Papers
12
Total Citations
237
H-Index
8
About
Daibing Zhang is a pioneering researcher in bionic underwater robotics and bio-inspired control systems, whose work bridges neural computation and aquatic locomotion. His most influential contributions center on designing artificial neural networks and central pattern generators (CPGs) that enable fish-like robots to achieve natural, efficient swimming. In his highly cited 2007 paper (47 citations), Zhang introduced an artificial bionic neural network to control fish-robot locomotion, laying the groundwork for a decade of innovation. He further advanced the field through computational and experimental studies on undulating fin propulsion (42 citations) and developed a supervised neural Q-learning framework for motion control (42 citations), demonstrating how reinforcement learning can optimize bionic robot behavior. Zhang’s research extends to hydrodynamics, where he modeled biologically inspired fins (37 citations), and to novel actuation systems like a bionic swim bladder (17 citations). His work on CPGs with angular frequency modulation and neural oscillators has been foundational for rhythmic joint control in robotics. With over 230 total citations across his top papers, Zhang has established himself as a key figure in the intersection of neural control, hydrodynamics, and bionic design, inspiring future generations of underwater roboticists.
Research Focus
Key Achievements
Top Papers
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- 6A bionic neural network for fish-robot locomotion16 citations · 2006
- 7CPGs control method using a new oscillator in robotic fish9 citations · 2010
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- 102D monocular visual odometry using mobile-phone sensors5 citations · 2015