Detailed knowledge on the shape of the seafloor is crucial for many researchers. Bathymetric data are critical for navigational safety and are used for underwater mapping. This study develops a sea-surface vehicle (SSV) system for underwater mapping by using both bathymetric data from a low-cost single-beam echosounder located on the SSV, and the navigation data of the SSV. The navigation of the SSV was obtained using a global positioning system (GPS). The effect of changing bathymetric and navigation data due to external disturbances such as wind and waves on the map was analyzed. The sea-bottom slope angles, which are effective in changing bathymetric data, were estimated and corrected in relation to the estimated angles in a particular mapped area for more accurate underwater mapping. Additionally, the effects of the grid range of the mapped area, beam angle of the echosounder, and position of the echosounder on the underwater mapping were analyzed. These analyses were based on simulation data, and were performed in a MATLAB, HYPACK, and Global Mapper environment. An underwater map was also obtained in the Kozlu/Zonguldak area, Black Sea by using a single-beam echosounder located on the SSV. This map was improved by estimating sea-bottom slope angles and the corrected bathymetric data to obtain a more accurate underwater map of the area. The experimental and simulation results were compared, focusing on the sea-bottom slope changes, sea-surface disturbances, bathymetry grid range changes, and draft effects.
In this study, a nonlinear mathematical model for an unmanned underwater survey vehicle (SAGA) is obtained. The structure of the mathematical model of the vehicle comes from a Newton–Euler formulation. The three-dimensional motion is realized by a suitable combination of right, left and vertical thrusters. The navigation problem is solved by a combination of the inertial navigation system and acoustic-based measurements, which are integrated to obtain more accurate vehicle navigation data. In addition, a magnetic compass and a depth sensor are used to support vehicle attitude and depth information. A pool experimental set-up is designed for the navigation system. The performance of the resultant navigation system can be analysed by creating suitable system state, measurement and noise models. The vehicle navigational data are improved with a Kalman filter. The mathematical model of the vehicle includes some unknown parameters, such as added mass and damping coefficients. It is not possible to determine all the parameter values as their effect on the state of the system is usually negligible. However, most of the ‘important’ parameters are obtained from a system identification study of the vehicle by means of the estimated navigational data for coupled motion. The entire study is performed in a Matlab/Simulink environment.
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