path following of ROV with use of integrated navigation system based on continuous-time Kalman filter

A Remotely operated vehicle (ROV) requires a precise navigation system for positioning, path tracking, guidance, and control. Due to the limitation of the global positioning system in the underwater environment, the inertial navigation system is the most important positioning system in an underwater vehicle. In this paper, based on the nonlinear dynamics of the underwater robot to improve the performance of the underwater robot in path tracking, we propose an integrated navigation system based on inertial sensors, compass, and Doppler velocity log. A continuous-time extended Kalman filter was used to combine the sensor data and estimate the robot's position. The simulation results compared to the navigation systems based on linear Kalman filter and discrete-time extended Kalman filter show that the proposed integrated navigation system based on continuous-time extended Kalman filter can estimate the attitude and position of the robot in the control loop with high accuracy