A priori assessment of a smart-navigated unmanned aerial vehicle disaster cargo fleet

Abstract

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.