Papers
10
Total Citations
304
H-Index
7
About
Hesuan Hu is a distinguished researcher whose work spans multirobot systems, automated manufacturing scheduling, and discrete-event systems, with a particular focus on solving the complex, intertwined challenges of collision avoidance, deadlock prevention, and motion planning. His most celebrated contributions lie in developing distributed algorithms for multirobot coordination — his 2017 papers on collision and deadlock avoidance and real-time motion planning have collectively garnered over 120 citations, establishing him as a leading voice in scalable, decentralized robot control. Hu's 2018 work on robust multi-robot control further extended these ideas to handle system uncertainties, while his 2019 survey on deadlock control policies in automated manufacturing systems (41 citations) demonstrates his broad command of resource allocation and system reliability challenges. His research on dual-arm cluster tool scheduling addresses the demanding precision of semiconductor wafer fabrication, reflecting a practical engineering dimension to his theoretical strengths. More recently, Hu has explored deep reinforcement learning for motion planning and supervisory control of networked discrete-event systems, signaling an exciting evolution toward intelligent, learning-enabled automation. Across his career, his work has meaningfully advanced both the theoretical foundations and real-world applicability of autonomous multi-agent systems.
Research Focus
Key Achievements
Top Papers
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- 4A distributed approach to robust control of multi-robot systems41 citations · 2018
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- 6A distributed method to avoid higher-order deadlocks in multi-robot systems32 citations · 2019
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- 10Criticality-Guided Deep Reinforcement Learning for Motion Planning3 citations · 2021