Zedong Huang

About

Dr. Zedong Huang is a leading researcher at the intersection of computer vision, robotics, and intelligent manufacturing. His work focuses on enabling robots to perceive and interact with their environment with high precision, particularly in industrial and agricultural settings. Dr. Huang’s most impactful contribution is his pioneering application of deep learning to quality control, as evidenced by his highly cited 2019 paper on an improved YOLO V3 algorithm for detecting electronic components on printed circuit boards (160 citations). This work is foundational for automated inspection in the 3C (Computer, Communication, and Consumer Electronics) industry. He has further advanced robotic perception by developing a high-precision apple recognition and localization method using RGB-D data and improved SOLOv2 instance segmentation (25 citations), directly supporting the development of intelligent harvesting robots. Dr. Huang also makes significant contributions to robot control theory, designing robust model predictive control (MPC) schemes and nonlinear disturbance observers to enhance the performance and stability of robotic manipulators. His research, spanning from object detection to advanced control, is driving the next generation of autonomous, intelligent robots.

Research Focus

Key Achievements

Top Papers

1. 1
   Application Research of Improved YOLO V3 Algorithm in PCB Electronic Component Detection

   160 citations · 2019
2. 2
   High-precision apple recognition and localization method based on RGB-D and improved SOLOv2 instance segmentation

   25 citations · 2024
3. 3
   Blind-Kriging based natural frequency modeling of industrial Robot

   15 citations · 2021
4. 4
   Hierarchical multiloop MPC scheme for robot manipulators with nonlinear disturbance observer

   8 citations · 2022
5. 5
   Optimal design of model predictive controller based on transient search optimization applied to robotic manipulators

   6 citations · 2022

Key Collaborators

Contact & Links