Abstract
The significance of unmanned aerial vehicles (UAVs) for delivery services is increasing nowadays. Due to the energy capacity limitations, long-distance distribution is still a challenging problem in the UAV logistics market. In this paper, allowing UAVs to be charged in UAV stations, we studied the long-distance delivery of single UAV. We planed the route in advance with the cloud computing platform and send it to the designated UAV. First, an optimization algorithm is proposed based on the minimum cost maximum flow theory, which divides locations and UAV stations into several takeoff UAV station-locations-landing UAV station (SLS) sets. Then a sequence of SLSs is determined by comparing the total energy consumption to minimize the consumption of the UAV under the energy capacity limitation. Finally, experiments verify the effectiveness of the proposed methods.
This work was supported in part by National Natural Science Foundation of China (No. 61802181, No. 61701231), and the Foundation of Key Laboratory of Safety-Critical Software (Nanjing University of Aeronautics and Astronautics), Ministry of Industry and Information Technology (No. NJ2020022).
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Wang, R., Zhai, X.B., Zhao, Y., Zhao, X. (2021). Delivery Optimization for Unmanned Aerial Vehicles Based on Minimum Cost Maximum Flow with Limited Battery Capacity. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12939. Springer, Cham. https://doi.org/10.1007/978-3-030-86137-7_9
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DOI: https://doi.org/10.1007/978-3-030-86137-7_9
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