无人机海上搜救系统在克罗地亚实测中实现目标精准定位
UAV-Supported Maritime Search System: Experience from Valun Bay Field Trials
- 融合流场重建与动态概率模型，实时规划搜索路径
- 多无人机协同探测结合深度学习，目标检测率显著提升
- 适合海洋搜救、无人系统研发人员参考
本文提出一种集成流场重构、动态概率建模、搜索控制与机器视觉检测的自主海上搜寻系统。在克罗地亚克雷斯岛瓦卢恩湾开展的实地试验中，系统实现了实时漂浮物数据采集，基于计算流体动力学与数值优化的替代流场模型拟合，先进的多无人机搜索控制与视觉感知，以及基于深度学习的目标检测。结果表明，紧密耦合的方法可在真实不确定性与复杂环境条件下可靠探测漂浮目标，为未来自主海上搜救应用提供了切实可行的洞见。
原文摘要 · Abstract (English)
This paper presents the integration of flow field reconstruction, dynamic probabilistic modeling, search control, and machine vision detection in a system for autonomous maritime search operations. Field experiments conducted in Valun Bay (Cres Island, Croatia) involved real-time drifter data acquisition, surrogate flow model fitting based on computational fluid dynamics and numerical optimization, advanced multi-UAV search control and vision sensing, as well as deep learning-based object detection. The results demonstrate that a tightly coupled approach enables reliable detection of floating targets under realistic uncertainties and complex environmental conditions, providing concrete insights for future autonomous maritime search and rescue applications.
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