The bias error of the magnetic compass was estimated asymptotically by taking the GPS position data when it appeared. When the navigation algorithm estimated the bias error of the magnetic compass, the UUV's position was displayed in the NED coordinate system, even when the UUV was submerged. While using Matlab Simulink, a dynamic Autonomous Underwater Vehicle (AUV) simulation program was built to check the performance of the proposed navigation algorithm.
After receiving the data, the navigation algorithm estimates the compass heading bias error and the position of the AUV allowing control of the AUV and the ability to perform simulation of heading and heading control points. Simulations will show that the navigation algorithm improves the accumulated dead reckoning and magnetic compass position errors. This was achieved by the navigation algorithm that considers the bias error of the magnetic compass compared to the conventional dead reckoning method.
In other words, the navigation algorithm uses GPS positional data to estimate the position and attitude of the AUV and the TCM heading bias error, as long as the positional information is judged to be effective. Otherwise, the position and attitude of the AUV is estimated by dead reckoning considering the heading bias error of TCM obtained previously.
INTRODUCTION
Objective of research
By implementing dead reckoning with the DVL and the Tilt Compensated Compass Module (TCM), the AUV estimates the position relative to the initial position in the coordinate system with the X-axis pointing to magnetic north. In addition, the reference coordinate system generally used is based on geodetic north, which may cause another position error due to the difference between the reference coordinate system and the magnetic north-based coordinate system. 1.1 – Magnetic declination map and estimation of vehicle heading angle in North-East-Down (NED) coordinate system through initial calibration of magnetic compass.
Before elaborating on the change of the direction angles of the magnetic compass towards the geodetic north, in Fig. Considering the motion nature of the flying-type AUV and the magnetic compasses used for this study to verify the navigation algorithm, the conversion relationship of the magnetic compass coordinate system and the body-fixed coordinate system can be expressed as the rotation of to the (⫽) axis. From equation (1.2), the bias error can be considered as the sum of the magnetic declination , the rotation angle between the magnetic compass coordinate system and the body-fixed coordinate system , and measurement noise consistent with the influence of surrounding magnetic fields and sensor performance.
If GPS position data cannot be collected, the dead reckoning coordinate system can be approximated using the NED coordinate system, taking into account the initial value or the last estimated value of the heading angle difference. Thus, the position error that occurs due to the difference in the coordinate system will be corrected.
Organization of the thesis
GPS-AIDED NAVIGATION ALGORITHM
System model
Measurement model
The measured value of the magnetic compass is represented by the heading angle of the UUV to geodetic north and the heading angle error of the magnetic compass considering equation (1.1).
Navigation algorithm using extended Kalman filter
DYNAMIC SIMULATION
Block diagram of dynamic simulation
Dynamic model
- Coordinate system
- Kinematics
- Kinetics
- Rigid body dynamics
- Restoring forces and moments
- Equations of motion
- Ocean currents
Sensor model
Controller
Scenarios
Simulation results
FIELD TEST
Dimensions and specifications
The hover-type AUV is a test-bed for analyzing the dynamic characteristics of the AUV and performance verification of the control and navigation algorithm. In order to perform the test with the minimum number of employees, the size and weight of the AUV were determined as follows. The battery capacity was chosen by analyzing the energy consumption of the system so that it could perform the test for more than two hours in the sea.
To operate the AUV in the ocean, the operating depth of 10 m was set. On-board LabVIEW PC and Edison on-board C++ were used to develop the AUV's operating and navigation systems.
Boards
In order to modularize the navigation system and apply it to the AUV, an Edison board in Fig. The Edison module from Intel is composed of an Intel Atom 500MHz dual-core, dual-thread CPU, 1GB of RAM and 4GB of flash memory as an SoC using Intel's Quark 100MHz microcontroller. Yocto Linux was installed in the Edison board and navigation system module was developed by applying the navigation algorithm based on the Yocto Linux.
The calculation results of the navigation system will be delivered to the PC operating program via USB communication.
Sensors
The sensor measures speed by receiving reflected acoustic signals and has 4 bulging transducers to transmit an acoustic beam with a 22° radius. The pressure sensor used to measure the depth of the AUV is a PSC and is shown in Figure 1. The study used a PSC that has the output terminal form of a DIN 43650-A connector, shown in Figure 2.
The sensor measures pressure within the range of 0.5bar and accordingly has an analog voltage output of 1~5V. So the output can be measured using NI USB-6009 which is one of the data acquisition (DAQ) devices and shown in Fig. It is a multifunctional DAQ board that provides 8 analog inputs with 14bit resolution, two analog outputs with 12bit resolution and 12 digital inputs and outputs.
Additional specifications for the analog inputs used in the test had a maximum voltage range of –10~10V and an accuracy of 7.73mV.
Thrusters
The forward and backward thrust of the thrusters is controlled by the analog voltage of –5~5V.
Operating system
The measurement system consists of several modules developed by implementing the hardware and software respectively to receive and process the data from each sensor. Then, it obtains the position and attitude of the AUV via dead reckoning or the GPS-assisted navigation algorithm proposed in this study. The data is stored in the navigation system so that the user can resolve errors in the navigation system.
The display system receives information for the operation of the AUV from the measurement system and the navigation system. For convenience, command buttons have been made so that the operator can control the navigation system during the test. The information related to the behavior of the AUV was saved to review the test results and verify the performance of the navigation algorithm.
The control system receives the necessary information to make the AUV move according to given scenario. The control system was implemented by designing PD controllers for the two horizontal thrusters and two vertical thrusters to control the AUV with the desired heading angle and depth according to the scenario set by the operator.
Experimental overview
Field test results
The number 1 in the graph shows that the use of the navigation algorithm was efficient and it estimated the position of the AUV and the TCM's heading bias error for geodetic north using GPS positional data. Meanwhile, the number 0 shows that the position of the AUV has been estimated by dead reckoning and the TCM's heading bias error has not been estimated. It can also be confirmed that the navigation algorithm uses the last estimated heading bias error in those sections.
The estimated TCM heading bias error based on geodetic north converged within 2° from -4°~-6°. Considering the TCM bias error, the navigation algorithm estimated the heading angle based on geodetic north and it is shown in the green line. It can be seen that the TCM measurements and the desired steering angle show some variation because the navigation algorithm estimates the head bias error.
The field test results confirmed the effectiveness of the proposed GPS-assisted navigation algorithm. This study proposed a GPS-assisted AUV navigation algorithm using an extended Kalman filter. The navigation algorithm receives the GPS position data when the AUV appeared on the surface, improves the accumulated dead reckoning position error using the DVL and the magnetic compass, and estimates the heading error of the magnetic compass.
The dynamic simulation program was implemented using the NPS ARIES AUV model and applying the navigation algorithm. The performance of the navigation algorithm was verified by improving the accumulated dead reckoning position error and position errors caused by misalignment with the reference coordinate system. To verify the performance of the GPS-assisted navigation algorithm through field test, the navigation system with the navigation algorithm was developed and implemented on the hovering type AUV.
The performance of estimating the position of the AUV and estimating the TCM's heading bias error was verified by performing a heading control test. The performance of the navigation algorithm was also verified by comparing the endpoint position estimated by the navigation algorithm and dead reckoning. Furthermore, consideration for effective use of the navigation algorithm, taking into account the dynamic characteristics, depending on the platform, is likely to be necessary.
