Zetao Yang
Papers
3
Total Citations
10
H-Index
2
About
Zetao Yang is a robotics researcher specializing in vision-based control and manipulation, with a focus on enabling robots to operate effectively in unstructured environments. His work centers on three key areas: visual servoing, 3D object detection, and adaptive filtering for robotic positioning. Yang’s major contributions include developing an optical-flow-based visual servoing method that allows eye-in-hand robots to perform tasks without system calibration, addressing the challenge of feature point loss during rapid camera movement. He also advanced texture-less object detection and grasping by proposing a robust 3D template matching approach that improves upon the LINEMOD algorithm using Iterative Closest Point (ICP) registration. Additionally, Yang designed an adaptive filtering structure for monocular vision-based robotic positioning, eliminating the need for prior knowledge of system and observation noise in real-world workshops. While his citation counts are currently modest—with his most cited paper, “Optical-Flow-Based Visual Servoing for Robotic Moving Control Using Closed-Loop Joints” (2021), receiving 5 citations—his work represents practical solutions to persistent challenges in industrial robotics, particularly in calibration-free and noise-robust control. Yang’s research is notable for its direct applicability to manufacturing and automation settings.
Research Focus
Key Achievements
Top Papers
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