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

Please provide your name, email address and a comment

Your Name: Affili: E-Mail Address:

Comment (Please comment in English):


Verification Code(click to change a pic):