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.

翻译：本文介绍了一种用于自主海上搜索作业的系统，该系统集成了流场重建、动态概率建模、搜索控制与机器视觉检测技术。在克罗地亚茨雷斯岛瓦伦湾开展的实地实验中，系统实现了实时漂流浮标数据采集、基于计算流体力学与数值优化的代理流场模型拟合、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。实验结果表明，在现实不确定性与复杂环境条件下，紧密耦合的系统方法能够可靠地检测海上漂浮目标，为未来自主海上搜救应用提供了具体的技术参考。