Hongtang Gao
Papers
1
Total Citations
4
H-Index
1
About
Hongtang Gao is a researcher whose work centers on precision measurement and uncertainty evaluation in industrial robotics, with a particular focus on enhancing the reliability of robotic position accuracy. His key contributions lie in applying advanced statistical and grey system theory to address the challenges of small-sample data in metrology. In his most notable work, "Small sample uncertainty evaluation of industrial robot position accuracy measurement based on grey model" (2024), Gao introduced a novel framework that leverages grey models to assess measurement uncertainty when traditional large-sample methods are impractical. This approach is critical for improving the calibration and performance verification of industrial robots in real-world manufacturing environments. Although his work is emerging, with the paper accumulating 4 citations, it represents a significant step toward more robust and efficient uncertainty quantification in robotic systems. Gao’s research is particularly valuable for engineers and metrologists seeking to enhance the accuracy and traceability of automated processes, positioning him as a promising contributor to the fields of precision engineering and industrial robotics.
Research Focus
Key Achievements
Top Papers
- 1