Author:
Tian Zijing,Haas Zygmunt J.,Shinde Shatavari
Abstract
As interest grows in unmanned aerial vehicle (UAV) systems, UAVs have been proposed to take on increasingly more tasks that were previously assigned to humans. One such task is the delivery of goods within urban cities using UAVs, which would otherwise be delivered by terrestrial means. However, the limited endurance of UAVs due to limited onboard energy storage makes it challenging to practically employ UAV technology for deliveries across long routes. Furthermore, the relatively high cost of building UAV charging stations prevents the dense deployment of charging facilities. Solar-powered UAVs can ease this problem, as they do not require charging stations and can harvest solar power in the daytime. This paper introduces a solar-powered UAV goods delivery system to plan delivery missions with solar-powered UAVs (SPUs). In this study, when the SPUs run out of power, they charge themselves on landing places provided by customers instead of charging stations. Some advanced path planning algorithms are proposed to minimize the overall mission time in the statically charging efficiency environment. We further consider routing in the dynamically charging efficiency environment and propose some mission arrangement protocols to manage different missions in the system. The simulation results demonstrate that the algorithms proposed in our work perform significantly better than existing UAV path planning algorithms in solar-powered UAV systems.
Funder
U.S. National Science Foundation
Subject
Artificial Intelligence,Computer Science Applications,Aerospace Engineering,Information Systems,Control and Systems Engineering
Reference29 articles.
1. Chawla, L. (2021). UAV Delivery System. Int. J. Sci. Res. Eng. Manag. (IJSREM), 5.
2. DNCS: New UAV navigation with considering the no-fly zone and efficient selection of the charging station;Ain Shams Eng. J.,2021
3. CBDN: Cloud-based drone navigation for efficient battery charging in drone networks;IEEE,2018
4. Wubben, J., Fabra, F., Calafate, C.T., Krzeszowski, T., Marquez-Barja, J.M., Cano, J.-C., and Manzoni, P. (2019). Accurate landing of unmanned aerial vehicles using ground pattern recognition. Electronics, 8.
5. Prediction of hourly solar radiation using a novel hybrid model of ARMA and TDNN;Sol. Energy,2011
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