Jinghai Han
Papers
1
Total Citations
11
H-Index
1
About
Jinghai Han is a robotics researcher whose work focuses on intelligent visual perception and autonomous manipulation for industrial applications. His key research areas include 3D vision-based robotic grasping, point cloud processing, and human-robot collaboration in manufacturing environments. Han’s most notable contribution is a novel Kinect V2-based method for visual recognition and grasping of yarn-bobbin-handling robots, published in 2022. This work addresses a critical challenge in textile automation by enabling robots to autonomously perceive and manipulate yarn bobbins using three-dimensional point cloud data. By integrating depth sensing with advanced noise removal and recognition algorithms, Han’s method significantly reduces human dependency in repetitive handling tasks. With 11 citations, this research has already influenced the development of cost-effective vision systems for industrial robotics. Han’s work exemplifies the practical application of computer vision and robotics to solve real-world manufacturing problems, offering a scalable solution for smart factory automation. His contributions are particularly valuable for researchers and engineers seeking to implement robust, sensor-driven robotic systems in unstructured industrial environments.
Research Focus
Key Achievements
Top Papers
- 1