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Frontiers of Information Technology & Electronic Engineering
ISSN 2095-9184 (print), ISSN 2095-9230 (online)
2021 Vol.22 No.5 P.687-696
Dynamic value iteration networks for the planning of rapidly changing UAV swarms
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.
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
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。
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DOI:
10.1631/FITEE.1900712
CLC number:
TP183; TP393.1
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On-line Access:
2021-05-17
Received:
2019-12-19
Revision Accepted:
2020-06-27
Crosschecked:
2020-10-20