Publication
Experimental Evaluation of Various Machine Learning Regression Methods for Model Identification of Autonomous Underwater Vehicles
Bilal Wehbe; Marc Hidebrandt; Frank Kirchner
In: Proceedings of 2017 International Conference on Robotics and Automation (ICRA). IEEE International Conference on Robotics and Automation (ICRA-17), May 29 - June 3, Sands Expo and Convention Centre, Singapore, Pages 4885-4890, ISBN 9781509046324, IEEE, 5/2017.
Abstract
In this work we investigate the identification of
a motion model for an autonomous underwater vehicle by
applying different machine learning (ML) regression meth-
ods. By using the data collected from the robot’s on-board
navigation sensors, we train the regression models to learn
the damping term which is regarded as one of the most
uncertain components of the motion model. Four regression
techniques are investigated namely, artificial neural networks,
support vector machines, kernel ridge regression, and Gaussian
processes regression. The performance of the identified models
is tested through real experimental scenarios performed with
the AUV Leng. The novelty of this work is the identification
of an underwater vehicle’s motion model, for the first time,
through machine learning methods by using the robot’s on-
board sensory data. Results show that the damping model
learned with nonlinear methods yield better estimates than the
simplified linear and quadratic model which is identified with
least-squares technique.