Zhenhua Pan
Papers
12
Total Citations
680
H-Index
10
About
Zhenhua Pan is a leading researcher in multi-robot systems, with a focus on path planning, formation control, and obstacle avoidance for unmanned aerial vehicles (UAVs) and bio-inspired snake robots. His most influential work, "An Improved Artificial Potential Field Method for Path Planning and Formation Control of the Multi-UAV Systems," has garnered 378 citations, establishing him as a key contributor to the field. Pan has pioneered the integration of artificial potential fields with adaptive control algorithms, enabling efficient coordination and collision-free navigation in complex environments. His research extends to underwater robotics, where he has developed novel control algorithms for multi-joint snake-like robots using immersed boundary-lattice Boltzmann methods and improved serpenoid curves. With over 700 total citations across his top-cited papers, Pan's work addresses critical challenges in multi-robot coordination, including trajectory tracking, sideslip elimination, and fluid dynamics adaptation. His notable achievements include the MRCDRL framework for deep reinforcement learning-based multi-robot coordination and virtual spring methods for formation control. Pan's contributions are essential for advancing autonomous systems in applications ranging from search-and-rescue missions to environmental monitoring.
Research Focus
Key Achievements
Top Papers
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- 2MRCDRL: Multi-robot coordination with deep reinforcement learning57 citations · 2020
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