Multi-UAV Data Collection with In-Flight Wireless Power Transfer

The development of unmanned aerial vehicles (UAVs) faces a critical issue of limited energy. When UAVs hover over designated areas and collect sensing data, it is imperative to shorten flight trajectories, prolong flight times and minimize energy consumption. This paper proposes a multi-UAV cooperation scheme. There are two types of UAV: multiple mission UAVs of collecting data along predetermined routes and a supply UAV of providing energy to those mission UAVs via wireless power transfer. This scheme first considers the Traveling Salesman Problem when mission UAVs visit many points of interest to collect data. Then, it employs the Simulated Annealing algorithm to solve it. In addition, this scheme uses the Genetic Algorithm to determine a route for the supply UAV to charge the mission UAVs sequentially with a sufficient charging time. Simulation results show the efficacy of the proposed scheme in terms of maximum service time.

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Publication Details

Published
2025-08-20
DOI
https://doi.org/10.1109/apwcs67981.2025.11151969
Primary Topic
UAV Applications and Optimization
Type
article
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article

Multi-UAV Data Collection with In-Flight Wireless Power Transfer

Sheng‐Zhi Huang, Chih‐Lin Hu, Kouji Hirata, Uomi Naoya
UAV Applications and Optimization
article

Multi-UAV Data Collection with In-Flight Wireless Power Transfer

Sheng‐Zhi Huang, Chih‐Lin Hu, Kouji Hirata, Uomi Naoya
article en

Abstract

The development of unmanned aerial vehicles (UAVs) faces a critical issue of limited energy. When UAVs hover over designated areas and collect sensing data, it is imperative to shorten flight trajectories, prolong flight times and minimize energy consumption. This paper proposes a multi-UAV cooperation scheme. There are two types of UAV: multiple mission UAVs of collecting data along predetermined routes and a supply UAV of providing energy to those mission UAVs via wireless power transfer. This scheme first considers the Traveling Salesman Problem when mission UAVs visit many points of interest to collect data. Then, it employs the Simulated Annealing algorithm to solve it. In addition, this scheme uses the Genetic Algorithm to determine a route for the supply UAV to charge the mission UAVs sequentially with a sufficient charging time. Simulation results show the efficacy of the proposed scheme in terms of maximum service time.

Tamkang University (TW), National Central University (TW), Kansai University (JP)
Openalex Percentile: Top 67%
UAV Applications and Optimization
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