USV与AUV协同系统在极端海况下实现精准定位与稳定合作。
Never too Cocky to Cooperate: An FIM and RL-based USV-AUV Collaborative System for Underwater Tasks in Extreme Sea Conditions
- 基于FIM优化的USV路径规划，提升多AUV定位精度。
- 强化学习驱动多AUV协同执行任务，性能优于基线方法。
- 适合水下探测、搜救等极端环境作业，开源可复现。
本文提出一种新型无人水面艇（USV）与自主水下航行器（AUV）协同系统，旨在提升极端海况下的水下任务表现。系统采用双策略：（1）基于费舍尔信息矩阵（FIM）优化的USV路径规划，实现高精度多AUV定位；（2）基于强化学习的多AUV协同规划与控制方法，用于任务执行。在水下数据采集任务中开展大量实验，验证了系统的可行性。定量结果表明，该系统在极端海况下仍保持良好协同能力与运行稳定性，性能显著优于基线方法。为促进可复现性与社区发展，项目提供开源仿真工具包，地址：https://github.com/360ZMEM/USV-AUV-colab。
原文摘要 · Abstract (English)
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 .
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