Papers

3

Total Citations

18

H-Index

3

About

Quan Zeng is pioneering the next generation of robot-assisted minimally invasive surgery, with a sharp focus on ultrasound-guided puncture and endovascular intervention. His work directly addresses critical clinical challenges: improving precision, stability, and safety in robotic systems. In his 2024 paper on adaptive control for ultrasound-guided puncture robots, Zeng introduces a novel learning strategy that enables a multi-joint robotic arm to perform human ultrasound scanning with enhanced autonomy and accuracy—a key step toward reducing operator reliance and radiation exposure. His 2023 work on precise calibration for robot-assisted percutaneous puncture (RAPP) systems tackles the pre-surgical accuracy bottleneck, ensuring that robotic targeting aligns flawlessly with clinical needs. Earlier, in 2019, Zeng proposed a fully sensorized endovascular robotic system designed to preserve the interventionist’s natural intra-operative behavior, a breakthrough in master-slave robotic design that prioritizes both safety and ergonomics. With over 18 citations across his most influential papers, Zeng’s contributions are shaping the future of computer-assisted intervention, making robotic surgery safer, more intuitive, and more accessible for patients and clinicians alike.

Research Focus

Key Achievements

3
H-Index
3
Papers
18
Total Citations
6
Avg Citations/Paper
🏆 Most Cited Paper
An Adaptive Control Method and Learning Strategy for Ultrasound-Guided Puncture Robot
10 citations · 2024
📈 Most Prolific Year: 2024 (1 Papers)
🤝 Key Collaborators: 15
🏛 Institutions: Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago