Papers
4
Total Citations
88
H-Index
3
About
Yan Lang’s research bridges bio-inspired robotics, fluid dynamics, and intelligent control, with a focus on enabling autonomous underwater locomotion. Their major contributions lie in coupling hydrodynamics, kinematics, and motion control to simulate self-propelled swimming in bionic fish. By integrating deep reinforcement learning, Lang pioneered a numerical method that allows fish-like models to autonomously navigate and avoid obstacles—work that has garnered over 57 citations across key papers. Notably, their 2020 study on deep-reinforcement-learning-controlled swimming (36 citations) and their 2021 investigation into obstacle avoidance (21 citations) demonstrate how computational simulations can replace costly physical experiments for studying maneuverability. Earlier in their career, Lang also advanced optical measurement techniques for spherical actuators, developing a high-precision, three-degree-of-freedom laser-based orientation method (29 citations). This dual expertise—spanning intelligent control of bio-inspired swimmers and precision sensing for robotics—positions Lang as a versatile researcher whose work informs both autonomous underwater vehicles and robotic actuation systems. Their innovative coupling of AI with fluid-structure interaction continues to inspire new approaches in soft robotics and autonomous navigation.
Research Focus
Key Achievements
Top Papers
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