Deep learning method for 3-DOF motion prediction of unmanned surface vehicles based on real sea maneuverability test

Motion prediction in a real sea area is a relatively difficult problem due to the lack of reliable measurement of the environment and an accurate model of ship motion under external disturbance. A deep learning method is applied in this study for the modeling of 3-DOF motion prediction under a real sea environment with the influence of currents and waves. The network with angle feature and the network with trigonometric feature are validated for the identification of the influence of the environment regarding winds, waves, and currents, in which the selection of activation function is further discussed. The developed method is validated by learning the data generated through the turning test of JARI-USV in a real sea area in Rizhao, Shandong. Analysis on the turning radius and the drift distance of the integrated track shows maximum relative errors of 5.44% and 9.09%, and the derived drift angle has a maximum absolute error of 4.84. The prediction results verify the ability of the designed network to capture the environment influence on ship motion, as well as the turning feature of the ship itself from the maneuvering test data in a real sea trial.