Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions

Abstract

Besides commercial and military applications, unmanned aerial vehicles (UAVs) are now used more commonly in disaster relief operations. This study proposes a novel model for proactive and reactive planning (different scenarios) that allow for a higher degree of realism, thus a higher likelihood for a mission of being executed according to the plan even when weather forecasts are changing. The novelty of this study results from the addition of a function of resistance of UAVs mission to changes in weather conditions. We link the influence of weather conditions on the UAV’s energy consumption. The goal is to ensure the completion of planned deliveries by a fleet of UAVs under changing weather conditions before their batteries discharge and to identify the emergency route for returned if the mission cannot be completed. An approach based on constraint programming is proposed, as it has proven to be effective in various contexts, especially related to the nonlinearity of the system’s characteristics. The proposed approach has been tested on several instances, which have allowed for analyzing how the plan of mission is robust to the changing weather conditions with different parameters, such as the fleet size, battery capacity, and distribution network layout.

Modeling and Characterization of Traffic Flow Patterns and Identification of Airspace Density for UTM Application

“…There is special need to develop scenario-driven planning approaches that aim to optimize plans for UAVs and enable the selection of who to serve, which routes to take and how much to deliver [79]. Due to limited resources and other restrictions, planning a relief operation is difficult, particularly for last mile delivery activities (from distribution centers to beneficiaries) [80].…”

Traffic Flow Prediction for UTM Application: A Deep Learning Approach

“…There is special need to develop scenario-driven planning approaches that aim to optimize plans for UAVs and enable the selection of who to serve, which routes to take and how much to deliver [22]. Due to limited resources and other restrictions, planning a relief operation is difficult, particularly for last mile delivery activities (from distribution centers to beneficiaries) [23].…”

Drone-Based Delivery Systems: A Survey on Route Planning

“…In contrast, the strategy that keeps a constant ground speed is usually less efficient in terms of power consumption, but it achieves to complete the deliveries earlier. An extension of this work is presented in Radzki et al (2021) [10], where several applications of the proposed model are presented considering a disaster relief scenario. Among the applications proposed, there is the feasibility assessment of existing plans under given wind conditions; the identification of non-accessible areas that, without taking into account winds, would have been reachable by drones; the planning of emergency routes to return to the depot in case the actual wind conditions are worst than the forecast.…”

“…Among the applications proposed, there is the feasibility assessment of existing plans under given wind conditions; the identification of non-accessible areas that, without taking into account winds, would have been reachable by drones; the planning of emergency routes to return to the depot in case the actual wind conditions are worst than the forecast. The authors in [9] consider scenarios with different wind speeds and angles (10 m/s 30 • , 11 m/s 130 • , 12 m/s 230 • ), fleet size (2, 3, 4) and delivery demand (4,6,8,10). The objective is to compare the two proposed strategies for computing energy consumption considering wind effects.…”

“…The results show the energy consumed given the scenario and under varying wind conditions. The authors in [10] consider a scenario with 10 deliveries and 4 drones to show some of the proposed applications of the model. Given a feasible solution to the problem, the authors show how to find plans that are robust to the given wind conditions, how to identify inaccessible areas due to wind conditions, and how to plan emergency returns of UAVs in case real wind conditions are worse than the forecast.…”