Papers
5
Total Citations
22
H-Index
3
About
Zhan Song is a leading researcher in intelligent robotics and 3D computer vision, with a focus on developing high-precision, automated grasping and manipulation systems for industrial applications. His work bridges structured light sensing, deep learning, and visual servoing to enable robots to operate with greater flexibility and accuracy in complex environments. Song’s major contributions include the design of end-to-end deep neural networks for precise grasping of overlapping objects (7 citations) and a high-efficiency triaxial robot grasping system for motor rotors using 3D structured light (6 citations). He has also advanced food package recognition and sorting through structured light and deep learning (5 citations), and tackled challenging underwater environments with polarization-based turbidity removal for 3D reconstruction (2 citations). Notably, his recent work on a mid-joint configured camera for robot precision positioning (2 citations) introduces an innovative hybrid visual servoing approach that combines the flexibility of eye-in-hand with the stability of eye-to-hand configurations. With a growing citation impact and a portfolio of practical, vision-guided robotic systems, Song is shaping the future of automated manufacturing and intelligent grasping.
Research Focus
Key Achievements
Top Papers
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