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

2025-06-26

AI Summary

To address the autonomy bottlenecks faced by marine robots under strong nonlinear dynamics, system uncertainties, and complex oceanic environments, this paper proposes a data-driven intelligent paradigm integrating deep learning, reinforcement learning, and classical/modern control theory to establish an adaptive control framework for both single-agent and multi-agent cooperative operations. We systematically survey recent key advances and, for the first time, unify open-source toolchains, benchmark datasets, and simulation platforms into a reproducible technical ecosystem. The work identifies core research directions essential for achieving high-level autonomy—including robust learning, safety-constrained reinforcement learning, cross-domain transfer, and distributed coordination—thereby providing theoretical foundations and practical pathways for the transition of marine robotics from model-based to trustworthy intelligent control.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Multiagent Systems: Multiagent Learning
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Overcoming limitations of model-based marine robot control
• Integrating data-driven intelligence into marine robot strategies
• Advancing autonomy in complex marine environments

Innovation

• Data-driven intelligence replaces model-based control
• Machine learning addresses nonlinearity and uncertainties
• Open-source resources support advanced control validation