Guanghui Wen
Papers
18
Total Citations
804
H-Index
11
About
Guanghui Wen is a prominent researcher whose work sits at the intersection of multi-robot systems, cooperative control, and multi-agent reinforcement learning. His research has made significant contributions to solving complex coordination challenges in networked robotic systems, particularly focusing on finite-time consensus and formation control for nonholonomic mobile robots. His early landmark studies — including his widely cited 2015 and 2016 papers on finite-time consensus for chained-form nonholonomic systems, collectively accumulating over 380 citations — established foundational frameworks for achieving rapid, provably stable coordination among robot teams. Wen's work extends to Euler-Lagrange networked systems, heterogeneous multi-agent coordination under communication delays, and robust collision-avoidance formation navigation in complex environments. More recently, he has pioneered the integration of machine learning into multi-robot coordination, developing novel frameworks such as DTDE for cooperative multi-agent reinforcement learning and graph-based soft actor-critic algorithms capable of scaling to large robot teams. His use of Gaussian process-based control barrier functions to ensure safety during learning reflects a sophisticated blend of data-driven and model-based approaches. Together, his body of work — spanning theoretical rigor and practical robotics applications — has meaningfully advanced the field of distributed autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 5DTDE: A new cooperative multi-agent reinforcement learning framework44 citations · 2021
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