Parameter fine-tuning method for MMG model using real-scale ship data

In this paper, a fine-tuning method of the parameters in the MMG model for the real-scale ship is proposed. In the proposed method, all of the arbitrarily indicated target parameters of the MMG model are tuned simultaneously in the framework of SI using time series data of real-sale ship maneuvering motion data to steadily improve the accuracy of the MMG model. Parameter tuning is formulated as a minimization problem of the deviation of the maneuvering motion simulated with given parameters and the real-scale ship trials, and the global solution is explored using CMA-ES. By constraining the exploration ranges to the neighborhood of the previously determined parameter values, the proposed method limits the output in a realistic range. The proposed method is applied to the tuning of 12 parameters for a container ship with five different widths of the exploration range. The results show that, in all cases, the accuracy of the maneuvering simulation is improved by applying the tuned parameters to the MMG model, and the validity of the proposed parameter fine-tuning method is confirmed.

Ship trajectory planning method for reproducing human operation at ports

Among ship maneuvers, berthing/unberthing maneuvers are one of the most challenging and stressful phases for captains. Concerning burden reduction on ship operators and preventing accidents, several researches have been conducted on trajectory planning to automate berthing/unberthing. However, few studies have aimed at assisting captains in berthing/unberthing. The trajectory to be presented to the captain should be a maneuver that reproduces human captain's control characteristics. The previously proposed methods cannot explicitly reflect the motion and navigation, which human captains pay particular attention to reduce the mental burden in the trajectory planning. Herein, mild constraints to the trajectory planning method are introduced. The constraints impose certain states (position, bow heading angle, ship speed, and yaw angular velocity), to be taken approximately at any given time. The introduction of this new constraint allows imposing careful trajectory planning (e.g., in-situ turns at zero speed or a pause for safety before going astern), as if performed by a human during berthing/unberthing. The algorithm proposed herein was used to optimize the berthing/unberthing trajectories for a large car ferry. The results show that this method can generate the quantitatively equivalent trajectory recorded in the actual berthing/unberthing maneuver performed by a human captain.