Abstract
A major challenge to wide application of small size unmanned aerial vehicles (UAVs) is the limited working time. For recharging UAVs, ground-station based schemes had been proposed, for which contact charging by magnetic coupling and contactless charging by laser beam can be used. However, UAVs have to interrupt ongoing missions and cost extra time and energy on recharging. In this work, with the aim of charging UAVs without interrupting the mission, we propose the novel concept of charging UAVs aerially via wireless power transmission (WPT). In this case, the mission UAVs (MUAVs) can be recharged by the charging UAV (CUAV) while on the fly. Firstly, the feasibility of aerially wireless charging for small UAVs is verified. Then we consider the practical application of multiple MUAVs for collecting data from several points of interest (PoIs), where the MUAVs will be recharged by the CUAV. Accordingly, the issue of scheduling the CUAV’s flying path and charging process to minimize the mission time arises. To this end, deep reinforcement learning based algorithms for scheduling CUAV recharging MUAVs is proposed. The CUAV explores and optimizes the scheduling strategies, thereby improving the working efficiency. Extensive evaluations and comparisons show the effectiveness of the proposed scheme.
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Notes
- 1.
Note that the path planning and scheduling for MUAVs to collect data is not considered in this work.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (No. 62071230 and No. 61972199).
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Yang, J., Zhu, K., Zhu, X., Wang, J. (2021). Learning-Based Aerial Charging Scheduling for UAV-Based Data Collection. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12938. Springer, Cham. https://doi.org/10.1007/978-3-030-86130-8_47
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DOI: https://doi.org/10.1007/978-3-030-86130-8_47
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