Zhenghua Chen

Institute for Infocomm Research, Agency for Science, Technology and Research, Binzhou Medical University

Papers

Total Citations

212

H-Index

About

Zhenghua Chen is a leading researcher at the intersection of robotics, artificial intelligence, and intelligent systems, with key contributions in computer vision, indoor positioning, and medical robotics. His work on "Detecting the shuttlecock for a badminton robot: A YOLO based approach" (105 citations) showcases his expertise in applying deep learning to real-time object detection for autonomous sports robots. Chen also addresses the persistent challenge of indoor robot localization with his paper "WiFi-Based Indoor Robot Positioning Using Deep Fuzzy Forests" (92 citations), which innovatively combines fuzzy logic with deep learning to overcome the limitations of vision-based and domain-dependent methods. More recently, his systematic review on "Image-guided, surgical robot-assisted percutaneous puncture" (15 citations) critically examines the benefits and challenges of integrating imaging and robotic systems into minimally invasive surgery, highlighting his growing impact in medical robotics. With over 200 total citations, Chen’s work is shaping the future of autonomous systems, from sports arenas to operating rooms, making him a notable figure in applied AI and robotics research.