Conceptual Design of Navigation of an AUV for Monitoring CCS Site at Deep Sea Bottom

2011 ◽  
Author(s):  
Yoshitaka Watanabe ◽  
Hiroshi Yoshida ◽  
Hiroshi Ochi ◽  
Tadahiro Hyakudome ◽  
Shojiro Ishibashi ◽  
...  
Keyword(s):  
Conceptual Design ◽  
Navigation System ◽  
Autonomous Underwater Vehicle ◽  
Acoustic Method ◽  
Doppler Velocity ◽  
Depth Sensor ◽  
Short Baseline ◽  
Position Information ◽  
Sea Bottom ◽  
Acoustic Methods

We, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), are developing an autonomous underwater vehicle (AUV) whose main mission is monitoring a site at the sea bottom for the carbon dioxide capture and storage (CCS). The AUV cruises very near the sea bottom, and is equipped with chemical sensors in order to detect escape of CO2 from sub-bottom. Of course, the position information of the AUV is critical information for the monitoring. In this paper, a conceptual design of navigation of the AUV is described. Recently, navigation of AUV is implemented by integrating multiple navigation devices including inertial navigation system (INS), Doppler velocity log (DVL), depth sensor, acoustic navigation system, and others. The AUV under construction will be equipped with these navigation sensors, and will integrate those sensors’ outputs to navigate herself. In order to measure the absolute position of the AUV the acoustic method is one of fundamental technique. At the first step of development of the AUV, three acoustic methods are considered to adopt. The three methods are super short baseline (SSBL) method which is a tracking from support ship or other surface station, long baseline (LBL) which is navigation based on preplaced acoustic transponders, and virtual LBL (VLBL) which is navigation based on only single transponder. These acoustic methods are integrated with the navigation result of INS, depth sensor, and DVL. The three methods are used in each appropriate case. Which feature of observation is desired simplicity, accuracy, or independence from support ship and time efficiency? The acoustic method is influenced by environment, and also output of other sensors is depending on the environment, for example the DVL miss the data when the terrain is with many up-hills and down-hills. The integration or filtering parameters of the navigation should be adjusted depending on the influential environmental factor.

Basic Study on Positioning of Autonomous Underwater Vehicle Based on Acoustic Ranging From One Reference Device

2020 ◽  
Author(s):  
Yoshitaka Watanabe
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Atomic Clock ◽  
Underwater Vehicle ◽  
Direct Sequence Spread Spectrum ◽  
Integrated System ◽  
Doppler Velocity ◽  
Depth Sensor ◽  
Short Baseline ◽  
Reference Device ◽  
Acoustic Positioning

Abstract An autonomous underwater vehicle (AUV) equipped with some navigational devices, such as an inertial navigation system (INS), a Doppler velocity log (DVL), and a depth sensor. This integrated system is typical and accurate, however, have drift error in position output. Then an acoustic positioning, which is one of absolute positioning, is necessary to compensate the drift error. As acoustic positioning, ultra-short baseline (USBL) is frequently used, however, not accurate especially in deep water. Long baseline (LBL) is very accurate, however, spends much time to operate. In this paper, acoustic positioning method of AUV based on ranging from only one reference device is considered with simulations. One-way travel time (OWTT) is measured for ranging assuming use of chip scale atomic clock (CSAC). And the ranging signal is continuous and modulated direct sequence spread spectrum (DSSS). Ranging is executed at each symbol peak, then period of the ranging is very small. In process of the method, the drift error of position output of the INS is estimated with extended Kalman filter (EKF). Simulation with two models, constant-position-error, and constant-velocity-error models, were performed. The later was obviously unstable. Circular cruising track of the A UV makes the estimation more stable. Moreover, when the AUV cruises near the reference, the estimation is more stable. It may good that at final phase of descending of the AUV, the AUV cruises near the seabed and spirally, and the estimation process may be performed stably.

scholarly journals A single acoustic beacon-based positioning method for underwater mobile recovery of an AUV

