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.
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The authors would like to thank BAE Systems for providing funding and support for the project.
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© 2016 Springer International Publishing Switzerland
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Khan, A.A., Marion, K.E., Bil, C., Simic, M. (2016). Motion Prediction for Ship-Based Autonomous Air Vehicle Operations. In: Pietro, G., Gallo, L., Howlett, R., Jain, L. (eds) Intelligent Interactive Multimedia Systems and Services 2016. Smart Innovation, Systems and Technologies, vol 55. Springer, Cham. https://doi.org/10.1007/978-3-319-39345-2_28
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DOI: https://doi.org/10.1007/978-3-319-39345-2_28
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