Dynamic value iteration networks for the planning of rapidly changing UAV swarms
FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING(2021)
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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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
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