2018 ◽  
Vol 15 (5) ◽  
pp. 172988141880173 ◽  
Author(s):  
Ziye Zhou ◽  
Yanqing Jiang ◽  
Ye Li ◽  
Cao Jian ◽  
Yeyi Sun
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Dead Reckoning ◽  
Underwater Vehicle ◽  
Doppler Velocity ◽  
Fusion Algorithm ◽  
Time Step ◽  
Short Baseline ◽  
Positioning Method ◽  
One Step ◽  
The One

This article presents a navigation method for an autonomous underwater vehicle being recovered by a human-occupied vehicle. The autonomous underwater vehicle is considered to carry underwater navigation sensors such as ultra-short baseline, Doppler velocity log, and inertial navigation system. Using these sensors’ information, a navigation module combining the ultra-short baseline positioning and inertial positioning is established. In this study, there is assumed to be no communication between the autonomous underwater vehicle and human-occupied vehicle; thus, to obtain the autonomous underwater vehicle position in the inertial coordinate, a conjecture method to obtain the human-occupied vehicle coordinates is proposed. To reduce the error accumulation of autonomous underwater vehicle navigation, a method called one-step dead reckoning positioning is proposed, and the one-step dead reckoning positioning is treated as a correction to combine with ultra-short baseline positioning by a data fusion algorithm. One-step dead reckoning positioning is a positioning method based on the previous time-step coordinates of the autonomous underwater vehicle.

scholarly journals Underwater caves sonar data set

2017 ◽  
Vol 36 (12) ◽  
pp. 1247-1251 ◽  
Author(s):  
Angelos Mallios ◽  
Eduard Vidal ◽  
Ricard Campos ◽  
Marc Carreras
Keyword(s):  
Autonomous Navigation ◽  
Autonomous Underwater Vehicle ◽  
Ground Truth ◽  
Ease Of Use ◽  
Doppler Velocity ◽  
Depth Sensor ◽  
Data Set ◽  
Sonar Sensors ◽  
Measurement Units ◽  
Imaging Sonar

This paper describes a data set collected with an autonomous underwater vehicle testbed in the unstructured environment of an underwater cave complex. The vehicle is equipped with two mechanically scanned imaging sonar sensors to simultaneously map the caves horizontal and vertical surfaces, a Doppler velocity log, two inertial measurement units, a depth sensor, and a vertically mounted camera imaging the sea floor for ground truth validation at specific points. The testbed collected the data in July 2013, guided by a human diver, to sidestep autonomous navigation in a complex environment. For ease of use, the original robot operating system bag files are provided together with a version combining imagery and human-readable text files for processing on other environments.

Acoustic AUV Navigation Referring to the Position of a Surface Station With Acoustic Data Transmission

2010 ◽  
Author(s):  
Yoshitaka Watanabe ◽  
Hiroshi Ochi ◽  
Takuya Shimura ◽  
Takehito Hattori
Keyword(s):  
Acoustic Signal ◽  
Autonomous Underwater Vehicle ◽  
The Body ◽  
Positional Error ◽  
Depth Sensor ◽  
Short Baseline ◽  
Acoustic Data ◽  
Receiver Array ◽  
Acoustic Navigation ◽  
Surface Station

In this paper, a navigation method of autonomous underwater vehicle (AUV) is proposed. In the method, the continuous acoustic signal is transmitted from a surface station to the AUV. The acoustic signal includes information of the position and the velocity of the surface station measured by the global positioning system (GPS). The AUV receives the acoustic signal with receiver array equipped on the top surface of the body, and obtain the included information and perform the inverted ultra short baseline (IUSBL) computation using the same acoustic signal. Using the depth data by a depth sensor, the AUV is not needed to transmit any acoustic signal to measure the round-trip time of the acoustical propagation. The output of the inertial navigation system (INS) equipped on the AUV, the IUSBL result, and the transmitted information are integrated for the navigation of AUV. A simulation result was shown. The depth of the AUV was 3000 meters. In the simulation the used sensors had the typical error source respectively. The initial positional error of the INS output was about 100 meters. As the result, the error was converged within about 100 seconds and finally the error was around 1 meter. In this method the large random error of the acoustic navigation is rapidly converged because the output rate of the acoustic navigation is very fast.

scholarly journals NAVIGATION AND MOTION CONTROL SYSTEMS OF THE AUTONOMOUS UNDERWATER VEHICLE

