University of Tasmania

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Improving the localisation accuracy of AUVs operating in highly variable environmental conditions

posted on 2023-05-28, 11:17 authored by Randeni Pathiranachchilage, SAT
The objective of this thesis is to contribute to Autonomous Underwater Vehicle (AUV) navigation by improving vehicle localisation accuracy when Doppler Velocity Log (DVL) bottom-tracking is unavailable. The Inertial Navigation System (INS) based localisation solution is prone to extreme uncertainties due to double integration of inherent errors within the INS acceleration measurements, unless the solution being externally aided (e.g. velocity measurements using the DVL bottom-track). As a solution for this, an improved model-aided INS localisation technique is introduced, which is complimented with the development of a novel model calibration and new water column velocity estimation method. The techniques established in this project are tested and validated using experimental data from a set of field manoeuvres using a Gavia class AUV and the performance is compared against other commonly used localisation methods. A baseline mathematical model was developed in this work using system identification to predict the motion response of the AUV based on its control commands. However, such models are generally calibrated for low water column velocities and a standard vehicle configuration, and are limited in application for variations in environmental conditions. To address this limitation, a novel model calibration technique was established to field calibrate the parameters within the baseline model to the current operating condition and vehicle configuration. Model calibration improved the results of the baseline model up to 73% when operating in low energy environments and the AUV position can be computed within an uncertainty range of 1.5% of the distance travelled. In comparison, uncertainties of conventional non-bottom-tracking localisation techniques could be up to 10% in similar environmental conditions. A secondary approach is also presented to determine the hydrodynamic coefficients of the mathematical model using Computational Fluid Dynamics (CFD) simulations and captive model experiments when the AUV is operating in complex flow conditions. A non-acoustic method was introduced to estimate the velocity of the water column using the motion response of the AUV. This method is capable of accurately estimating water column velocities in proximity to the AUV (i.e., the water column velocities at the same depth as the vehicle is), which is not typically resolved with existing methods such as acoustic Doppler current profilers. When the mathematical model-aided localisation solution is complimented with water column velocity prediction method, the localisation error is limited to less than 6% of the distance travelled even in extremely high currents (i.e. >2 m s\\(^{-1}\\) in tested conditions). In such environmental conditions, the uncertainties of other commonly used non-bottom-tracking localisation methods, as tested against in this work, such as DVL water-track mode and unaided INS could be above 30%. One of the key advantages of the proposed localisation technique is that it could be applied to any torpedo shaped AUV (for example, platforms such as REMUS, Iver, Bluefin, Explorer, etc.) of any configuration by simply conducting a set of established field manoeuvres to identify its mathematical model parameters. Further, additional sensors beyond a typical AUV navigational payload (i.e. global positioning, accelerometers and gyroscopes) are not required to implement this technique in an AUV. The localisation technique developed in this thesis is capable of improving the motion control and navigation solution of the AUV in the absence of DVL bottom-tracking, which is critical for the expansion of vehicle performance in extreme environmental conditions.


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Copyright 2017 the author Chapter 2 appears to be the equivalent of a post-peer-review, pre-copyedit version of an article published in Nonlinear dynamics. The final authenticated version is available online at: Chapter 3 appears to be the equivalent of a post-print version of an article published as: Randeni, S. A. T., Forrest, A. L., Cossu, R., Leong, Z. Q., Ranmuthugala, D., 2017. Determining the horizontal and vertical water velocity components of a turbulent water column using the motion response of an autonomous underwater vehicle, Journal of marine science and engineering, 5(3), 25. Copyright 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( Appendix A appears to be the equivalent of an accepted article published as: Randeni, S. A. T., Forrest, A. L., Cossu, R., Leong, Z. Q., Ranmuthugala, D., 2015. Estimating flow velocities of the water column using the motion response of an autonomous underwater vehicle (AUV). OCEANS '15 MTS/IEEE. Washington D.C.: IEEE. Copyright 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Appendix C appears to be the equivalent of a post-print version of an article published as: Randeni, S. A. T., Leong, Z. Q., Ranmuthugala, D., Forrest, A. L., 2015. Numerical investigation of the hydrodynamic interaction between two underwater bodies in relative motion, Applied ocean research, 51, 14-24

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