Xiaobiao Ge

Papers

Total Citations

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About

Xiaobiao Ge is a researcher whose work bridges robotics, signal processing, and precision engineering, with a focus on improving the dynamic performance and calibration of industrial robots. His key research areas include operational modal analysis, vibration signal processing, and data-driven calibration for hybrid and milling robots. Ge’s most cited paper, "Removal of AM-FM harmonics using VMD technology for operational modal analysis of milling robot" (2023, 13 citations), introduces a novel variational mode decomposition (VMD) approach to eliminate amplitude and frequency modulation harmonics, enabling more accurate modal identification in robotic milling operations—a critical contribution to chatter avoidance and machining stability. In another notable work, "A Local Overfitting Alleviation Method for Data-Driven Calibration Applied in a 5-DOF Hybrid Robot" (2023, 2 citations), he addresses a key challenge in robot calibration by mitigating local overfitting, enhancing the accuracy and reliability of data-driven models for hybrid robots. Though early in his career, Ge’s work demonstrates a clear impact on advancing robotic machining and precision control, offering practical solutions for real-world industrial applications.