Zhibiao Yan

Papers

Total Citations

H-Index

About

Dr. Zhibiao Yan is a leading researcher in the field of robotics, with a primary focus on the precision and calibration of hybrid robots—machines that combine the rigidity of serial and parallel kinematic structures. His major contributions center on enhancing the accuracy of multi-degree-of-freedom (DOF) robotic systems through innovative data-driven calibration and real-time error compensation techniques. Dr. Yan’s most influential work, "Pose error prediction and real-time compensation of a 5-DOF hybrid robot" (2022, 71 citations), provides a foundational method for predicting and correcting positional errors during operation, significantly improving industrial robot performance. He further advanced the field with an improved data-driven calibration method for 6-DOF hybrid robots, which dramatically increases measurement efficiency and practicality—a crucial step for real-world manufacturing applications. His recent work on alleviating local overfitting in data-driven calibration (2023) addresses a critical limitation in machine learning-based approaches, ensuring more robust and reliable robot accuracy. Dr. Yan’s research is pivotal for industries requiring high-precision automation, such as aerospace and machining, and his methods are widely cited as benchmarks for hybrid robot calibration.