Control of Marine Robots in the Era of Data-Driven Intelligence

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 perspectives are outlined to guide research toward achieving high-level autonomy for marine robots in real-world applications. This paper aims to serve as a roadmap toward the next-generation control framework of marine robots in the era of data-driven intelligence.