USV与多AUV协同导航，提升极端海况下水下任务效率
USV-AUV Collaboration Framework for Underwater Tasks under Extreme Sea Conditions
- 基于费雪信息矩阵与强化学习的USV路径规划，实现多AUV高精度定位
- 仿真验证在极端海况下仍保持优异协作性能和鲁棒性
- 开源模拟代码，助力水下协同系统研究
自主水下航行器（AUV）因灵活性强且可搭载通信与探测设备，在海洋勘探中具有重要价值。然而，在恶劣和极端海况下，单一AUV常面临挑战。本研究提出一种无人水面艇（USV）与多AUV协同框架，结合基于费雪信息矩阵优化的路径规划与强化学习，实现多AUV的高精度定位及协同任务执行。在多AUV水下数据采集场景中，大量仿真验证了该框架的可行性与优越性能，展现出在极端海况下的出色协调能力与鲁棒性。为推动该领域研究，已开放仿真代码（演示版）作为开源资源。
原文摘要 · Abstract (English)
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.
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