Frontiers of Information Technology & Electronic Engineering
>> 2021,
Volume 22,
Issue 5
doi:
10.1631/FITEE.1900712
Dynamic value iteration networks for the planning of rapidly changing UAV swarms
Affiliation(s): School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China; School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; less
Received: 2019-12-19
Accepted: 2021-05-17
Available online: 2021-05-17
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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 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 with a high average success rate.
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