Papers
10
Total Citations
278
H-Index
6
About
Yves Papegay is a researcher whose work spans robotics, numerical methods, and computational geometry, with particular expertise in robot calibration, interval arithmetic, and cable-driven parallel robots (CDPRs). His most influential contribution, "Choosing Measurement Poses for Robot Calibration with the Local Convergence Method and Tabu Search" (2005, 141 citations), established robust optimization strategies for selecting measurement configurations in robot calibration—a foundational challenge in ensuring accuracy despite sensor noise. Building on this, Papegay has made significant strides in applying interval arithmetic and interval analysis to certify the kinematic calibration of parallel robots, providing rigorous numerical guarantees that traditional methods cannot offer. His research bridges theoretical rigor and real-world application in compelling ways. Notably, he has led the development of large-scale CDPRs deployed in artistic exhibitions—including "The Prince's Tears" (2020)—demonstrating that advanced robotics research can intersect meaningfully with creative practice. More recent work incorporating neural networks with classical Newton methods for sagging-cable kinematics reflects his adaptability to emerging computational tools. With over 270 cumulative citations, Papegay's contributions have shaped both the theoretical foundations and practical methodologies of parallel robot calibration and cable-driven robotic systems.
Research Focus
Key Achievements
Top Papers
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- 2Interval method for calibration of parallel robots: Vision-based experiments71 citations · 2006
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- 9The new exhibition Blind machines, a large 3D printing machine3 citations · 2023
- 10Interval method for calibration of parallel robots2 citations · 2005