Dynamic value iteration networks for the planning of rapidly changingUAVswarms
Wei LI, Bowei YANG, Guanghua SONG, Xiaohong JIANG
Dynamic value iteration networks for the planning of rapidly changingUAVswarms
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.
Dynamic value iteration networks / Episodic Q-learning / Unmanned aerial vehicle (UAV) ad-hoc network / Non-dominated sorting genetic algorithm II (NSGA-II) / Path planning
/
〈 | 〉 |