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About

Ruoyu Pan is a researcher specializing in radio frequency identification (RFID) systems, robotics, and intelligent manufacturing, with a particular focus on optimizing RFID performance in dynamic and mobile environments. His work addresses critical challenges at the intersection of Internet of Things (IoT) technology and autonomous robotics, developing practical solutions for real-world industrial applications such as unmanned warehouses and smart manufacturing facilities. Pan's most notable contributions include designing effective anti-collision algorithms that reduce missed readings and accelerate tag identification for mobile RFID robots, earning 11 citations and establishing him as a voice in this specialized domain. His research extends to fast tag identification systems compatible with both passive UHF and active sensor tags, as well as machine learning-driven approaches — including neighborhood rough sets, random forests, and GWO-MLP neural networks — to predict identification rates and improve hardware deployment strategies. More recently, he has developed real-time system status evaluation methods that enable adaptive parameter adjustment in dynamic scenarios. Collectively accumulating over 26 citations, Pan's body of work demonstrates a consistent drive to bridge theoretical algorithm design with practical robotic deployment, making meaningful contributions to smarter, more efficient RFID-integrated automation systems.