The cooperation of tugboats is necessary for the entry and berthing of large boats inside the area of port. Tugboats are effective for assisting in port operations due to their small size and adaptable handling. However, because of the peculiarities of tugboat operation, tugboat motion control has become a popular topic for study. The initial phase in motion control, which can efficiently increase tugboat operation management, is the motion prediction of tugs. Moreover, if the trajectory of tugboats can be precisely predicted, it will be possible to anticipate the existence of unjustified towing operations or poor inter-tug cooperation, making the operating environment in the port’s internal waters safer. The widely-used motion prediction methods, such as long short-term memory techniques and other neural networks, are effective in normal ship manoeuvre. As a widely used prediction method, the hidden Markov model is rarely used in motion prediction. This paper uses the hidden Markov model to learn the behavior pattern of the tugboat in the port from the historical traffic flow. Firstly, for the data generated by simulation, the hidden Markov model is used to first calculate the state transition probability of the tugboat’s rudder angle, heading and speed. Secondly using the state of the previous moment and the calculated state transition probability to predict the state of the tugboat at the future moment. Thirdly, predicting the possible path points of the tugboat in the future according to the predicted tugboat state and predict the motion trajectory of tugboat, and finally analyze the error between the predicted trajectory and the actual simulated trajectory to verify the effectiveness of the method.


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    Titel :

    Motion Prediction of Tugboats Using Hidden Markov Model


    Beteiligte:
    Zhang, Zijian (Autor:in) / Zhao, Jie (Autor:in) / Wang, Tengfei (Autor:in) / Ma, Feng (Autor:in) / Diaconeasa, Mihai A. (Autor:in)


    Erscheinungsdatum :

    04.08.2023


    Format / Umfang :

    1100387 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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