Jie Fang

Papers

Total Citations

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About

Jie Fang is a robotics researcher whose work centers on improving the precision and reliability of industrial robotic systems. His most notable contribution lies in the domain of kinematic parameter calibration for handling robots, where he has addressed a critical challenge in modern automation: the inherent positional inaccuracies that limit robot performance in real-world applications. In his 2017 study, Fang proposed an innovative error compensation methodology grounded in the modified Denavit-Hartenberg (D-H) kinematic model, leveraging differential mathematics to linearize complex error models and make calibration computationally tractable. This approach represents a meaningful step forward in bridging the gap between theoretical robot kinematics and practical operational accuracy. While his citation record is in its early stages, with his calibration work accumulating 2 citations, his research addresses a foundational problem that underpins countless industrial automation applications, from manufacturing assembly lines to logistics systems. For students and researchers working in robot accuracy, motion planning, or industrial automation, Fang's methodology offers a principled framework for understanding and correcting the kinematic errors that inevitably arise in deployed robotic systems.