Dynamic value iteration networks for the planning of rapidly changingUAVswarms

Wei LI, Bowei YANG, Guanghua SONG, Xiaohong JIANG

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Front. Inform. Technol. Electron. Eng All Journals
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Front. Inform. Technol. Electron. Eng ›› 2021, Vol. 22 ›› Issue (5) : 687-696. DOI: 10.1631/FITEE.1900712
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Dynamic value iteration networks for the planning of rapidly changingUAVswarms

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Abstract

In an unmanned aerial vehicle ad-hoc network (UANET), sparse and rapidly mobile unmanned aerial vehicles (UAVs)/nodes can dynamically change the UANET topology. This may lead to UANET service performance issues. In this study, for planning rapidly changing UAV swarms, we propose a dynamic value iteration network (DVIN) model trained using the episodic Q-learning method with the connection information of UANETs to generate a state value spread function, which enables UAVs/nodes to adapt to novel physical locations. We then evaluate the performance of the DVIN model and compare it with the non-dominated sorting genetic algorithm II and the exhaustive method. Simulation results demonstrate that the proposed model significantly reduces the decisionmaking time for UAV/node path planning with a high average success rate.

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Dynamic value iteration networks / Episodic Q-learning / Unmanned aerial vehicle (UAV) ad-hoc network / Non-dominated sorting genetic algorithm II (NSGA-II) / Path planning

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Wei LI, Bowei YANG, Guanghua SONG, Xiaohong JIANG. Dynamic value iteration networks for the planning of rapidly changingUAVswarms. Front. Inform. Technol. Electron. Eng, 2021, 22(5): 687‒696 https://doi.org/10.1631/FITEE.1900712
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2021 Zhejiang University Press
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