Model-Aided Navigation with Sea Current Estimation for an Autonomous Underwater Vehicle

Martínez, Alain; Hernández, Luis; Sahli, Hichem; Valeriano-Medina, Yunier; Orozco‐Monteagudo, Maykel; Garcia-Garcia, Delvis

doi:10.5772/60415

Cited by 19 publications

(8 citation statements)

References 26 publications

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“…According to [8,10] underwater navigation has generally two main approaches, namely: dead reckoning, and acoustic. In the dead reckoning navigation [1,3,12] we deal with three main components, i.e. sensors, kinematic model of underwater vehicle, and vehicle state estimation method.…”

Section: Considered Workmentioning

confidence: 99%

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Correction of Navigational Information Supplied to Biomimetic Autonomous Underwater Vehicle

Praczyk

2018

Polish Maritime Research

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In order to autonomously transfer from one point of the environment to the other, Autonomous Underwater Vehicles (AUV) need a navigational system. While navigating underwater the vehicles usually use a dead reckoning method which calculates vehicle movement on the basis of the information about velocity (sometimes also acceleration) and course (heading) provided by on-board devicesl ike Doppler Velocity Logs and Fibre Optical Gyroscopes. Due to inaccuracies of the devices and the influence of environmental forces, the position generated by the dead reckoning navigational system (DRNS) is not free from errors, moreover the errors grow exponentially in time. The problem becomes even more serious when we deal with small AUVs which do not have any speedometer on board and whose course measurement device is inaccurate. To improve indications of the DRNS the vehicle can emerge onto the surface from time to time, record its GPS position, and measure position error which can be further used to estimate environmental influence and inaccuracies caused by mechanisms of the vehicle. This paper reports simulation tests which were performed to determine the most effective method for correction of DRNS designed for a real Biomimetic AUV.

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Section: Considered Workmentioning

confidence: 99%

“…To increase the chance of finding a global optimum of the function (12), the optimization algorithm is repeated from many starting points which differ in value of . Starting values for and do not change and they are solutions of the linear problem.…”

Section: Correction Procedures No 1 -Cp1mentioning

confidence: 99%

Correction of Navigational Information Supplied to Biomimetic Autonomous Underwater Vehicle

Praczyk

2018

Polish Maritime Research

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“…Although [5], [15] and [8] proposed techniques to accurately estimate underwater currents, such techniques require the above mentioned sensors, and cannot be applied to low-cost AUVs that are only packaged with a MEMS IMU. Martinez et al [16] proposed a method to estimate the magnitude and direction of surface currents by running the AUV in circles on the surface when the AUV has GPS fix and computing the drift due to currents. This technique can be utilized by low-cost AUVs; however, the water column velocity often varies with the depth.…”

Section: Limitationsmentioning

confidence: 99%

Implementation of a Hydrodynamic Model-Based Navigation System for a Low-Cost AUV Fleet

Randeni

Rypkema

Fischell

et al. 2018

2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV)

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This work implements a hydrodynamic model-based localization and navigation system for low-cost autonomous underwater vehicles (AUVs) that are limited to a micro-electro mechanical system (MEMS) inertial measurement unit (IMU). The hydrodynamic model of this work is uniquely developed to directly determine the linear velocities of the vehicle using the measured vehicle angular rates and propeller speed as inputs. The proposed system was tested in the field using a fleet of low-cost Bluefin SandShark AUVs. Implementation of the model-based localization system and fusing of the solution into the vehicle navigation loop was conducted using backseat computers of the AUV fleet that run mission orientated operating suite -interval programming (MOOS-IvP). With the model-based navigation system, the maximum localization error (i.e., in comparison to a long baseline (LBL) based ground-truth position) was limited to 15 m and 30 m for two 650-second and 1070-second long missions. Extrapolation of the position drift shows that the model-based localization system is able to limit the position uncertainty to less than 100 m by the end of hour-long mission; whereas, the drift in the default IMU-based localization solution was over 1 km per hour. This is a considerable improvement by only using a MEMS IMU that generally costs less than $100. Furthermore, this work is a step towards generalizing and automating the process of hydrodynamic modeling, model parameter estimation and data fusion (i.e., fusing the localization solution with those from other available aiding sensors and feeding to the navigation loop) so that a model-based localization system can be implemented in any AUV that has backseat computing capability.

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“…Owing to the complexity of the marine environment, DVL measurements are easily affected by seawater properties. Many studies have been devoted to solving the problems posed by the time-varying noise, non-Gaussian noise, and outliers of the 3D velocity in LC systems [14][15][16][17]. Research on LC systems has been fruitful, and it has been applied to practical systems.…”

Section: Introductionmentioning

confidence: 99%

A performance-enhanced DVL/SINS integrated navigation system based on data-driven approach

Jin

Chai

et al. 2023

Meas. Sci. Technol.

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With the aid of Doppler velocity logger (DVL), strapdown inertial navigation systems (SINS) can provide continuous and accurate navigation parameters for unmanned underwater vehicles (UUVs). However, owing to the complex underwater environment, partial DVL beams may fail to be reflected by the seafloor, resulting in DVL measurement outage. In this study, a novel data-driven approach enhancing DVL/SINS integrated navigation system is proposed to improve the robustness and accuracy of UUV navigation with limited DVL beams. First, to make full use of the available beam, the velocities of DVL beams are used to assist the SINS navigation instead of the 3D velocity of DVL. Subsequently, a virtual beam predictor based on multi-output least-squares support vector regression (MLS-SVR) is built to run in parallel with the DVL/SINS integrated system. Specifically, when the DVL operates normally, all four beams can be applied for integrated navigation and stored as the training dataset. Once partial beams are missing, the available beams and the outputs of SINS and pressure sensor can train the MLS-SVR model along with the corresponding missing beam in the training set. Subsequently, the missing beams can be predicted by the trained model for integrated navigation together with the available beams. UUV sea trial indicate the proposed system can accurately predict the unavailable beams and improve the positioning accuracy of the DVL/SINS integrated navigation system.

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Model-Aided Navigation with Sea Current Estimation for an Autonomous Underwater Vehicle

Cited by 19 publications

References 26 publications

Correction of Navigational Information Supplied to Biomimetic Autonomous Underwater Vehicle

Correction of Navigational Information Supplied to Biomimetic Autonomous Underwater Vehicle

Implementation of a Hydrodynamic Model-Based Navigation System for a Low-Cost AUV Fleet

A performance-enhanced DVL/SINS integrated navigation system based on data-driven approach

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