KMOU Researchers Unveil Innovative Autonomous Ship Navigation Method

A new study has focused on assessing the path-following performance of maritime autonomous surface ships (MASS) under adverse weather conditions in order to reduce greenhouse gas emissions. Traditional methods for analyzing the path-following performance of autonomous ships have been found to lead to inaccurate predictions. To combat this, researchers have investigated the path-following performance of MASS using a free-running computational fluid dynamics model, which can lead to more accurate assessment and therefore safer autonomous navigation.

The ability of MASS to follow a pre-determined path at sea is essential for safe navigation, and any deviation from this path due to adverse weather conditions poses serious risks. Current methods for assessing path-following performance rely on simplified mathematical ship models, which are unable to capture the complicated interactions between ship components, leading to inaccurate estimates. The recent study employed a CFD model to assess the path-following performance of MASS under adverse weather conditions, revealing that the ship experienced oscillatory deviations in all cases. The findings of the study could contribute to enhancing the safety of autonomous marine navigation and offer low-cost alternatives to model-scale free-running experiments or full-scale sea trials.

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