Abstract
Unmanned Aerial Vehicles (UAVs), also known as drones, are a class of aircraft without the presence of pilots on board. UAVs have the ability to reduce the time and cost of deliveries and to respond to emergency situations. Currently, UAVs are extensively used for data delivery and/or collection to/from dangerous or inaccessible sites. However, trajectory planning is one of the major UAV issues that needs to be solved. To address this question, we focus in this paper on determining the optimized routes to be followed by the drones for data pickup and delivery with a time window with an intermittent connectivity network, while also having the possibility to recharge the drones’ batteries on the way to their destinations. To do so, we formulated the problem as a multi-objective optimization problem, and we showed how to use the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve this problem. Several experiments were conducted to validate the proposed algorithm by considering different scenarios.
Highlights
We focus on the Multi-Objective Evolutionary Algorithm (MOEA) technique to solve the PDPTW using multiple Unmanned Aerial Vehicles (UAVs)
The Pickup and Delivery optimization Problem with a Time Window for UAVs (PDPTW-UAV) is an extended variant of the PDPTW originally designed for terrestrial vehicles
To better describe our optimization problem, we propose below the different terminologies used in the PDPTW-UAV
Summary
During natural disasters such as earthquakes, floods, landslides, or hurricanes, road and telecommunications infrastructures can suffer significant damage that renders them unusable. Communication and network connectivity may be lost, or roads may become impassable and prevent emergency vehicles from reaching damaged areas, which we call critical sites. These sites need the rapid deployment of a temporary solution to communicate with the other critical sites and with the emergency headquarters management center, known as the central entity. It illustrates a scenario in which several UAVs leave a truck, operating as a depot, to pick up data at given sites, deliver them to other sites while satisfying data latency, UAV load capacity, and UAV autonomy (i.e., UAV energy), and fly back to the depot
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