ROBUST ENERGY-AWARE UNMANNED AERIAL VEHICLE ROUTING USING ENSEMBLE WEATHER FORECASTS

Abstract

The Marine Corps seeks to develop energy-aware unmanned aerial vehicle (UAV) routing for last-mile logistics resupply. UAVs have limited range and time on station to execute their assigned mission. To optimize the delivery of supplies to dispersed units, users must optimally utilize the internal energy onboard the UAV while considering external factors such as weather and priorities of resupply requests. Energy-aware UAV routing will increase Marine Corps logistics capabilities during expeditionary advanced base operations (EABO). The current EABO construct places forces within the threat rings of adversary weapon systems. Use of UAVs can allow dispersed forces to operate in the adversary’s threat rings without the stoppage of logistical support. This thesis builds upon the two-layer framework developed in previous theses by Jatho (2020) and Haller (2021) to include ensemble weather forecasts and partial delivery of supplies. The first layer, which solves the boundary value problem to obtain optimal trajectories between all nodes in the network, is solved for each member of the ensemble forecast. The second layer consists of a stochastic vehicle routing problem using the cost matrix from the first layer. This thesis also introduces the notion of partial delivery of supplies in the second layer to allow demand nodes to request multiple packages of supplies that can be delivered by multiple UAVs. Finally, this thesis analyzes various case studies and corresponding results.