Papers
8
Total Citations
207
H-Index
7
About
Runyu Tian is a leading researcher in bio-inspired underwater robotics, specializing in the control, modeling, and simulation of fish-like robots. His work bridges the gap between computational fluid dynamics (CFD) and intelligent control systems, with a particular focus on deep reinforcement learning (DRL) for autonomous navigation and maneuvering. Tian’s major contributions include developing a learning framework that enables fish-like robots to transition from simulation to real-world control tasks, achieving over 49 citations for this foundational work. He has also pioneered coupling methods for hydrodynamics, kinematics, and motion control, as seen in his highly cited 2020 paper on self-propelled swimming simulation (36 citations). His research on path-following control using DRL (27 citations) and obstacle avoidance for self-propelled fish (21 citations) has advanced autonomous underwater vehicle capabilities. Notably, Tian has explored three-dimensional dynamic modeling for fin-actuated and tail-actuated robots, incorporating barycentre regulating mechanisms for enhanced stability. His work on artificial lateral line systems for multi-robot formation sensing (19 citations) demonstrates his commitment to biomimetic sensory integration. With over 200 total citations, Tian’s research is instrumental in creating more agile, efficient, and intelligent underwater robots for real-world applications.
Research Focus
Key Achievements
Top Papers
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- 3CFD based parameter tuning for motion control of robotic fish28 citations · 2020
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