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ISBN 9783843935302

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978-3-8439-3530-2, Reihe Robotik und Automation

David Oertel
Deep-Sea Model-Aided Navigation Accuracy for Autonomous Underwater Vehicles Using Online Calibrated Dynamic Models

212 Seiten, Dissertation Karlsruher Institut für Technologie (2018), Softcover, B5

Zusammenfassung / Abstract

In this work, the accuracy of inertial-based navigation systems for autonomous underwater vehicles (AUVs) in typical mapping and exploration missions up to 5000m depth using high-quality navigation sensors is examined. Ultra-short baseline (USBL) is used as the main acoustic positioning sensor which is challenging for long range applications. Additionally, the benefit of model-aided navigation in deep-sea, i.e. feeding an AUV motion model as an additional virtual sensor to the navigation system, is surveyed. This is done w.r.t. the best possible case when all basic sensors are available at all times with their basic sample rate.

The model-aiding is activated after the AUV gets close to sea-bottom. This reflects the case where the motion model is identified online which is only feasible if the velocity sensor (DVL) is available close to the sea bottom (e.g. 100m).

The results indicate that, ideally, deep-sea navigation via USBL can be achieved with an accuracy in range of 3-15 w.r.t. the expected root-mean-square error.

In case the actual estimation certainty is already below a certain threshold (ca. <4m), the simulations reveal that the model-aided scheme can improve the navigation accuracy w.r.t. position by 3-12% in case the full sensor ensemble is available.