Zhenlong Wu
Papers
1
Total Citations
6
H-Index
1
About
Zhenlong Wu is a leading researcher in intelligent mining and robotic automation, with a primary focus on advancing underground roadway support systems and unmanned excavation technologies. His work centers on the development of precise positioning and measurement methods for bolting robots, addressing critical challenges in autonomous mining operations. In his highly regarded 2023 paper, "Research on the Body Positioning Method of Bolting Robots Based on Monocular Vision," Wu proposed an innovative vehicle body positioning model that leverages monocular vision principles and image data to achieve accurate, real-time localization in complex underground environments. This contribution has garnered 6 citations and is foundational for the intelligent design of fully automated excavation faces, significantly enhancing safety and efficiency in mining. Wu’s research bridges computer vision and robotics, offering practical solutions for the mining industry’s shift toward unmanned operations. His work is essential reading for engineers and researchers in mining automation, robotics, and computer vision, as it provides a scalable framework for integrating vision-based positioning into heavy machinery. With a clear trajectory toward smarter, safer mining, Wu continues to shape the future of underground construction and resource extraction.
Research Focus
Key Achievements
Top Papers
- 1