Flight Trajectory Prediction and Analysis of Power Grid Unmanned Aerial Vehicles Using Variational Autoencoders

With the increasing popularity of power grid drones in power inspection, how to improve the accuracy of flight trajectory prediction has become a key challenge. Traditional prediction methods have problems with insufficient accuracy and limited generalization ability when dealing with complex and high-dimensional flight trajectory data. This paper introduces variational autoencoder (VAE) as a new machine learning algorithm for efficient prediction and analysis of the flight trajectory of power grid drones. VAE encodes and decodes flight trajectory data, learns its potential implicit representation, and thus generates more accurate future flight trajectory predictions. Experimental results show that the accuracy of flight trajectory prediction is increased to $\mathbf{9 8 \%}$ after using VAE. This study verifies the effectiveness of the VAE algorithm in power grid drone flight trajectory prediction, reveals its potential application in power grid operation management, and provides technical support and theoretical reference for improving power grid management efficiency and safety.