Improving underwater vehicle navigation state estimation using locally weighted projection regression

Georgios Fagogenis, David Flynn, David Michael Lane

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

Navigation is instrumental in the successful deployment of Autonomous Underwater Vehicles (AUVs). Sensor hardware is installed on AUVs to support navigational accuracy. Sensors, however, may fail during deployment, thereby jeopardizing the mission. This work proposes a solution, based on an adaptive dynamic model, to accurately predict the navigation of the AUV. A hydrodynamic model, derived from simple laws of physics, is integrated with a powerful non-parametric regression method. The incremental regression method, namely the Locally Weighted Projection Regression (LWPR), is used to compensate for un-modeled dynamics, as well as for possible changes in the operating conditions of the vehicle. The augmented hydrodynamic model is used within an Extended Kalman Filter, to provide optimal estimations of the AUV's position and orientation. Experimental results demonstrate an overall improvement in the prediction of the vehicle's acceleration and velocity.
Original languageEnglish
Title of host publicationRobotics and Automation (ICRA), 2014 IEEE International Conference on
PublisherIEEE
Pages6549-6554
DOIs
Publication statusPublished - 31 May 2014

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