الگوهای تاخیر ارتباطی یادگیری برای شبکه های کنترل شده از راه دور توسط هواپیماهای بدون سرنشین

This paper deals with collaborative unmanned aerial vehicles (UAVs) that are remotely controlled from a cloud server. The main contribution is to apply machine learning technique to find a pattern of network-induced effects on maneuvers of UAVs, in order to compensate for time delays and packet losses in remote communication. As machine learning technique, a Gaussian process (GP) based approach is employed due to its computational simplicity and flexibility in modelling complex expressions using a small number of parameters. We combine a deterministic compensation for an enhanced GP model to overcome a problem of the lack of training data at the beginning of training phase. This is done by defining training data input as a set of delayed observation and the deterministic compensation term, and by training the GP on residual between the true state and the input set. The proposed algorithm is evaluated to collaborative trajectory tracking of two UAVs. Simulations are performed for various delays and tracking scenarios. It is shown that the better tracking results are achieved compared to a conventional linear compensation algorithm.