Jicheng Wan
Papers
4
Total Citations
37
H-Index
3
About
Jicheng Wan is an emerging researcher specializing in intelligent robotics and automation for underground mining environments, with a particular focus on coal mine roadway operations. His work sits at the intersection of robotic systems, computer vision, and autonomous path planning — areas critical to modernizing one of the world's most hazardous industrial settings. Wan's most notable contributions include developing a binocular vision-based positioning method for locating anchor drilling holes in coal mine roadways (14 citations), significantly advancing the automation of roof bolt support systems that protect miners from unstable rock strata. Complementing this, his high-precision drilling research employs hybrid visual servo control to enhance rock bolting accuracy in underground conditions. His work on ant colony optimization for energy-efficient cutting trajectory planning (16 citations) demonstrates a strong command of metaheuristic algorithms applied to real-world robotic challenges. More recently, his collision prediction framework for safe path planning addresses the unique constraints of navigating robotic roadheaders through narrow tunnels. Though early in his career, Wan's cumulative citation impact — exceeding 37 citations across recent publications — signals growing recognition within the mining robotics community. His research holds meaningful implications for improving both operational efficiency and worker safety in underground mining worldwide.
Research Focus
Key Achievements
Top Papers
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