Interpretable Data-Driven Ship Dynamics Model: Enhancing Physics-Based Motion Prediction with Parameter Optimization

The deployment of autonomous navigation systems on ships necessitates accurate motion prediction models tailored to individual vessels. Traditional physics-based models, while grounded in hydrodynamic principles, often fail to account for ship-specific behaviors under real-world conditions. Conversely, purely data-driven models offer specificity but lack interpretability and robustness in edge cases. This study proposes a data-driven physics-based model that integrates physics-based equations with data-driven parameter optimization, leveraging the strengths of both approaches to ensure interpretability and adaptability. The model incorporates physics-based components such as 3-DoF dynamics, rudder, and propeller forces, while parameters such as resistance curve and rudder coefficients are optimized using synthetic data. By embedding domain knowledge into the parameter optimization process, the fitted model maintains physical consistency. Validation of the approach is realized with two container ships by comparing, both qualitatively and quantitatively, predictions against ground-truth trajectories. The results demonstrate significant improvements, in predictive accuracy and reliability, of the data-driven physics-based models over baseline physics-based models tuned with traditional marine engineering practices. The fitted models capture ship-specific behaviors in diverse conditions with their predictions being, 51.6% (ship A) and 57.8% (ship B) more accurate, 72.36% (ship A) and 89.67% (ship B) more consistent.

Towards Real-World Validation of a Physics-Based Ship Motion Prediction Model

The maritime industry aims towards a sustainable future, which requires significant improvements in operational efficiency. Current approaches focus on minimising fuel consumption and emissions through greater autonomy. Efficient and safe autonomous navigation requires high-fidelity ship motion models applicable to real-world conditions. Although physics-based ship motion models can predict ships' motion with sub-second resolution, their validation in real-world conditions is rarely found in the literature. This study presents a physics-based 3D dynamics motion model that is tailored to a container-ship, and compares its predictions against real-world voyages. The model integrates vessel motion over time and accounts for its hydrodynamic behavior under different environmental conditions. The model's predictions are evaluated against real vessel data both visually and using multiple distance measures. Both methodologies demonstrate that the model's predictions align closely with the real-world trajectories of the container-ship.