Junwei Yan
Papers
5
Total Citations
225
H-Index
4
About
Junwei Yan’s research sits at the intersection of remanufacturing, cyber-physical production systems, and intelligent robotics, with a particular emphasis on making industrial automation more efficient, adaptive, and human-centric. His most influential contribution is an improved multi-objective discrete bees algorithm for the robotic disassembly line balancing problem in remanufacturing, a work that has garnered 120 citations and addresses critical challenges in sustainable manufacturing. Yan has also advanced human–robot collaboration through a function block-based cyber-physical production system for physical human–robot interaction, cited 52 times, which enables safer and more flexible industrial workflows. More recently, he has pioneered the use of deep reinforcement learning for mobile robot path planning in unknown environments, with two key papers accumulating 50 combined citations; these studies demonstrate how prior knowledge can accelerate learning and improve navigation in unstructured settings. His work on knowledge sharing and evolution in industrial cloud robotics, though earlier in impact, lays groundwork for distributed robotic intelligence. Yan’s research is notable for its practical orientation—bridging algorithmic innovation with real-world manufacturing constraints—and his growing citation record reflects a steady influence on both the remanufacturing and autonomous robotics communities.
Research Focus
Key Achievements
Top Papers
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- 5Knowledge Sharing and Evolution of Industrial Cloud Robotics3 citations · 2018