Yuhan Kang
Papers
4
Total Citations
69
H-Index
4
About
Yuhan Kang is a leading researcher at the intersection of mobile robotics, multi-agent systems, and game theory, with a core focus on optimizing the coordination of mobile vehicle networks—including unmanned aerial vehicles (UAVs) and mobile robots—for Internet-of-Things (IoT) and mobile crowd sensing (MCS) applications. Their major contribution lies in pioneering the application of **mean-field games** to solve complex, large-scale coordination problems, specifically joint task assignment and collision-free trajectory optimization. Kang’s work addresses the critical challenge of minimizing energy consumption while enabling swarms of mobile agents to efficiently execute sensing tasks. Their most cited paper (30 citations) introduces a novel framework for joint sensing task assignment and trajectory optimization, while subsequent work (23 citations) extends this to multi-population scenarios in MCS. By modeling the interactions of hundreds of agents as a mean-field game, Kang provides a mathematically rigorous and scalable solution that avoids the computational intractability of traditional approaches. Their research is notable for bridging theoretical game theory with practical robotics, offering a blueprint for deploying energy-efficient, autonomous mobile sensor networks in real-world IoT environments.
Research Focus
Key Achievements
Top Papers
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