Yongchen Guo
Papers
6
Total Citations
52
H-Index
5
About
Yongchen Guo is a leading researcher in robot-assisted minimally invasive surgery (RMIS), with a primary focus on solving the critical challenge of force perception. His work centers on developing high-accuracy, lightweight deep neural networks for grip force measurement, enabling surgeons to achieve better control performance without the need for additional sensors. Guo’s most influential contribution, the CAM-FoC network (21 citations), introduced a deep-learning method that measures instrument grip force from training trajectory data, setting a new standard for sensorless force estimation. He has also advanced vision-based hand–eye calibration and motion hysteresis compensation for cable-driven surgical instruments, addressing real-world issues of mass production and large-scale identification. His novel grip force cognition scheme, which integrates dynamic analysis and prior knowledge, further demonstrates his ability to combine feature engineering with deep learning. With over 50 total citations across his key publications, Guo’s work is driving the next generation of smarter, more perceptive surgical robots, making RMIS safer and more effective for patients worldwide.
Research Focus
Key Achievements
Top Papers
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