Dynamic value iteration networks for the planning of rapidly changingUAVswarms

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 decisionmaking time for UAV/node path planning with a high average success rate.

Keywords

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

RIGHTS & PERMISSIONS

2021 Zhejiang University Press