Learning Coupled Dynamic Models of Underwater Vehicles using Support Vector Regression
Bilal Wehbe, Mario Michael Krell
In Proceeding of Oceans '17 MTS/IEEE Aberdeen, (OCEANS-17), 19.6.-22.6.2017, Aberdeen, IEEE, Jun/2017. MTS/IEEE. ISBN: 9781509052783.
This work addresses a data driven approach which employs a machine learning technique known as Support Vector Regression (SVR), to identify the coupled dynamical model of an autonomous underwater vehicle. To train the regressor, we use a dataset collected from the robot’s on-board navigation sensors and actuators. To achieve a better fit to the experimental data, a variant of a radial-basis-function kernel is used in combination with the SVR which accounts for the different complexities of each of the contributing input features of the model. We compare our method to other explicit hydrodynamic damping models that were identified using the total least squares method and with less complex SVR methods. To analyze the transferability, we clearly separate training and testing data obtained in real-world experiments. Our presented method shows much better results especially compared to classical approaches.
Underwater Robotics, AUV, Coupled Dynamics, Support Vector Regression