Do-Hyun Jang
Papers
3
Total Citations
101
H-Index
3
About
Do-Hyun Jang is a leading researcher in multi-robot systems, focusing on active sensing, environmental learning, and distributed intelligence. His work addresses the critical challenge of enabling robot teams to autonomously explore and model unknown environments without centralized control. Jang’s most impactful contribution, "Multi-Robot Active Sensing and Environmental Model Learning With Distributed Gaussian Process" (77 citations), introduced a pioneering framework where robots collaboratively locate the global maximum of an unknown field using only noisy local measurements, leveraging Gaussian process regression for efficient map building. He further advanced the field with "Distributed Multi-agent Target Search and Tracking With Gaussian Process and Reinforcement Learning" (17 citations), integrating reinforcement learning to enhance target tracking in complex, dynamic settings. His "Fully Distributed Informative Planning for Environmental Learning with Multi-Robot Systems" (7 citations) tackled the critical challenge of online, decentralized cooperation, allowing robots to share information and plan paths without a central coordinator. By solving fundamental problems in distributed coordination and active perception, Jang’s research provides scalable, robust solutions for real-world applications like search-and-rescue, environmental monitoring, and precision agriculture, establishing him as a key innovator in autonomous multi-agent systems.
Research Focus
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Top Papers
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