Shengnan Yang
Papers
1
Total Citations
1
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About
Dr. Shengnan Yang is a leading researcher at the intersection of robotics, digital twin technology, and intelligent manufacturing. Her primary research focuses on enhancing the precision and reliability of industrial robotic systems through data-driven modeling and real-time error compensation. Yang’s most notable contribution is her pioneering work on a digital twin-based paradigm for robotic trajectory error prediction and online compensation. This approach directly tackles the critical industry challenge of limited absolute positioning accuracy, which arises from discrepancies between a robot’s nominal kinematic model and its physical reality. By leveraging data-driven methods, her framework enables real-time correction, significantly boosting operational fidelity. Her 2025 paper on this topic has already garnered early citations, signaling its growing influence in the field. Yang’s work is not only advancing the theoretical foundations of cyber-physical systems but also providing practical, scalable solutions for high-precision automation in manufacturing. Her research is essential reading for engineers and scientists working to bridge the gap between digital simulation and physical robotic performance.
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Top Papers
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