2020 ◽  
Vol 4 ◽  
pp. 38-50
Author(s):  
Dmitry Antonov ◽  
Leonid Kolganov ◽  
Aleksey Savkin ◽  
Egor Chekhov ◽  
Maxim Ryabinkin
Keyword(s):  
Control System ◽  
Automatic Control ◽  
Control Systems ◽  
Motion Control ◽  
Navigation System ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Transportation Safety ◽  
Doppler Velocity ◽  
Using Data

Autonomous underwater vehicles (AUVs) are widely used and have proven their effectiveness in tasks such as transportation safety, area monitoring and seafloor mapping. When developing AUV’s navigation and control systems, the engineers have to ensure the required levels of accuracy and reliability for solving navigation and motion control tasks in autonomous underwater operation under restrictions on the overall dimensions and power consumption of the AUV. The main purpose of this paper is to present preliminary results of AUV navigation and motion control systems development. The AUV’s navigation system is built around strapdown inertial navigation system (SINS) designed specifically for this AUV. When surfaced, position and angular SINS correction is performed using data from dual-antenna GNSS receiver and doppler velocity log (DVL). When underwater, SINS position and velocity correction is performed using acoustic navigation system (ANS) and DVL data. AUV’s control system provides manual and automatic control. Manual control is carried out in real-time by operator via fiber-optic cable using a joystick. Automatic control allows AUV to move independently along a specified trajectory at a given depth and speed. The AUV also has a collision avoidance system that utilizes readings from a forward-facing acoustic rangefinder to estimate time before impact based on AUV’s analytic model. If possible collision is detected, information is transmitted to the control system so that a further appropriate action can be taken. Computer simulation utilizing the analytic AUV model was used in order to check the performance characteristics of the designed control and navigation algorithms. After confirming the operability of the developed algorithms, preliminary tests of the AUV were carried out. During the tests, AUV’s on-board equipment and navigation system readings were recorded and compared to the readings of the reference system, which was also installed on the AUV. During the tests, the dynamic characteristics of the AUV were evaluated. AUV’s characteristics obtained during simulation and testing will be used as a reference during future development

A Low Complexity Integrated Navigation System for Underwater Vehicles

2018 ◽  
Vol 71 (5) ◽  
pp. 1161-1177 ◽  
Author(s):  
Mehdi Emami ◽  
Mohammad Reza Taban
Keyword(s):  
Navigation System ◽  
Autonomous Underwater Vehicle ◽  
Three Dimensional ◽  
Low Complexity ◽  
Doppler Velocity ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Sea Trial ◽  
Integrated Navigation System ◽  
Computational Load

This paper proposes a simplified algorithm for reducing the computational load of the conventional underwater integrated navigation system. The system usually comprises a three-dimensional accelerometer, a three-dimensional gyroscope, a three-dimensional Doppler Velocity Log (DVL) and a data fusion algorithm, such as a Kalman Filter (KF). Since the expected variations of roll, pitch and depth are small, these quantities are assumed to be constant, and the proposed system is designed in a two-dimensional form. Due to the low speed of the vehicle, the nonlinear dynamic equation of the velocity can be simplified in a linear form. We also simplify the conventional KF in order to avoid matrix multiplications and matrix inversions. The performance of the designed system is evaluated in a sea trial by an Autonomous Underwater Vehicle (AUV). The results show that the proposed system can significantly reduce the computational load of the conventional integrated navigation system without a significant reduction in position and velocity accuracy.

Identification of hydrodynamic coefficients of AUV in the presence of measurement biases

2021 ◽  
pp. 147509022110574
Author(s):  
Mustafa Dinç ◽  
Chingiz Hajiyev
Keyword(s):  
Parameter Identification ◽  
Navigation System ◽  
Autonomous Underwater Vehicle ◽  
Least Square ◽  
Parameter Determination ◽  
Doppler Velocity ◽  
Second Phase ◽  
Hydrodynamic Coefficients ◽  
Identification Method ◽  
Measurement Biases

