Autonomous underwater vehicles (AUVs) are valuable for ocean exploration due to their flexibility and ability to carry communication and detection units. Nevertheless, AUVs alone often face challenges in harsh and extreme sea conditions. This study introduces a unmanned surface vehicle (USV)-AUV collaboration framework, which includes high-precision multi-AUV positioning using USV path planning via Fisher information matrix optimization and reinforcement learning for multi-AUV cooperative tasks. Applied to a multi-AUV underwater data collection task scenario, extensive simulations validate the framework's feasibility and superior performance, highlighting exceptional coordination and robustness under extreme sea conditions. To accelerate relevant research in this field, we have made the simulation code (demo version) available as open-source.

翻译：自主水下航行器（AUVs）因其灵活性及搭载通信与探测单元的能力，在海洋探索中具有重要价值。然而，在恶劣与极端海况下，仅依靠AUVs常面临诸多挑战。本研究提出一种无人水面艇（USV）-AUV协作框架，该框架包含通过费舍尔信息矩阵优化进行USV路径规划的高精度多AUV定位，以及用于多AUV协同任务的强化学习方法。通过应用于多AUV水下数据采集任务场景，大量仿真验证了该框架的可行性与优越性能，突显了其在极端海况下卓越的协同能力与鲁棒性。为加速该领域相关研究，我们已将仿真代码（演示版本）开源发布。