Full Text:   <1053>

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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: 2417

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Wei Li

https://orcid.org/0000-0001-5102-8597

Bowei Yang

https://orcid.org/0000-0001-8581-3817

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

http://doi.org/10.1631/FITEE.1900712


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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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.

用于规划快速变化无人机群的动态值迭代网络

李伟1,杨波威1,宋广华1,姜晓红2
1浙江大学航空航天学院,中国杭州市,310027
2浙江大学计算机科学与技术学院,中国杭州市,310027

摘要:在无人机自组网(UANET)中,稀疏且高速移动的无人机节点会动态改变无人机自组网的拓扑结构,这可能会导致无人机自组网服务性能问题。为规划快速变化的无人机群,本文提出一种动态值迭代网络(DVIN)模型,该模型利用无人机自组网的连接信息,采用场景式Q学习方法训练,生成状态值传播函数,使无人机节点能够自适应调节至新的物理位置。然后,评估了动态值迭代网络模型的性能,并将其与非支配排序遗传算法NSGA-II和穷举法比较。仿真结果表明,动态值迭代网络模型显著缩短了无人机节点路径规划的决策时间,且平均成功率更高。

关键词:动态值迭代网络;场景式Q学习;无人机自组网;NSGA-II;路径规划

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