A learning-based approach to surface vehicle dynamics modeling for robust multistep prediction

Determining the dynamics of surface vehicles and marine robots is important for developing marine autopilot and autonomous navigation systems. However, this often requires extensive experimental data and intense effort because they are highly nonlinear and involve various uncertainties in real operating conditions. Herein, we propose an efficient data-driven approach for analyzing and predicting the motion of a surface vehicle in a real environment based on deep learning techniques. The proposed multistep model is robust to measurement uncertainty and overcomes compounding errors by eliminating the correlation between the prediction results. Additionally, latent state representation and mixup augmentation are introduced to make the model more consistent and accurate. The performance analysis reveals that the proposed method outperforms conventional methods and is robust against environmental disturbances.