Yong-Shin Kang
Papers
1
Total Citations
2
H-Index
1
About
Yong-Shin Kang is a leading researcher at the intersection of digital twins, reinforcement learning, and intelligent manufacturing systems. His work focuses on developing dynamic, data-driven solutions for complex industrial automation challenges, particularly in the realm of autonomous guided vehicle (AGV) path planning. Kang’s most notable contribution, "Digital Twin-Driven Reinforcement Learning for Dynamic Path Planning of AGV Systems" (2024), pioneers a novel framework that integrates real-time digital twin simulations with reinforcement learning algorithms to enable AGVs to adaptively navigate unpredictable factory environments. This approach significantly improves operational efficiency and reduces collision risks in smart logistics. While his research is still emerging, with his 2024 paper already garnering 2 citations, it signals strong early impact and relevance in the rapidly evolving field of Industry 4.0. Kang’s work is particularly valuable for students and engineers seeking to understand how AI and simulation can converge to create more resilient, autonomous industrial systems. His ongoing research promises to further bridge the gap between virtual modeling and physical control, setting a foundation for next-generation smart manufacturing.
Research Focus
Key Achievements
Top Papers
- 1