Optimal Charging Station Deployment for Drone-Assisted
This challenge is addressed through the placement of charging stations where drone batteries are recharged. As assignment issues have not yet received much attention in the literature, this
This challenge is addressed through the placement of charging stations where drone batteries are recharged. As assignment issues have not yet received much attention in the literature, this
In this article, a novel building-integrated photovoltaic (BIPV) structure is developed. The proposed system concentrates on wirelessly charging drones on the rooftop of the building
ct—Drones have become a new means for parcel delivery in recent years. As the flight distances of currently available commercial drones are usually limited due to the battery capac-ity
In this article, we study how to extend the drone flight time with charging stations and ensure multiple deliveries in a single mission. For multiple long-distance deliveries, optimization
In this article, we study how to extend the drone flight time with charging stations and ensure multiple deliveries in a single mission. For multiple long-distance deliveries, optimization
Unmanned aerial vehicle (UAV) cluster is increasingly used in the field of logistics. However, the efficiency of drone delivery is greatly affected by the limited cruising range. Optimal planning
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Upgrading these building envelopes by deploying building-integrated photovoltaics (BIPV) and allocating UAV recharging stations on their roofs would represent a dual green solution. The environmental benefits of reducing energy consumption in upgraded buildings are coupled with generating clean electricity required for the UAV charging functions.
Dang et al. (2021) propose a multi-criteria decision-making framework for island photovoltaic charging station site selection. While literature is abundant on ground vehicles and ships, UAVs have had less share of this focus. Compared to ground vehicles, the average UAV range is 3 km, which is significantly lower.
There is a literature gap in addressing the precise estimate of UAV operational energy based on real-life trajectories to inform charging station allocation. The present study builds on previous works to address the problem of charging stations allocation for an autonomous UAV parcel delivery system.
UAVs are assumed fully charged when they leave the charging station (SoC=100%). The UAV's flight range is estimated according to the UAV 3D minimal energy trajectory model. As the energy consumption rate varies for loaded and unloaded UAVs, two different flight scenarios are implemented.