A Physics-Data Co-Operative Ship Dynamic Model for a Docking Operation

The development of onboard sensors is bringing us to the next level of ship digitalization. Its ultimate goal is to ensure safe & efficient marine operation by ship intelligence. In particular, during a docking operation, situation awareness based on precise motion prediction is of great importance. Knowledge-based ship models, developed based on the understanding of ship dynamics and simplifications, have played an important role in ship intelligence. However, they do not fully handle highly nonlinear and complex ship dynamics in the docking operation beyond our explicit understanding. On the contrary, data-driven models deal with such non-linearity and complexity in a non-parametric manner, however, the maritime industry does not regard them as reliable and applicable models due to the lack of interpretability and the physics foundation. To alleviate this dilemma, this study proposes a physics-data co-operative ship dynamic model for the docking operation; a knowledge-based ship model serves as a physics foundation with supportive data-driven models compensating a single-step-ahead velocity prediction error made by a physics foundation model. Neural networks trained with onboard sensor data are employed in supportive data-driven models. In a case study, we conducted full-scale docking operations of a 28.9m-length research vessel Gunnerus. The results show that mean prediction error in a position at 30s future is reduced by 34.6% compared to that made solely by a physics foundation model. The present approach will be the first step in the development of high-fidelity and cost-efficient ship dynamic models, thus contributing to ship autonomy in the future.