Papers
3
Total Citations
13
H-Index
2
About
Quoc Tuan Vu is a rising researcher in the field of biomimetic robotics, with a primary focus on bio-inspired underwater locomotion. His work centers on the development and optimization of control systems for elongated undulating fin robots, drawing inspiration from the black knife fish to achieve both high-speed swimming and exceptional maneuverability. Vu’s major contributions lie in the application of advanced computational intelligence to robotic control. He pioneered the use of an improved Particle Swarm Optimization-based Central Pattern Generator (CPG) for force optimization, a method that has garnered 9 citations and established a foundation for efficient locomotion. More recently, Vu has pushed the boundaries of the field by integrating deep reinforcement learning, specifically Deep Deterministic Policy Gradient (DDPG) and multi-agent DDPG, to autonomously optimize swimming gaits for speed and propulsive efficiency. Though his most cited works are from 2022 and 2024, his innovative fusion of CPG models with cutting-edge optimization algorithms marks him as a promising contributor to the next generation of agile, efficient underwater robots.
Research Focus
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