Nonlinear Dynamic Model-Based State Estimators for Underwater Navigation of Remotely Operated Vehicles

Abstract

This brief reports single degrees of freedom nonlinear dynamic model-based state estimators for the navigation of remotely operated vehicles (ROVs)-unmanned, tethered robots commonly used for underwater commercial and scientific tasks. These methods exploit knowledge of the vehicle's nonlinear dynamics, the forces and moments acting on the vehicle, and disparate position and velocity measurements. The position and velocity of the ROV are estimated using two methods: 1) a nonlinear observer (NLO) and 2) an extended Kalman filter (EKF). The NLO is derived and its stability proven using Lyapunov techniques. A high-precision 300-kHz acoustic positioning system provided a ground truth for the laboratory experiments and our results show that both the NLO and EKF position estimates possess lower standard deviations than the measurements from conventional 12-kHz long-baseline systems. A dynamic model sensitivity analysis is included for the laboratory experiments. Field experiments obtained with the Jason2 ROV at 2300-m depth demonstrate that the NLO and EKF work in the field. These experiments are, to the best of our knowledge, the first reported experiments of an NLO and EKF using a nonlinear model of ROV dynamics. The performance of the NLO and EKF are compared using the criteria of convergence, accuracy, precision, parameter sensitivity, and robustness to velocity measurement outages, and our results show that the NLO provides superior performance. The impact of these results includes more robust state estimation for underwater robots and an increased operating range that will provide new capabilities.