Junxue He

Papers

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About

Junxue He’s research lies at the intersection of robotic control, spray painting automation, and intelligent manufacturing. His most influential work introduces a predictive model for coating growth rate during varied dip-angle spraying, leveraging a Gaussian sum model to solve a longstanding challenge in automatic spray painting. This contribution, with 15 citations, provides a theoretical foundation for more precise and efficient robotic painting, directly impacting industrial coating processes. He further advances robotic adaptability through work on uncalibrated visual servoing, employing support vector regression (SVR) to estimate Jacobian matrices in unknown environments, enabling robots to perform impedance control guided by visual feedback. This approach enhances robotic autonomy and precision in complex tasks. With a focus on bridging theoretical modeling and practical application, He’s research demonstrates significant potential for improving automation in manufacturing. His contributions are particularly notable for addressing real-world industrial problems, offering solutions that improve both efficiency and quality in robotic systems.