数据驱动如何重塑海洋机器人控制？顶刊权威综述，港科大、大连海事大学团队综述数据驱动时代下的海洋机器人控制

Core Insights - The article discusses the evolution and significance of underwater robots, highlighting their roles in ocean exploration, resource investigation, engineering inspection, and national defense [1][2][3]. Group 1: Types of Underwater Robots - The family of underwater robots includes Autonomous Underwater Vehicles (AUVs), Remotely Operated Vehicles (ROVs), Unmanned Surface Vehicles (USVs), and underwater gliders (UG), each designed for specific tasks [2][3]. - Bionic underwater robots (BUR) mimic fish movements, while air-water cross-domain robots (AUR) can seamlessly switch between air and underwater operations [3]. Group 2: Control Systems and Challenges - Control systems are crucial for underwater robots, ensuring basic movement and supporting complex autonomous decisions [3]. - Traditional control methods face limitations in complex marine environments due to non-linear dynamics, model uncertainties, and unpredictable disturbances [4][5]. Group 3: Data-Driven Control Approaches - The rise of machine learning has introduced data-driven control methods, which allow robots to learn from data rather than relying solely on precise mathematical models [7]. - Data-driven methods are categorized into model-based, model-free, and hybrid approaches, enhancing adaptability and robustness in control [7][8][9]. Group 4: Multi-Robot Collaboration - Multi-robot systems enhance operational efficiency and coverage, enabling complex tasks like large-scale flow field mapping and underwater collaborative operations [12][14]. - Collaborative control strategies include coordinated formations, game-theoretic competition, and cross-domain cooperation, leveraging the strengths of multiple robots [12][14]. Group 5: Open Source Technology - The emergence of open-source platforms is democratizing ocean robot research, providing low-cost testing environments and hardware options for researchers [15][16]. - Open-source software frameworks like ROS facilitate seamless integration between simulation and real-world applications, promoting algorithm sharing and innovation [16]. Group 6: Future Directions - Future developments in underwater robotics will focus on enhancing intelligence, autonomy, and safety, addressing challenges such as data efficiency and communication constraints [17]. - The integration of physical information learning and offline reinforcement learning will enable rapid learning with minimal data, while advancements in cross-domain collaboration will create a comprehensive exploration network [17].