Papers
6
Total Citations
59
H-Index
5
About
Wanrong Huang’s research lies at the intersection of multi-robot systems, behavior planning, and deep reinforcement learning, with a focus on enabling intelligent, coordinated autonomy under real-world constraints. Her most influential work introduces “BT Expansion,” a sound and complete algorithm for automatically synthesizing Behavior Trees for intelligent robots—a contribution that has garnered 21 citations and addresses a critical bottleneck in robotic behavior design. She has also made pioneering contributions to connectivity preservation in multi-robot systems, developing deep Q-network and DDPG-based learning frameworks (with 12 and 9 citations, respectively) that allow robot teams to maintain communication links during cooperative tasks. Her research further extends to dynamic task allocation for heterogeneous robot teams under communication constraints, and multi-feature fusion for sequential control of mobile robots. Across her publications, Huang demonstrates a consistent commitment to bridging theoretical algorithms with practical deployment challenges, such as limited communication and heterogeneous capabilities. Her work is essential reading for researchers in multi-robot coordination, behavior planning, and learning-based control, offering both foundational theory and actionable solutions for complex, real-world robotic systems.
Research Focus
Key Achievements
Top Papers
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- 2Deep Q-Learning to Preserve Connectivity in Multi-robot Systems12 citations · 2017
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