Zhigang Bing

Institute of Semiconductors, Tianjin University of Technology and Education

Papers

Total Citations

H-Index

About

Zhigang Bing is a robotics researcher whose work focuses on visual tracking, multi-agent coordination, and service robot autonomy. His most cited contributions center on developing robust tracking algorithms for mobile robots, particularly through the integration of Speed-Up Robust Features (SURF) with Kalman Filters and RANSAC-based matching. In his 2010 paper "Research of tracking robot based on SURF features" (6 citations), Bing proposed a novel method that combines SURF feature matching with Fuzzy C-Means clustering to improve object tracking accuracy in dynamic environments. His subsequent work on tracking models (4 citations) further refined these techniques for real-time robot applications. Beyond visual tracking, Bing has contributed to multi-robot formation control (3 citations), exploring leader-follower models and feedback control strategies using AmigoBot platforms. His earlier work on service robot design and path planning (2007, 3 citations) demonstrates his long-standing interest in practical robotics. Though his citation counts are modest, Bing's research represents foundational work in integrating computer vision techniques with robotic control systems, particularly in the emerging field of feature-based tracking for autonomous navigation.