N. Marcassus
Papers
1
Total Citations
4
H-Index
1
About
N. Marcassus is a robotics researcher whose work centers on the critical intersection of parametric identification and industrial robot calibration. His primary contribution lies in quantifying the relationship between measurement accuracy and the reliability of physical parameter estimation in robotic systems. In his most cited work, "Minimal resolution needed for an accurate parametric identification - application to an industrial robot arm" (2007, 4 citations), Marcassus systematically investigated how measurement precision affects the outcomes of least squares regression methods—the most widely used approach for identifying robot dynamics. By establishing the minimal sensor resolution required for dependable parameter estimates, he provided practical guidelines for engineers designing identification experiments in industrial settings. This work addresses a fundamental gap in robotics: while least squares methods are valued for their simplicity, their sensitivity to measurement noise had been poorly understood. Marcassus's findings help practitioners avoid costly over-specification of sensors while ensuring identification accuracy. Though his citation count is modest, his contribution is notable for bridging theoretical identification theory with real-world industrial constraints, offering a pragmatic framework that continues to inform robot calibration practices.
Research Focus
Key Achievements
Top Papers
- 1