Papers
4
Total Citations
30
H-Index
4
About
Huifeng Kang is a rising leader in bio-inspired robotics, specializing in the hydrodynamics and intelligent control of biomimetic robotic fish. Their research centers on solving fundamental challenges in underwater locomotion: optimizing propulsion efficiency, achieving precise trajectory tracking, and modeling complex fluid-structure interactions. Kang’s most impactful work, a 2024 study on propulsion curve analysis for a three-degree-of-freedom pectoral fin, uses multi-layer perception to minimize resistance during the fin’s recovery stroke, directly improving swimming efficiency. This paper has garnered 15 citations, establishing a foundation for subsequent advances. In 2025, Kang introduced a data-driven dynamic model integrating attention mechanisms and deep neural networks with a nonlinear model predictive controller, enabling precise trajectory control of robotic fish—a breakthrough for autonomous underwater missions. Further contributions include motion trajectory prediction via LSSVR interactive networks and numerical simulations of pectoral fin and body gait hydrodynamics. Collectively, Kang’s work bridges computational modeling and real-world application, with a growing citation record that underscores their influence in the field. Their innovative fusion of machine learning and biomechanics is shaping the next generation of agile, efficient underwater robots.
Research Focus
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