Optimization-Based Online Flow Fields Estimation for AUVs Navigation

Abstract

<p>The motion of an autonomous underwater vehicle (AUV) is affected by its surrounding water flows, so an accurate estimation of the flow field could be used to assist the vehicle’s navigation. We propose an optimization-based approach to the problem of online flow field learning with limited amounts of data. To compensate for the shortage of online measurements, we identify two types of physically meaningful constraints from eddy geometry of the flow field and the property of fluid incompressibility respectively. By parameterizing the flow field as a polynomial vector field, the optimization problem could be solved efficiently via semi-definite programming (SDP). The effectiveness of the proposed algorithm in terms of flow field estimation is experimentally validated on real-world ocean data by providing performance comparisons with a related method. Further, the proposed estimation algorithm is proved to be able to be combined with a motion planning method to allow an AUV to navigate efficiently in an underwater environment where the flow field is unknown beforehand.</p>