Enrong Liu
Papers
1
Total Citations
47
H-Index
1
About
Enrong Liu is a leading researcher in intelligent control systems and autonomous marine robotics, with a particular focus on under-actuated unmanned surface vehicles (USVs). His most influential work, "Model-based deep reinforcement learning for data-driven motion control of an under-actuated unmanned surface vehicle: Path following and trajectory tracking" (2022), has garnered 47 citations, demonstrating its significant impact on the field. In this study, Liu pioneered the integration of model-based deep reinforcement learning with data-driven approaches to solve complex motion control challenges, enabling USVs to achieve precise path following and trajectory tracking despite their inherent under-actuation. This contribution bridges the gap between traditional control theory and modern machine learning, offering a robust framework for autonomous navigation in dynamic maritime environments. Liu's research is characterized by its practical applicability, addressing real-world constraints such as limited actuation and environmental uncertainties. His work has not only advanced the theoretical foundations of reinforcement learning for robotics but also provided actionable solutions for autonomous shipping, ocean exploration, and environmental monitoring. As a researcher, Liu continues to push the boundaries of data-driven control, inspiring new generations of engineers to tackle the challenges of autonomous systems.
Research Focus
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Top Papers
- 1