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CLC number: TP183; TP393.1
On-line Access: 2021-05-17
Received: 2019-12-19
Revision Accepted: 2020-06-27
Crosschecked: 2020-10-20
Cited: 0
Clicked: 5308
Citations: Bibtex RefMan EndNote GB/T7714
Dynamic value iteration networks for the planning of rapidly changing UAV swarms
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Wei Li, Bowei Yang, Guanghua Song, Xiaohong Jiang. Dynamic value iteration networks for the planning of rapidly changing UAV swarms[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(5): 687-696.
@article{title="Dynamic value iteration networks for the planning of rapidly changing UAV swarms",
author="Wei Li, Bowei Yang, Guanghua Song, Xiaohong Jiang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="5",
pages="687-696",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900712"
}
%0 Journal Article
%T Dynamic value iteration networks for the planning of rapidly changing UAV swarms
%A Wei Li
%A Bowei Yang
%A Guanghua Song
%A Xiaohong Jiang
%J Frontiers of Information Technology & Electronic Engineering
%V 22
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%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900712
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T1 - Dynamic value iteration networks for the planning of rapidly changing UAV swarms
A1 - Wei Li
A1 - Bowei Yang
A1 - Guanghua Song
A1 - Xiaohong Jiang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 5
SP - 687
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%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1900712
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 decision-making time for UAV/node path planning with a high average success rate.
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