Artificial Intelligence Based Short-Term Motions Forecasting for Autonomous Marine Vehicles Control

The development of fast and accurate intelligent vessel control systems is a necessary milestone on the path toward operating autonomous marine vehicles effectively in harsh environments and complex mission settings. One of the main problems of existing control systems is the disparity between the forecasted behavior and how the vessel actually responds to its environment. This disparity can be partly attributed to the dependency on physics-based methods to model the response of the vessel and the fact that accurate high-fidelity physical models are too computationally expensive to be utilized in real time. One promising solution to this problem is to integrate the dynamic environmental conditions such as sea states, winds, and currents to model the response of the vessel. However, this may not be feasible with the existing physics-based controller strategies due to the high computational requirements. Instead, we propose using Artificial Intelligence (AI) based methods, which leverage Data Mining and Machine Learning, to enable fast and accurate short-term motions forecasting for autonomous marine vehicles. The AI-based approach is extremely time-aware in the forecasting phase since it does not rely on solving the physics behind the phenomenon but rather learns a phenomenon from historical examples linking the vessel’s motions to a holistic view of its real-time environment. To test our hypothesis, we will develop state-of-the-art AI-based models for the short-term motions forecasting of the roll and trim of a twin-engine commercial vessel using real-world operational data and leverage statistical methods to validate our results.