Papers
5
Total Citations
35
H-Index
3
About
Haifeng Fang is a robotics researcher whose work bridges tactile sensing, intelligent automation, and mobile robot mobility. His key research areas include flexible tactile sensors, robotic perception for object recognition, and the mechanical design of climbing and tracked robots. Fang made significant contributions by developing an interdigital flexible tactile sensor using Velostat, enabling robots to acquire detailed tactile information about objects—a crucial step for dexterous manipulation. He also advanced fruit recognition and classification by integrating tactile data from flexible hands, achieving 13 citations for this innovative approach. In the domain of construction automation, Fang applied deep learning with an improved YOLO-V7 network for real-time detection of waste impurities, garnering 11 citations. His earlier work on articulated tracked robots, which examined how fiber release affects step-climbing performance, laid foundational insights for communication robot design. Additionally, Fang proposed a Halbach square array structure to optimize magnetic adsorption for wall-climbing robots, enhancing their ability to navigate ship hulls. With over 35 total citations across his publications, Fang’s research demonstrates a consistent focus on enhancing robot-environment interaction through novel sensing and mechanical solutions.
Research Focus
Key Achievements
Top Papers
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- 4Halbach Square Array Structure for Optimizing Magnetic Adsorption3 citations · 2021
- 5Interdigital flexible tactile sensor based on Velostat2 citations · 2023