考虑风速与无人机差异，优化救援无人机站位置
New Location Science Models with Applications to UAV-Based Disaster Relief
- 提出SFT模型统一建模风、无人机差异和往返飞行
- 实测可减少84%无效作业时间
- 适合灾害应急规划与无人机调度研究者
自然灾害与人为灾害常造成严重破坏并导致大量人员伤亡。高效灾后响应与管理对救援团队至关重要，其中快速获取受灾区域信息、损毁情况和受困者位置尤为关键。更多数据有助于提升协调效率、加快救援速度，最终挽救更多生命。然而在地震等灾害中，通信基础设施常被完全或部分摧毁，导致幸存者难以发出求救信号，救援队也难以及时定位并施救。无人机（UAV）已成为此类场景下的重要工具，尤其可通过从移动站派出机群前往灾区进行数据采集并建立临时通信网络。但实际部署面临诸多挑战，其中恶劣天气（尤其是风力）是主要因素之一。为此，本文提出一种新的数学框架，用于确定移动无人机站的最优位置，明确考虑了无人机异质性及风的影响。具体而言，将经典Sylvester问题扩展为包含风影响、无人机异质性及往返运动的Sylvester-Fermat-Torricelli（SFT）问题，构建统一模型。实验结果表明，该框架可使灾后任务中的无效作业时间减少高达84%，显著提升基于无人机的灾后响应效率与实用性。
原文摘要 · Abstract (English)
Natural and human-made disasters can cause severe devastation and claim thousands of lives worldwide. Therefore, developing efficient methods for disaster response and management is a critical task for relief teams. One of the most essential components of effective response is the rapid collection of information about affected areas, damages, and victims. More data translates into better coordination, faster rescue operations, and ultimately, more lives saved. However, in some disasters, such as earthquakes, the communication infrastructure is often partially or completely destroyed, making it extremely difficult for victims to send distress signals and for rescue teams to locate and assist them in time. Unmanned Aerial Vehicles (UAVs) have emerged as valuable tools in such scenarios. In particular, a fleet of UAVs can be dispatched from a mobile station to the affected area to facilitate data collection and establish temporary communication networks. Nevertheless, real-world deployment of UAVs faces several challenges, with adverse weather conditions--especially wind--being among the most significant. To address this, we develop a novel mathematical framework to determine the optimal location of a mobile UAV station while explicitly accounting for the heterogeneity of the UAVs and the effect of wind. In particular, we generalize the Sylvester problem to introduce the Sylvester-Fermat-Torricelli (SFT) problem, which captures complex factors such as wind influence, UAV heterogeneity, and back-and-forth motion within a unified framework. The proposed framework enhances the practicality of UAV-based disaster response planning by accounting for real-world factors such as wind and UAV heterogeneity. Experimental results demonstrate that it can reduce wasted operational time by up to 84%, making post-disaster missions significantly more efficient and effective.
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