Control of Marine Robots in the Era of Data-Driven Intelligence
Lin Hong, Lu Liu, Zhouhua Peng, and Fumin Zhang

TL;DR
This paper reviews recent advances in data-driven control methods for marine robots, emphasizing the shift from traditional model-based approaches to machine learning techniques for improved autonomy and handling environmental uncertainties.
Contribution
It provides a comprehensive overview of data-driven control strategies for marine robots, highlighting recent progress, open-source resources, and future research directions.
Findings
Notable achievements in data-driven marine robot control
Summarization of open-source tools for control development
Future perspectives for high-level autonomy
Abstract
The control of marine robots has long relied on model-based methods grounded in classical and modern control theory. However, the nonlinearity and uncertainties inherent in robot dynamics, coupled with the complexity of marine environments, have revealed the limitations of conventional control methods. The rapid evolution of machine learning has opened new avenues for incorporating data-driven intelligence into control strategies, prompting a paradigm shift in the control of marine robots. This paper provides a review of recent progress in marine robot control through the lens of this emerging paradigm. The review covers both individual and cooperative marine robotic systems, highlighting notable achievements in data-driven control of marine robots and summarizing open-source resources that support the development and validation of advanced control methods. Finally, several future…
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Taxonomy
TopicsMaritime Navigation and Safety
