Full Text:   <2878>

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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: 4710

Citations:  Bibtex RefMan EndNote GB/T7714


Wei Li


Bowei Yang


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Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.5 P.687-696


Dynamic value iteration networks for the planning of rapidly changing UAV swarms

Author(s):  Wei Li, Bowei Yang, Guanghua Song, Xiaohong Jiang

Affiliation(s):  School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   li2ui2@zju.edu.cn, boweiy@zju.edu.cn, ghsong@zju.edu.cn, jiangxh@zju.edu.cn

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

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.

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T1 - Dynamic value iteration networks for the planning of rapidly changing UAV swarms
A1 - Wei Li
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A1 - Xiaohong Jiang
J0 - Frontiers of Information Technology & Electronic Engineering
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DOI - 10.1631/FITEE.1900712

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.





Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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