The United Nations Office for the Coordination of Humanitarian Affairs has asserted that risks in deployment of unmanned aerial vehicles (UAVs) within disaster response must be reduced by careful development of best-practice standards before implementing such systems. With recent humanitarian field tests of cargo UAVs as indication that implementation may soon become reality, a priori assessment of a smart-navigated (autonomous) UAV disaster cargo fleet via simulation modeling and analysis is vital to the best-practice development process. Logistical problems with ground transport of relief supplies in Puerto Rico after Hurricane Maria (2017) pose a compelling use scenario for UAV disaster cargo delivery. In this context, we introduce a General Purpose Assessment Model (GPAM) that can estimate the potential effectiveness of a cargo UAV fleet for any given response region. We evaluate this model using the following standards: (i) realistic specifications; (ii) stable output for various realistic specifications; and (iii) support of humanitarian goals. To this end, we discuss data from humanitarian cargo delivery field tests and feedback from practitioners, perform sensitivity analyses, and demonstrate the advantage of using humanitarian rather than geographic distance in making fleet delivery assignments. We conclude with several major challenges faced by those who wish to implement smart-navigated UAV cargo fleets in disaster response, and the need for further GPAM development. This paper proposes the GPAM as a useful simulation tool to encourage and guide steps toward humanitarian use of UAVs for cargo delivery. The model’s flexibility can allow organizations to quickly and effectively determine how best to respond to disasters.

中文翻译：

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智能导航无人机灾难货运车队的先验评估

联合国人道主义事务协调厅声称，在实施此类系统之前，必须通过仔细制定最佳实践标准来降低在灾害响应中部署无人驾驶飞行器 (UAV) 的风险。随着最近对货运无人机的人道主义现场测试表明实施可能很快成为现实，通过模拟建模和分析对智能导航（自主）无人机灾难货运车队进行先验评估对于最佳实践开发过程至关重要。飓风玛丽亚（2017 年）过后，波多黎各救灾物资地面运输的后勤问题为无人机灾难货物运输带来了令人信服的使用场景。在这种情况下，我们引入了通用评估模型 (GPAM)，该模型可以估计货运无人机机队在任何给定响应区域的潜在有效性。我们使用以下标准评估该模型：(i) 实际规格；(ii) 各种实际规格的稳定输出；(iii) 支持人道主义目标。为此，我们讨论了来自人道主义货物交付现场测试的数据和从业人员的反馈，进行了敏感性分析，并展示了在进行车队交付任务时使用人道主义而不是地理距离的优势。我们总结了那些希望在灾难响应中实施智能导航无人机货运机队的人面临的几个主要挑战，以及进一步发展 GPAM 的必要性。本文提出 GPAM 作为一种有用的模拟工具，以鼓励和指导将无人机用于货运的人道主义用途。该模型的灵活性可以让组织快速有效地确定如何最好地应对灾难。