators. His work has made significant contributions to the field of robotics metrology, particularly through the development and application of the stochastic ellipsoid approach — a sophisticated mathematical framework for characterizing the repeatability of industrial robots with greater accuracy than conventional methods. Vasselin's most influential publication, "Modelling of repeatability phenomena using the stochastic ellipsoid approach" (2005, 32 citations), introduced the use of jump processes and covariance matrix analysis to model positional variability in manipulator robots. He extended this framework experimentally using a Kuka industrial robot (2006, 22 citations) and later broadened its scope to encompass orientation repeatability and comparative analyses between serial and parallel robot architectures. His 2012 work on micrometre-scale robot performance challenged the sufficiency of traditional repeatability and accuracy indices, proposing new precision metrics for high-resolution applications. With contributions spanning theoretical modeling, experimental validation, and cross-platform performance comparison, Vasselin's cumulative citation impact of over 100 reflects his meaningful influence on how researchers and engineers quantify and improve robot precision. His work on dental implantation robotics further demonstrates the real-world clinical relevance of his research.