Motion performance prediction of underwater gliders based on deep learning and image modeling

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With the industrialization development and pedigree application, the underwater gliders are facing more extensive motion modes and application scenarios. However, it is complicated to establish a specific relationship between the explicit motion property and the implicit design parameters. This study proposes an accurate and rapid performance prediction method for underwater gliders based on deep learning and image modeling. First of all, a parametric model is established to generate large quantities of data about design parameters and performance indexes. Then, an image modeling method is employed to transform the numerical data of the glider body into structure images. Finally, a hybrid convolutional neural network-multilayer perceptron (CNN-MLP) model is proposed, and the structure images and other numerical data are input into the model to predict the total gliding range of the gliders. The results show that the developed hybrid CNN-MLP model achieves excellent prediction ability. Sea trial data further demonstrate the effectiveness of the proposed model. Thus, the proposed architecture provides a promising method for predicting underwater glider performance with multidimensional data and can be further applied to other types of gliders. Our research also provides a valuable reference for intelligent design and optimization iteration of other underwater equipment.