Papers
4
Total Citations
32
H-Index
3
About
Mao Jun is a researcher whose work bridges robotics, autonomous systems, and intelligent sensing technologies, with particular expertise in mobile robotics, underground mining automation, and aerial vehicle perception. His research has made meaningful contributions to some of the most technically demanding environments in robotics — from subterranean tunnels to aerial surveillance scenarios. Among his most influential contributions is his work on multi-sensor information fusion for laser SLAM-based mobile robot positioning, which has garnered 14 citations and addresses a critical challenge in autonomous navigation: achieving reliable localization in complex, dynamic environments. His investigations into roadheader robotics for tunnel excavation — including kinematic analysis of profiling memory cutting systems and boom angle detection using triaxial digital acceleration sensors — demonstrate a sustained commitment to advancing automation in hazardous underground mining operations, where human safety depends on precise mechanical control. More recently, Mao Jun has extended his research into aerial robotics, developing target detection and recognition algorithms for moving UAVs using machine vision, reflecting his ability to adapt core sensing and control principles across diverse robotic platforms. With citations spanning nearly a decade of work, his research portfolio reflects a coherent and evolving vision for intelligent robotic systems capable of operating reliably in challenging real-world environments.
Research Focus
Key Achievements
Top Papers
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