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-optimized USV path planning, and (2) reinforcement learning-based cooperative planning and control method 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, we provide an open-source simulation toolkit available at: https://github.com/360ZMEM/USV-AUV-colab .

翻译：本文开发了一种新型的无人水面艇（USV）-自主水下航行器（AUV）协作系统，旨在提升极端海况下的水下任务性能。该系统融合了双重策略：（1）通过费舍尔信息矩阵优化的USV路径规划实现高精度多AUV定位，以及（2）基于强化学习的多AUV任务执行协同规划与控制方法。在水下数据收集任务中的大量实验评估证明了该系统的操作可行性，定量结果显示其性能相较于基线方法有显著提升。所提出的系统展现了USV与AUV之间强大的协调能力，同时在极端海况下保持了稳定性。为促进可复现性与领域发展，我们提供了一个开源仿真工具包，地址为：https://github.com/360ZMEM/USV-AUV-colab。