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Item type:Publication, Access status: with files , Practical method for evaluating wind influence on autonomous ship operations (2nd report)Recently, a considerable number of research and development projects have focused on automatic vessels. A highly realistic simulator is needed to validate control algorithms for autonomous vessels. For instance, when considering the automatic berthing/unberthing of a vessel, the effect of wind in such low-speed operations cannot be ignored because of the low rudder performance during slow harbor maneuvers. Therefore, a simulator used to validate an automatic berthing/unberthing control algorithm should be able to reproduce the time histories of wind speed and wind direction realistically. Therefore, in our first report on this topic, to obtain the wind speed distribution, we proposed a simple algorithm to generate the time series and distribution of wind speed only from the mean wind speed. However, for wind direction, the spectral distribution could not be determined based on our literature surveys, and hence, a simple method for estimating the coefficients of the stochastic differential equation (SDE) could not be proposed. In this study, we propose a new methodology for generating the time history of wind direction based on the results of Kuwajima et al.’s work. They proposed a regression equation of the standard deviation of wind direction variation for the mean wind speed. In this study, we assumed that the wind direction distribution can be represented by a linear filter as in our previous paper, and its coefficients are derived from Kuwajima’s proposed equation. Then, as in the previous report, the time series of wind speed and wind direction can be calculated easily by analytically solving the one-dimensional SDE. The joint probability density functions of wind speed and wind direction obtained by computing them independently agree well with the measurement results.Publicationtype: Journal Article34 22 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A study on the implementation of nonlinear Kalman filter applying MMG modelMany technologies need to be established to realize autonomous ships. In particular, accurate state estimation in real time is one of the most important technologies. In the ship and ocean engineering fields, there have been many studies on state estimation using nonlinear Kalman filters. Several methods have been proposed for nonlinear Kalman filters. However, there is insufficient verification on the selection of which filter should be applied among them. Therefore, this study aims to validate the filter selection to provide a guideline for filter selection. The effects of modeling error, observation noise, and type of maneuvers on the estimation accuracy of the unscented Kalman filter (UKF) and ensemble Kalman filter (EnKF) used in this study were investigated. In addition, it was verified whether filtering could be performed in real time. The results show that modeling error significantly impacts the estimation accuracy of the UKF and EnKF. However, the observation noise and types of maneuvers did not have an impact like the modeling error. Thus, we obtained the guideline that UKF and EnKF should be used differently depending on the required computation time. We also obtained that keeping the modeling error sufficiently small is essential to improving the estimation accuracy.Publicationtype: Journal Article32 55 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Practical method for evaluating wind influence on autonomous ship operationsThe autonomous operations of marine vehicles have recently attracted significant attention. In particular, automation of harbor operations, such as autonomous docking or berthing, is a challenging target. In low-speed harbor operations, the external disturbance of wind is a non-negligible factor. Therefore, the stochastic behavior of gusty wind should be considered in numerical simulations. This study presents a practical computational scheme for generating random wind velocity fields. In the proposed method, the von Kármán’s spectrum [1] and Hino’s wind speed spectrum [2] were fitted by a degree of freedom (1DoF) filter driven by the Wiener process. The corresponding Itô’s equation had the form of the Ornstein–Uhlenbeck process. By employing the analytical solution of the Ornstein–Uhlenbeck process, the wind process was successfully generated without time series repetition. Furthermore, the wind speed and direction processes were also generated from the measured drift and diffusion terms. The spectra of these numerical results are consistent with the observed spectra.Publicationtype: Journal Article61