Online Model Identification for Underwater Vehicles through Incremental Support Vector Regression

Bilal Wehbe, Alexander Fabisch, Mario Michael Krell

In: IEEE/RSJ. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-17) September 24-28 Vancouver British Columbia Canada Pages 4173-4180 ISBN 9781538626818 IEEE 9/2017.


This paper presents an online technique which employs incremental support vector regression to learn the damping term of an underwater vehicle motion model, subject to dynamical changes in the vehicle’s body. To learn the damping term, we use data collected from the robot’s on-board navigation sensors and actuator encoders. We introduce a new sample-efficient methodology which accounts for adding new training samples, removing old samples, and outlier rejection. The proposed method is tested in a real-world experimental scenario to account for the model’s dynamical changes due to a change in the vehicle’s geometrical shape.


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German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz