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Erschienen in:
2016 | OriginalPaper | Buchkapitel
Motion Prediction for Ship-Based Autonomous Air Vehicle Operations
verfasst von : Ameer A Khan, Kaye E Marion, Cees Bil, Milan Simic
Erschienen in: Intelligent Interactive Multimedia Systems and Services 2016
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Abstract
A ship operating in an open sea environment undergoes stochastic motions which make deployment and landing of UAVs and other vehicles on a ship difficult and potentially dangerous. There is always a delay between the decision to commit and the moment of actual launch or recovery. This paper presents an artificial neural network trained using singular value decomposition, genetic algorithm and conjugate gradient method for the real time prediction of ship motions. These predictions assist in determining the best moment of commitment to launch or to recover. Predictions generated using these algorithms allow improvements in safety as well reducing the number of missed or aborted attempts. It is shown that the artificial neural network produces excellent predictions and is able to predict the ship motion satisfactorily for up to 7 s ahead.