Papers
17
Total Citations
330
H-Index
9
About
Ying Bai is a prominent researcher specializing in robotics calibration, intelligent measurement systems, and advanced interpolation techniques. His work sits at the intersection of machine learning, fuzzy logic, and precision engineering, with a sustained focus on improving the positional accuracy of robot manipulators and parallel machine tools. Bai's most influential contributions include pioneering the application of neural networks and camera-based measurement systems for robot calibration, a method that has garnered 70 citations and reshaped modeless calibration approaches. Equally significant is his comparative analysis of trilinear, cubic spline, and fuzzy interpolation techniques (58 citations), which provided the robotics community with clear, evidence-based guidance for selecting error-compensation strategies. His early development of dynamic fuzzy error mapping systems and multi-beam laser tracking calibration further cemented his reputation as an innovator in high-accuracy measurement. Across more than a decade of research, Bai has consistently bridged theoretical rigor and practical engineering, making sophisticated calibration methods more accessible to field practitioners. With a body of work accumulating over 300 citations, his contributions have meaningfully advanced robot precision across industrial and research settings, making him a foundational reference for students and engineers working in robotics metrology and intelligent control systems.
Research Focus
Key Achievements
Top Papers
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- 4Calibration of multi-beam laser tracking systems35 citations · 2003
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- 6Improving Position Accuracy of Robot Manipulators Using Neural Networks23 citations · 2006
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