Ship Motion Prediction for Maritime Flight Operations

Yang, Xilin; Pota, Hemanshu R.; Garratt, Matthew A.; Ugrinovskii, Valery

doi:10.3182/20080706-5-kr-1001.02100

Cited by 10 publications

(4 citation statements)

References 14 publications

(14 reference statements)

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“…In Yang's work, a recursive least squares (RLS) method was employed to make 5 and 10 s predictions using pitch data [12]. The RLS method had an RMSE of 0.06548 deg for the 5 s prediction and 0.1339 deg for the 10 s prediction.…”

Section: Rmse Inmentioning

confidence: 99%

“…In the past, a great deal of emphasis was placed on using time-series analysis to predict ship motion. Such methods include autoregressive (AR) [7] [8] [9] and moving average autoregressive (ARMAX) [8], as well as Kalman filters [4] [9] [10] [11] [12], Wiener filters [7] [13], and the Volterra model [10]. Time-series analyses are still used commonly today; however, most modern approaches utilize neural networks to make predictions.…”

Section: Introductionmentioning

confidence: 99%

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A Sine-Summation Algorithm for the Prediction of Ship Deck Motion

Bolander

Hunsaker

2018

OCEANS 2018 MTS/IEEE Charleston

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Landing a fixed-wing aircraft on a moving aircraft carrier is a risky and inefficient process. Having an accurate prediction of ship deck motion decreases the risk posed to both the pilot and the aircraft and increases the efficiency of landing maneuvers. The present work proposes the use of a sine-summation algorithm to predict future ship motion. The algorithm decomposes recorded ship acceleration data into its characteristic harmonic parameters using a fast Fourier transform. The harmonic parameters are then used in a summation of sine waves to create a fit for the acceleration data, which is projected into future time intervals to predict ship motion. An aircraft carrier can supply the prediction made by the algorithm to an autopilot, which then decides to land or make another attempt. Included in this work is a brief overview of ship motion with six degrees of freedom and a description of the method. The results generated by the algorithm are presented for a specific ship motion dataset to provide a point of comparison between the proposed method and other common methods used. The proposed method appears to be accurate in comparison to similar prediction methods, while reducing the computational cost required to make a prediction.

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Section: Rmse Inmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Sine-Summation Algorithm for the Prediction of Ship Deck Motion

Bolander

Hunsaker

2018

OCEANS 2018 MTS/IEEE Charleston

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“…There are some papers in the literature considering the prediction of ship motion. Yang et al [13], [14] discuss the problem of landing a helicopter on a ship in high sea and predict the ship motion by fitting the ship model to the measured data using recursive least squares. Khan et al [15] use artificial neural networks to solve the same problem.…”

Section: Related Researchmentioning

confidence: 99%

On the influence of ship motion prediction accuracy on motion planning and control of robotic manipulators on seaborne platforms

From

Gravdahl

Abbeel

2010

2010 IEEE International Conference on Robotics and Automation

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Abstract-Robotic manipulators on non-inertial platforms, such as ships, have to endure large inertial forces due to the non-inertial motion of the platform. When the non-inertial platform's motion is known, motion planning and control algorithms can eliminate these perturbations-in fact, in some situations the motion planning algorithms can even leverage the inertial forces to more cheaply move to a target point. However, for many non-inertial platforms, the motion is unknown.In this paper we investigate how prediction errors and the choice of the prediction horizon affect the motion planning and control of robots mounted on a non-inertial base with a particular focus on seaborne platforms. We study the following three aspects: (i) We study prediction of ship motion and how prediction errors affect the motion planning and control of the manipulator. (ii) We evaluate the relationship between prediction accuracy and control. In particular, we study what prediction horizon length is useful for motion planning and control. We also consider how uncertainties in the ship motion predictions map to uncertainties in the future state of the robot and how to include the variance in the cost function to increase the optimal horizon length. (iii) Finally, we study a receding horizon approach, which re-solves the optimal control problem on-line over a horizon as determined to be meaningful from (ii). Several simulations are presented and, to our knowledge, for the first time experiments of ship-manipulator systems based on real ship motion data are presented.

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“…For example, the spectral estimation method can analyse and forecast the ship motion by using the perspective of energy superposition 5 , which is too complex and abstract for using. The time-series method forecasts the ship motion by using the linear autoregressive sequence (AR) or autoregressive moving average model (ARMA) [6][7][8] , which needs complex models and the forecasting results are not very good. The Kalman filter method considers the wave excitation as white noise, and the accuracy of the prediction results will be reduced with time growing 9,10 .…”

Section: Introductionmentioning

confidence: 99%

A ship-motion prediction algorithm based on modified covariance method and neural networks

Ding¹

2022

International Conference on Computer Application and Information Security (ICCAIS 2021)

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In order to provide accurate ship-motion prediction for safe seaboard operations, this paper presents a new ship-motion prediction algorithm based on modified covariance (MCOV) method and neural networks. This algorithm firstly uses the MCOV method to analysis spectrums of the ship motion. And then, major spectrums of the ship motion are used to find the ship motion model by using the Neural Network (NN). Simulation results show that this method can present a confidence performs on ship-motion prediction.

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Ship Motion Prediction for Maritime Flight Operations

Cited by 10 publications

References 14 publications

A Sine-Summation Algorithm for the Prediction of Ship Deck Motion

A Sine-Summation Algorithm for the Prediction of Ship Deck Motion

On the influence of ship motion prediction accuracy on motion planning and control of robotic manipulators on seaborne platforms

A ship-motion prediction algorithm based on modified covariance method and neural networks

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