Zhongbin Wang
Papers
5
Total Citations
53
H-Index
4
About
Zhongbin Wang is a leading researcher in intelligent mining robotics, with a focus on autonomous navigation and perception systems for extreme underground environments. His work addresses the critical challenges of operating robots in coal mines, where narrow roadways, heavy dust, and poor illumination degrade conventional sensors. Wang’s most impactful contributions include novel multi-sensor fusion positioning methods that integrate redundant IMUs, UWB, and visual images to achieve robust localization for anti-punching drilling robots and mobile equipment, with his top-cited papers accumulating 19 and 18 citations, respectively. He has also pioneered deep learning-based approaches for pressure relief hole recognition using SinGAN and improved Faster R-CNN, and developed adaptive image enhancement techniques guided by no-reference quality evaluation to improve robotic perception in low-visibility conditions. Additionally, Wang has advanced coal-rock drilling state recognition through multi-sensor information fusion, enabling intelligent drilling for rockburst prevention. His work is instrumental in driving the automation and safety of coal mining operations, earning recognition as a key contributor to the field of mining robotics and intelligent equipment.
Research Focus
Key Achievements
Top Papers
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