Maxime Selingue

HESAM Université, École nationale supérieure d'arts et métiers

Papers

Total Citations

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About

Maxime Selingue is a researcher specializing in industrial robotics, with a primary focus on robot calibration and accuracy enhancement. His work addresses a critical challenge in manufacturing: while industrial robots exhibit excellent repeatability, their absolute accuracy often falls short, limiting their application in precision tasks. Selingue’s major contribution lies in developing hybrid calibration methods that combine geometrical identification with artificial neural networks, as demonstrated in his 2022 study. This approach significantly improves robot accuracy by compensating for systematic errors, including those caused by payload variations—a key factor in real-world industrial settings. His 2023 paper on hybrid calibration accounting for payload variation, which has garnered 7 citations, represents his most impactful work to date, highlighting the practical relevance of his research. Additionally, his 2024 analysis of robot base frame identification methods further refines calibration techniques. Selingue’s research bridges the gap between theoretical modeling and industrial application, offering cost-effective solutions for upgrading existing robotic systems. His work is particularly valuable for engineers and researchers seeking to enhance robot performance in assembly, machining, and other high-precision operations without replacing entire robotic fleets.