Neural Networks for high accuracy short term ship motion predictions with applications to autonomous UAVs

• Computationally efficient models which can be used by UAV mounted computers • Comparison of common Neural Network model and new model • Robust Neural Network models that can now be used without noise filtering • Large augmented dataset to reflect real data measured from UAV An Autonomous Uncrewed Aerial Vehicle (UAV) attempting to perform a vertical landing on a moving ship’s deck must be capable of predicting the ship’s motion in order determine the most opportune landing time. If the UAV is acting independent of the ship additional constraints are introduced; computation resources are limited to what can be mounted on the drone and the UAV must predict motion from noisy UAV-mounted sensors. The work presented proposes a Gated Recurrent Unit based Autoencoder (GRU-A) Neural Network (NN) model for predicting future ship motion with the aforementioned constraints. The GRU-A model is compared to a more typical Multi-Layered Perceptron, Nonlinear Auto-Regressive (MLP-NAR) NN model. Both NN models are tested for their ability to minimize error over a 5 s prediction horizon composed of 50 separate time-steps, their ability to predict through noisy inputs and mitigate the introduced error, and their computation costs. Furthermore, a large dataset made from a high fidelity simulation is transformed to reflect data that would be encountered in-situ, improving the applicability of the work. It was found that the proposed GRU-A model has superior signal prediction capabilities, achieving approximately 30 times lower error than the MLP-NAR model when predicting over a 5 s period, suitable of a vertical landing time horizon. In addition, the proposed GRU-A model was more resilient to input noise and, when trained with noise it outperformed the MLP-NAR. It was also found that the memory required to compute predictions with both models is approximately equal and that the computation time of the GRU-A model is similar to the MLP-NAR model with both models being capable of making predictions within 100 ms so long as they are not chosen to be too large. Overall, the proposed GRU-A model is demonstrated as a superior alternative to the more typical MLP-NAR model when predicting full signals in all cases for use with a small UAV.

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