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About

Dr. Zhaojun Yang is a robotics researcher whose work bridges precision manufacturing and intelligent fault diagnosis, with a focus on enhancing the accuracy and reliability of automated systems. His research centers on robot kinematics, path accuracy, and the application of advanced machine learning to industrial robotics. In his foundational work, "A Novel Vision Localization Method of Automated Micro-Polishing Robot" (2009, 10 citations), Dr. Yang developed a vision-based approach that significantly improved the positioning precision of micro-polishing robots, a critical contribution to automated surface finishing. He further advanced the field with "Reliability Analysis of Path Accuracy of Series Robot Based on Quasi-Interval Monte Carlo Method" (2019, 2 citations), where he introduced a novel probabilistic model to account for mechanical tolerances in robotic arm trajectories. Most recently, Dr. Yang has pioneered the use of graph neural networks for industrial diagnostics in "A Diagnostic Framework for Harmonic Drives Based on Dynamic Graph Data Augmentation and Adaptive Knowledge Distillation for Graphs" (2025, 3 citations), addressing the challenge of extracting features from nonlinear, nonstationary operational data. His work demonstrates a clear trajectory from improving robotic precision to enabling intelligent, data-driven maintenance for complex mechanical systems.