Fang Ren

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About

Fang Ren is a researcher whose work sits at the critical intersection of mining engineering and advanced robotics, focusing on the automation and precision control of heavy mining equipment. Ren’s primary research areas include the kinematics of scraper and armoured face conveyors, the development of intelligent straightening methods for longwall mining systems, and the application of industrial robot models to solve complex spatial positioning problems in underground environments. Ren’s major contributions center on addressing the fundamental challenge of maintaining conveyor straightness during coal extraction—a problem that directly impacts mining efficiency and safety. By modeling the floating connecting mechanisms between hydraulic supports and conveyors as space kinematic systems, Ren developed novel virtual straightening methods that correct positional errors caused by the complex, three-dimensional movement of these massive machines. This work has been recognized by the research community, with Ren’s most-cited paper, “Virtual straightening of scraper conveyor based on the position and attitude solution of industrial robot model” (2021), accumulating 22 citations. A subsequent refinement of this approach, published in 2022, has already garnered 8 citations, demonstrating the growing interest in Ren’s solutions for improving sensing accuracy and automation in longwall mining. Through this innovative fusion of robotics and mining mechanics, Fang Ren is helping to pave the way for smarter, more autonomous underground operations.