Xiaoqiang Guo

China University of Mining and Technology

Papers

Total Citations

H-Index

About

Xiaoqiang Guo is a leading researcher in mining robotics and intelligent visual perception, with a primary focus on enhancing safety and automation in underground mining environments. His work centers on developing advanced computer vision and image processing techniques to enable precise, real-time positioning of drilling robots for rockburst prevention—a critical challenge in mechanized mining. Guo’s most-cited paper, “Visual detection of drilling robot position for rockburst prevention in mining processing by a new image dehazing method” (2024), introduces an innovative dehazing algorithm that effectively removes dust and particulate interference from images captured in harsh mining conditions. This contribution directly addresses the problem of degraded visual data, enabling more accurate drill pipe positioning and advancing the goal of unmanned, autonomous drilling operations. While his citation count is still emerging, Guo’s research represents a vital step toward integrating robust visual sensing into mining automation, with potential to significantly reduce human risk and improve operational efficiency. His work is particularly notable for its practical application in real-world, high-dust environments, bridging the gap between laboratory algorithms and field-ready solutions.