Predicting underwater vehicle manipulator system dynamics using time-series methods

In this study, we apply two time-series models, Long Short-Term Memory (LSTM) and Vector AutoRgressive with eXogenous Variables (VARX) models to predict underwater vehicle manipulator system (UVMS) dynamics simulated in a ROS/Gazebo environment under two environmental conditions commonly encountered in the wave zone, namely splash zone conditions, and wave loads on the UVMS when it is fully submerged, but close to the surface of the water. We also investigate the effect of dataset size and heterogeneity on the performance of these models.