Papers
6
Total Citations
203
H-Index
6
About
Benyan Huo is a leading researcher at the intersection of intelligent manufacturing, robotic perception, and precision control. His work primarily focuses on advancing automation in welding, power infrastructure inspection, and medical robotics. Huo’s most impactful contribution is in deep learning for laser vision seam tracking, where his 2022 paper has garnered 84 citations, pioneering robust laser stripe extraction methods that overcome traditional image processing limitations. He further advanced industrial inspection through multi-feature fusion and convolutional networks for welding defect detection (54 citations) and developed PLE-Net, a deep learning approach for automatic power line extraction from aerial images (35 citations). In robotics, Huo has made notable strides in motion control, including antisaturation control for cable-driven continuum robots and impedance control for robotic needles with fiber optic force sensors, enhancing precision in surgical applications. His cascaded ESO-based multi-task priority control for redundant robots (2023) demonstrates continued innovation in null-space compliance. With a portfolio spanning from factory floors to surgical theaters, Huo’s work is driving the next generation of autonomous, adaptive robotic systems.
Research Focus
Key Achievements
Top Papers
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- 4ESO-Based Antisaturation Motion Control for Cable-Driven Continuum Robots14 citations · 2024
- 5Impedance control of a robot needle with a fiber optic force sensor10 citations · 2016
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