Xinqing Jia

China University of Mining and Technology

Papers

Total Citations

H-Index

About

Xinqing Jia is a leading researcher in robotics and intelligent mining machinery, with a primary focus on improving the stability and efficiency of automated excavation systems. Their most cited work, "Full coverage cutting path planning of robotized roadheader to improve cutting stability of the coal lane cross-section containing gangue" (2021, 4 citations), addresses a critical challenge in underground mining: the presence of gangue—hard, unwanted rock—which causes severe pick wear and machine vibration during cutting. Jia’s key contribution lies in developing optimized, full-coverage cutting path planning algorithms that enable robotized roadheaders to navigate and cut through coal seams containing gangue with significantly reduced mechanical stress. This work directly enhances the service life and operational reliability of autonomous mining equipment, a vital step toward safer and more productive underground operations. Though early in its citation impact, Jia’s research is foundational for the next generation of intelligent, self-adaptive mining robots. Their achievements represent a meaningful intersection of robotics, path planning, and geotechnical engineering, offering practical solutions for real-world mining challenges.