Towards Real-World Validation of a Physics-Based Ship Motion Prediction Model
원문 초록
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.
함께 살펴볼 논문
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Interpretable Data-Driven Ship Dynamics Model: Enhancing Physics-Based Motion Prediction with Parameter Optimization
PriorMotion: Generative Class-Agnostic Motion Prediction with Raster-Vector Motion Field Priors
iMotion-LLM: Motion Prediction Instruction Tuning
GeoDistill: Geometry-Guided Self-Distillation for Weakly Supervised Cross-View Localization
Temporal Continual Learning with Prior Compensation for Human Motion Prediction
Stochastic Human Motion Prediction with Memory of Action Transition and Action Characteristic
토론
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