Design of Spatiotemporal Collaborative Trajectory Prediction Method for Quadcopter UAVs Based on Transformer Model
In order to reduce the risk of accidents and predict and respond to abnormal situations in the complex and changeful flight environments faced by unmanned aerial vehicles(UAVs)during mission execution,a spatiotemporal collaborative trajectory prediction method based on Transformer model for quadcopter UAVs is studied.Collect the original trajectory of the quadcopter drone,implement the outlier removal and missing point interpolation processing to optimize and clean up the original trajectory data for subsequent trajectory prediction.Convolutional neural networks are used to conduct the feature extraction,the low dimensional learning data are obtained through encoding and decoding processes,and deep learning and representation learning methods are com-bined to complete the data dimensionality reduction.Based on the Transformer model,the UAV spatiotemporal cooperative trajectory is precisely predicted.By removing the outliers and interpolation,the collected raw trajectory data of quadcopter UAVs is prepro-cessed to improve the quality and completeness of the data.The experimental test results show that although the prediction results of the design method have a slight deviation from the actual coordinate points,the overall results are within an acceptable range.The mean square error of all data in the validation set is only 0.32 m when the number of data is 300,and the goodness of fit index is clo-sest to 1,indicating its good trajectory prediction ability.This method can better optimize the trajectory planning of drones,achieve spatiotemporal collaborative trajectory planning among multiple drones,avoid collisions and conflicts,and optimize flight efficiency.
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