Kaifan Zhong
Papers
6
Total Citations
187
H-Index
4
About
Kaifan Zhong is a leading researcher in intelligent robotic systems, specializing in computer vision, human-robot interaction, and precision control. His most influential work, "A robust weld seam recognition method under heavy noise based on structured-light vision" (2019), has garnered 155 citations, establishing a foundational approach for industrial automation in challenging environments. Zhong’s contributions span hand-eye calibration for robot vision systems, where he introduced a single planar constraint method (2024), and advanced stability control through fuzzy-PI algorithms for real-time tracking (2021). Notably, his recent prior information-assisted neural network (2024) enhances point cloud segmentation in human-robot interaction scenarios by leveraging robot joint angles as contextual data, improving safety and adaptability. His research also addresses path correction via global-local matching (2021) and force tracking impedance control for contour following (2022), demonstrating a comprehensive focus on robust, autonomous systems. With over 180 total citations, Zhong’s work bridges theoretical innovation and practical deployment, making significant impacts in manufacturing, collaborative robotics, and intelligent automation.
Research Focus
Key Achievements
Top Papers
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- 6Force Tracking Impedance Control Based on Contour Following Algorithm3 citations · 2022