Zhenyang Shang

Papers

Total Citations

H-Index

About

Zhenyang Shang is a researcher whose work bridges robotics, computer vision, and intelligent control systems. His most cited contribution, "Development of a calibrating algorithm for Delta Robot’s visual positioning based on artificial neural network" (2016, 13 citations), addresses a critical challenge in precision automation: enhancing the accuracy of Delta robots through neural network-based visual calibration. This work demonstrates his expertise in integrating artificial intelligence with robotic kinematics to improve real-time positioning, a key requirement in high-speed manufacturing and pick-and-place operations. While his citation count reflects a focused and emerging impact, Shang’s research is notable for its practical application—offering a data-driven alternative to traditional geometric calibration methods. His approach has implications for reducing error in automated systems, making his work relevant to engineers and researchers in industrial robotics and machine vision. As the field moves toward more adaptive and intelligent automation, Shang’s contributions provide a foundation for further exploration in neural network-enhanced robotic control.