Estimation of Linear and Pressure Drag Coefficients of an Underwater Robot Using Nonlinear Kalman Filters

Document Type : Research Article

Authors

1 Dept. of Mechanical Eng., Yazd University

2 Yazd University, Yazd, Iran

Abstract

Using kinetic models for the navigation of underwater robots is an important issue that has recently attracted the attention of many researchers. They are used as an auxiliary tool alongside the common navigation algorithms that use the kinematic models of the robots. Their use in underwater navigation is more crucial as the GPS and radio signals are not available in underwater environments and navigation algorithms mainly rely on the kinematic models used in a dead-reckoning configuration, where IMU and/or DVL data are used. To use a kinetic model for the navigation of an underwater vehicle, it is required to have accurate values of its hydrodynamic coefficients, where the linear and pressure drag coefficients are among the most crucial parameters to be identified. In this paper, the drag coefficients of a sample remotely operated vehicle (ROV) are estimated using an Extended Kalman filter (EKF) and an Unscented Kalman filter (UKF). For this purpose, a six DOF model of the underwater vehicle is used to simulate its motion. Then, the inputs and outputs of the simulated model are imported into the estimation algorithms to identify the drag coefficients of the robot. The simulation results show that the UKF identifies the hydrodynamic coefficients more accurately than EKF, using the same model and measurement noises. Also, by comparing the simulated maneuvers of the robot using the identified coefficients and the exact coefficients of the robot, it is observed that the coefficients identified by UKF lead to more accurate trajectories as compared to the coefficients identified by EKF.

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References

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Amirkabir Journal of Mechanical Engineering
Volume 56, Issue 1
2024
Pages 81-102

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