新的位置科学模型及其在无人机灾害救援中的应用
New Location Science Models with Applications to UAV-Based Disaster Relief
AI总结：
本文提出了一种新的数学框架，用于确定无人机救援任务中移动基站的最佳位置，考虑风影响和无人机异质性，以提高灾害响应效率。
摘要：
自然灾害和人为灾害可能导致严重破坏并造成数千人死亡。因此，开发高效的灾害响应和管理方法是救援队伍的关键任务。有效响应中最关键的组成部分是迅速收集受影响区域、损坏情况和受害者的相关信息。更多的数据意味着更好的协调、更快的救援行动，最终挽救更多的生命。然而，在某些灾害，如地震中，通信基础设施往往被部分或完全摧毁，使受害者难以发送求救信号，使救援队伍难以及时定位并帮助他们。无人驾驶航空器（UAVs）在这样的场景中已成为有价值的工具。特别是，一架无人机可以从移动站派往受影响区域以促进数据收集并建立临时通信网络。然而，无人机的实际部署面临多个挑战，恶劣天气条件——尤其是风——是最显著的挑战之一。为了解决这个问题，我们开发了一种新的数学框架，以确定移动无人机站的最佳位置，同时明确考虑无人机的异质性和风的影响。特别是，我们将塞维斯特问题推广为塞维斯特-费马-托里切利（SFT）问题，该问题在一个统一的框架中捕捉了诸如风影响、无人机异质性和来回运动等复杂因素。所提出的框架通过考虑现实因素如风和无人机异质性，增强了无人机灾害响应规划的实用性。实验结果表明，它可以将浪费的操作时间减少多达84%，使灾后任务显著更加高效和有效。
英文摘要：
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.
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。
