The application of Multiple Unmanned Aerial Vehicles (Multi-UAV) in Wilderness Search and Rescue (WiSAR) significantly enhances mission success due to their rapid coverage of search areas from high altitudes and their adaptability to complex terrains. This capability is particularly crucial because time is a critical factor in searching for a lost person in the wilderness; as time passes, survival rates decrease and the search area expands. The probability of success in such searches can be further improved if UAVs leverage terrain features to predict the lost person's position. In this paper, we aim to enhance search missions by proposing a smart agent-based probability model that combines Monte Carlo simulations with an agent strategy list, mimicking the behavior of a lost person in the wildness areas. Furthermore, we develop a distributed Multi-UAV receding horizon search strategy with dynamic partitioning, utilizing the generated probability density model as prior information to prioritize locations where the lost person is most likely to be found. Simulated search experiments across different terrains have been conducted to validate the search efficiency of the proposed methods compared to other benchmark methods.

翻译：多无人机系统在野外搜救中的应用，因其能够从高空快速覆盖搜索区域并适应复杂地形，显著提升了任务成功率。这一能力尤为关键，因为在野外搜寻失踪人员时时间是决定性因素：随着时间推移，生存率会下降而搜索范围会扩大。若无人机能利用地形特征预测失踪人员位置，此类搜索的成功概率可得到进一步提升。本文旨在通过提出一种结合蒙特卡洛模拟与代理策略列表的智能代理概率模型来增强搜救任务效能，该模型可模拟失踪人员在野外区域的行为模式。此外，我们开发了一种基于动态分区的分布式多无人机滚动时域搜索策略，利用生成的概率密度模型作为先验信息，优先搜索失踪人员最可能出现的区域。通过在不同地形场景下进行仿真搜索实验，验证了所提方法相较于其他基准方法的搜索效率优势。