Runxi Wu
Papers
3
Total Citations
16
H-Index
2
About
Runxi Wu is a researcher specializing in industrial robotics, precision measurement, and automated manufacturing systems. Their work focuses on overcoming critical challenges in robot calibration, vision-based recognition, and intelligent material processing. Wu’s most-cited paper (2023, 11 citations) introduces a novel calibration method for industrial robots under variable load conditions, using a double-ball rotary structure and three contact displacement sensors to correct spatial errors—a key advancement for high-precision automation. In 2024, Wu proposed a recognition and positioning system for black, light-absorbing objects (e.g., volutes) that evade standard RGB-D cameras, enabling automated depalletizing by leveraging reference environment information. Most recently (2025), Wu developed a robot intelligent polishing system for fiber-reinforced plastics (FRP), integrating online measurement for adaptive finishing. These contributions demonstrate a clear trajectory toward solving real-world industrial automation problems, from error compensation to material handling and surface processing. With growing citation impact, Wu’s work is increasingly relevant for researchers and engineers in robotics, computer vision, and smart manufacturing.
Research Focus
Key Achievements
Top Papers
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