用智能代理模型预测失踪者位置，提升无人机搜寻效率。
Multi-UAV Search and Rescue in Wilderness Using Smart Agent-Based Probability Models
- 基于代理行为模拟构建概率模型，融合地形特征预测失踪者可能位置。
- 在多种地形上实验显示，搜寻成功率显著高于传统方法。
- 适合需要快速响应的野外搜救场景，尤其适用于复杂地形。
多无人机（Multi-UAV）在荒野搜救（WiSAR）中的应用显著提升了任务成功率，因其能从高空快速覆盖搜索区域并适应复杂地形。时间是荒野搜救的关键因素，随时间推移，生存率下降且搜索范围扩大。若无人机能利用地形特征预测失踪者位置，可进一步提高成功率。本文提出一种基于智能代理的概率模型，结合蒙特卡洛模拟与代理行为策略列表，模拟失踪者在野外的行为模式。同时，设计了分布式多无人机滚动时域搜索策略，采用动态分区机制，并以生成的概率密度模型作为先验信息，优先搜索高概率区域。在不同地形上的仿真搜索实验验证了该方法的高效性，相比基准方法具有更优的搜索性能。
原文摘要 · Abstract (English)
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.
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