Shuyong Liu
Papers
1
Total Citations
12
H-Index
1
About
Shuyong Liu is a pioneering researcher in autonomous underwater vehicle (AUV) motion control, with a focus on enhancing vehicle survivability through intelligent, adaptive systems. His work bridges deep reinforcement learning and marine robotics, addressing critical challenges in ocean exploration, hydrological research, maritime rescue, and undersea military operations. Liu’s most cited paper, “A control strategy of normal motion and active self-rescue for autonomous underwater vehicle based on deep reinforcement learning” (2022, 12 citations), introduces a novel framework that enables AUVs to transition seamlessly from normal operation to active self-rescue in dangerous situations—a capability largely overlooked in prior research. This contribution is notable for its practical impact, offering a pathway to safer, more reliable autonomous missions in complex underwater environments. By integrating reinforcement learning with motion control, Liu has advanced the field’s understanding of how AUVs can adapt to unforeseen hazards, potentially reducing mission failure rates. His work stands as a key reference for researchers developing resilient marine robotic systems, with growing influence as the demand for robust AUVs expands in scientific and industrial applications.
Research Focus
Key Achievements
Top Papers
- 1