固定翼无人机在海洋环境中的搜索策略与船只检测
A Search Strategy and Vessel Detection in Maritime Environment Using Fixed-Wing UAVs
AI总结：
本文针对无GNSS海洋环境中固定翼无人机的自主搜索与船只检测问题，提出了基于概率地图的有信息搜索和基于预定路径的无信息搜索方法，并通过合成数据训练的检测算法在仿真中验证了有效性。
摘要：
本文研究了在未知且无GNSS的海洋环境中，使用固定翼无人机进行自主搜索和船只检测的问题。在定位受限、通信范围有限以及无人机和传感器总数有限的环境中，主要挑战是实施合适的搜索策略，以便尽快检测到目标船只。因此，我们提出了用于搜索环境的有信息和无信息方法。有信息方法依赖于获取的概率地图，而无信息方法则使无人机沿着根据环境计算的预定路径导航。船只检测方法使用数据标注工具在模拟器中收集的合成数据进行训练。仿真中的对比实验表明，我们的传感器组合、搜索方法和船只检测算法的结合，能够在如此具有挑战性的环境中成功搜索到目标船只。
英文摘要：
In this paper, we address the problem of autonomous search and vessel detection in an unknown GNSS-denied maritime environment with fixed-wing UAVs. The main challenge in such environments with limited localization, communication range, and the total number of UAVs and sensors is to implement an appropriate search strategy so that a target vessel can be detected as soon as possible. Thus we present informed and non-informed methods used to search the environment. The informed method relies on an obtained probabilistic map, while the non-informed method navigates the UAVs along predefined paths computed with respect to the environment. The vessel detection method is trained on synthetic data collected in the simulator with data annotation tools. Comparative experiments in simulation have shown that our combination of sensors, search methods and a vessel detection algorithm leads to a successful search for the target vessel in such challenging environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
