Development and Performance Validation of a Navigation System for an Underwater Vehicle

Abstract

Underwater position data is a key requirement for the navigation and control of unmanned underwater vehicles. The proposed navigation scheme can be used in any vessel or boat for any shallow water vehicle. This paper presents the position estimation algorithm developed for shallow water Remotely Operated Vehicles (ROVs) using attitude data and Doppler Velocity Log data with the initial position from the Global Positioning System (GPS). The navigational sensors are identified using the in-house developed simulation tool in MATLAB, based on the requirement of a position accuracy of less than 5%. The navigation system is built using the identified sensors, Kalman filter and navigation algorithm, developed in LabVIEW software. The developed system is tested and validated for position estimation, with an emulator consisting of a GPS-aided fibre optic gyro-based inertial navigation system as a reference, and it is found that the developed navigation system has a position error of less than 5%.2. Doppler Velocity Log. 3. Position estimation. 4. Remotely Operated Vehicle.

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

“…Many scientists globally are working on comparative (terrain reference) navigation [8][9][10]. Most studies have analyzed the shape of the bottom of bodies of water obtained from the depth of the basin.…”

Testing an Underwater Robot Executing Transect Missions in Mayotte

“…Moreover, few works deal with testing and validating functionalities with a complete specification comparisons. From the authors cited previously, in [10], the authors validate that the final error is below a desired threshold (5%). In [6], the authors validate that the DVL used meets the indicated developer specification (0.2% maximum error).…”

“…For example, in [5], the authors performed tests in a river with several marine robots and only compared the localization performance of an Extended Kalman Filter (EKF) with other filters. In [10], the authors measured the precision of the navigation of their AUV by evaluating the position of the robot after resurfacing. In [6], the authors also performed sea trial tests comparing the speed measurement of their DVL sensor with the data of an accurate GNSS-USBL used as ground truth.…”

MSIS Image Postioning in Port Areas with the Aid of Comparative Navigation Methods

“…The comparative navigation methods have been mainly used, so far, for positioning with the aid of a radar and electronic navigation maps [18][19][20], although in recent years the comparison methods are becoming more and more frequently used in underwater positioning [21][22][23][24][25][26]. The research direction which has recently been very popular in this area refers to mobile navigation [27][28][29].…”