Probability machine-learning-based communication and operation optimization for cloud-based UAVs

Abstract

This paper proposes a smart machine-learning-based unmanned aerial vehicle (UAV) control system that optimizes UAV operations in real time. The purpose of this system is to increase the efficiency of UAVs that need to operate with limited resources. This can be accomplished by allowing the UAVs in flight to identify their current state and respond appropriately. The proposed system, which is developed based on “cloud robotics,” benefits from the powerful computational capabilities of cloud computing and can therefore calculate many types of information received from various sensors in real time to maximize the performance of the UAV control system. The system learn about normal situations when creating models. That is, preprocessing data that is correlated with a particular situation and modeling it with a “multivariate Gaussian distribution.” Once the model is created, the UAV can be used to analyze the current situation in real time during flight. Of course, it is possible to recognize the situation based on the traditional RULE or the latest LSTM. However, this is not an appropriate solution for UAV situations where irregularities are severe and unpredictable. In this paper, we succeeded in recognizing the UAV flight status in real time by the proposed method and succeeded in optimizing it by adjusting communication cycle based on a recognized situation. Based on the results of this study, we expect to be able to stabilize and optimize systems that are highly irregular and unpredictable. In other words, this system will be extended to learn about various situations and create a model. A reliable and efficient smart system can be designed by judging the situation comprehensively.