About

Fang Cui’s research lies at the intersection of robotics, human-robot collaboration, and smart wearable systems, with a focus on practical solutions for industrial automation and human health. Their major contributions include developing a two-step workpiece localization method for robotic de-palletizing that uses region growing and PPHT to overcome unstable ambient light, achieving 13 citations for this work. They also advanced human-robot interaction with a one-shot gesture recognition approach employing attention-based dynamic time warping (DTW), cited 10 times, enabling efficient communication in collaborative systems. Notably, Cui designed a smart legging for posture monitoring during running, leveraging textile sensing networks to prevent knee injuries—a novel application of smart textiles with 5 citations. Earlier work includes mechanical design of a marine in-pipe robot using metamorphic mechanisms and workspace analysis of palletizing robots via AutoCAD. With over 30 total citations across these key papers, Fang Cui demonstrates a versatile impact, bridging industrial robotics and wearable technology, and their gesture recognition work stands out for its potential to transform human-robot teamwork.

Research Focus

Key Achievements

3
H-Index
5
Papers
32
Total Citations
6
Avg Citations/Paper
🏆 Most Cited Paper
A Workpiece Localization Method for Robotic De-Palletizing Based on Region Growing and PPHT
13 citations · 2020
📈 Most Prolific Year: 2020 (1 Papers)
🤝 Key Collaborators: 19
🏛 Institutions: Beijing University of Posts and Telecommunications, University of Electronic Science and Technology of China, Tongji University, Polytechnic University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago