The National Korea Maritime & Ocean University (KMOU) has unveiled its new vessel control method that optimises autonomous ship navigation after researching the use of Model Predictive Control (MPC) in realistic conditions. 

Researchers at the university said that while the concept of using MPC for Maritime Autonomous Surface Ships (MASS), vessels that operate with little to no human intervention, had gained popularity, current systems using the tech failed to account for the external loads, such as waves and currents, faced by a ship while at sea. 

Dr Daejeong Kim, assistant professor at the KMOU’s Division of Navigation Convergence Studies, said the study addressed a “critical gap” in the use of autonomous ships, adding: “Our method enhances the efficiency and safety of autonomous vessel operations and potentially reduces shipping costs and carbon emissions, benefiting various sectors of the economy.” 

A graphic outlining how KMOU's new model works
A graphic outlining how the KMOU model works. Credit: National Korea Maritime & Ocean University

The mathematical model developed by KMOU’s researchers has been outlined in their research paper published in Ocean Engineering as well as the novel time optimisation model that turns the idea into a spatial formulation ready for real world use. 

The method was tested by inputting various models into a MPC controller and simulating a real ship model in the sea across three control strategies, finding that including wave load data in the planning and tracking stage of the model was the most reliable use of the tech. 

KMOU’s model is the latest bit of research into the use of MASS, which have been subject to a range of studies into their capabilities and regulations on their use, with the IMO working on introducing a MASS code covering issues such as having multiple MASS under one master.

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