This paper mainly presents the parameter identification method developed from a Least Square Estimation (LSE) algorithm to estimate hydrodynamic coefficients of Autonomous Underwater Vehicle (AUV) in the presence of measurement biases. LSE based parameter determination method is developed to obtain unbiased estimated values of hydrodynamic coefficients of AUV from biased Inertial Navigation System (INS) measurements. The proposed parameter identification method consists of two phases: in the first phase, high precision INS and its auxiliary instrument including compass, pressure depth sensor, and Doppler Velocity Log (DVL) are designed as Integrated Navigational System coupled with Complementary Kalman Filter (CKF) to determine hydrodynamic coefficients of AUV by removing the INS measurement biases; in the second phase, LSE based parameter identification method is applied to the model in the first phase for obtaining unbiased estimated values of hydrodynamic coefficients of AUV. In this paper, a method for identifying the yaw and sway motion dynamic parameters of an AUV is given. Various maneuvering scenarios are verified to assess the parameter identification method employed. The simulation results indicate that using the CKF based Integrated Navigation System together with unbiased measurement conversion could produce better results for estimating the hydrodynamic coefficients of AUV.

An Experiment of Acoustic Navigation System Using Inverse Super Short Baseline for Underwater Vehicle

2014 ◽  
Author(s):  
Yoshitaka Watanabe ◽  
Hiroshi Ochi ◽  
Takuya Shimura
Keyword(s):  
Acoustic Signal ◽  
Relative Position ◽  
Navigation System ◽  
Deep Sea ◽  
Test System ◽  
Experimental Device ◽  
Doppler Velocity ◽  
Depth Information ◽  
Short Baseline ◽  
Mother Ship

Recently underwater vehicles are typically navigated with an inertial navigation system (INS), a Doppler velocity log (DVL), and an acoustic positioning system (APS). APS are necessary, especially in deep sea observation, because it is absolute positioning method. Super short baseline (SSBL) is frequently used because it is easy to operate. In SSBL, the position of vehicle is obtained on the mother ship. In order to use the positioning result to navigate the vehicle, the result is transmitted to the vehicle with a certain amount of delay. Authors are developing a test system of new type APS using inverse SSBL (ISSBL) method. In this method, the vehicle is equipped with a receiver array. Arrival direction of acoustic signal from mother ship is detected, and relative position between the mother ship and the vehicle is calculated with the obtained direction and the depth. Information of ship’s position is included in the transmitted acoustic signal, then absolute position of the vehicle can be calculated with the relative position and the included information. The vehicle position can be obtained in the vehicle in real-time and be used directly to navigate. No reply from the vehicle is necessary. An ocean experiment of this method was conducted in Sagami Bay in Japan. Experimental device was moored on the seabed and the ship cruised with acoustic signal transmission. As a result, this method was available in deep sea area. Demodulation of information in the method was feasible, and positioning of the experimental device was achieved. High rate positioning is useful suppress random error with filtering.

scholarly journals Adaptive Navigation Algorithm with Deep Learning for Autonomous Underwater Vehicle

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6406
Author(s):  
Hui Ma ◽  
Xiaokai Mu ◽  
Bo He
Keyword(s):  
Deep Learning ◽  
Microelectromechanical Systems ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Low Frequency ◽  
Doppler Velocity ◽  
Position Information ◽  
Localization Accuracy ◽  
Navigation Algorithm ◽  
Frequency Position

Precise navigation is essential for autonomous underwater vehicles (AUVs). The measurement deviation of the navigation sensors, especially the microelectromechanical systems (MEMS) sensors, is a crucial factor that affects the localization accuracy. Deep learning is a novel method to solve this problem. However, the calculation cycle and robustness of the deep learning method may be insufficient in practical application. This paper proposes an adaptive navigation algorithm with deep learning to address these questions and realize accurate navigation. Firstly, this algorithm uses deep learning to generate low-frequency position information to correct the error accumulation of the navigation system. Secondly, the χ2 rule is selected to judge if the Doppler velocity log (DVL) measurement fails, which could avoid interference from DVL outliers. Thirdly, the adaptive filter, based on the variational Bayesian (VB) method, is employed to estimate the navigation information simultaneous with the measurement covariance, improving navigation accuracy even more. The experimental results, based on AUV field data, show that the proposed algorithm could realize robust navigation performance and significantly improve position accuracy.

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