This paper develops a novel Unmanned Surface Vehicle (USV)–Autonomous Underwater Vehicle (AUV) collaborative system designed to enhance underwater task performance in extreme sea conditions. The system integrates a dual strategy: (1) high-precision multi-AUV localization enabled by Fisher Information Matrix (FIM)-optimized USV path planning, and (2) a Reinforcement Learning (RL)-based cooperative planning and control framework for multi-AUV task execution. Extensive experimental evaluations in the underwater data collection task demonstrate the system's operational feasibility, with quantitative results showing significant performance improvements over baseline methods. The proposed system exhibits robust coordination capabilities between USV and AUVs while maintaining stability in extreme sea conditions. To facilitate reproducibility and community advancement,

This study presents a novel environment-aware reinforcement learning (RL) framework designed to augment the operational capabilities of autonomous underwater vehicles (AUVs) in underwater environments. Departing from traditional RL architectures, the proposed framework integrates an environment-aware network module that dynamically captures flow field data, effectively embedding this critical environmental information into the state space. This integration facilitates real-time environmental adaptation, significantly enhancing the AUV’s situational awareness and decision-making capabilities. Furthermore, the framework incorporates AUV structure characteristics into the optimization process, employing a large language model (LLM)-based iterative refinement mechanism that leverages both environmental conditions and training outcomes to optimize task performance. Comprehensive experimental evaluations demonstrate the framework’s superior performance, robustness and adaptability.

The adaptivity and maneuvering capabilities of Autonomous Underwater Vehicles (AUVs) have drawn significant attention in oceanic research, due to the unpredictable disturbances and strong coupling among the AUV’s degrees of freedom. In this paper, we developed large language model (LLM)-enhanced reinforcement learning (RL)-based adaptive S-surface controller for AUVs. Specifically, LLMs are introduced for the joint optimization of controller parameters and reward functions in RL training. Using multi-modal and structured explicit task feedback, LLMs enable joint adjustments, balance multiple objectives, and enhance task-oriented performance and adaptability. In the proposed controller, the RL policy focuses on upper-level tasks, outputting task-oriented high-level commands that the S-surface controller then converts into control signals, ensuring cancellation of nonlinear effects and unpredictable external disturbances in extreme sea conditions. Under extreme sea conditions involving complex terrain, waves, and currents, the proposed controller demonstrates superior performance and adaptability in high-level tasks such as underwater target tracking and data collection, outperforming traditional PID and SMC controllers.3

The solution of how to accurately and timely predict the flowrate of gas-liquid mixtures is the key to help petroleum and other related industries to reduce costs, improve efficiency, and optimize management. Although numerous studies have been carried out over the past decades, the problem is still significantly challenging due to the complexity of multiphase flows. This paper attempts to seek new possibilities for multiphase flow measurement and novel application scenarios for state-of-the-art machine learning (ML) techniques. Convolutional neural networks (CNNs) are applied to predict the flowrate of multiphase flows for the first time and can achieve promising performance. In addition, considering the difference between data distributions of training and testing samples and its negative impact on prediction accuracy of the CNN models on testing samples, we propose flow adversarial networks (FANs) that can distill both domain-invariant and flowrate-discriminative features from the raw input. The method is evaluated on dynamic experimental data of different multiphase flows on different flow conditions and operating environments. The experimental results demonstrate that FANs can effectively prevent the accuracy degradation caused by the gap between training and testing samples and have better performance than state-of-the-art approaches in the flowrate prediction field.