A survey of underwater vehicle navigation: Recent advances and new challenges

A particle filter (PF)-based robust navigation with fault diagnosis (FD) is designed for an underwater robot, where 10 failure modes of sensors and thrusters are considered. The nominal underwater robot and its anomaly are described by a switching-mode hidden Markov model. By extensively running a PF on the model, the FD and robust navigation are achieved. Closed-loop full-scale experimental results show that the proposed method is robust, can diagnose faults effectively, and can provide good state estimation even in cases where multiple faults occur. Comparing with other methods, the proposed method can diagnose all faults within a single structure, it can diagnose simultaneous faults, and it is easily implemented.

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Particle Filter for Fault Diagnosis and Robust Navigation of Underwater Robot

Purpose – The main purpose of this paper is to investigate two key elements of localization and mapping of Autonomous Underwater Vehicle (AUV), i.e. to overview various sensors and algorithms used for underwater localization and mapping, and to make suggestions for future research.Design/methodology/approach – The authors first review various sensors and algorithms used for AUVs in the terms of basic working principle, characters, their advantages and disadvantages. The statistical analysis is carried out by studying 35 AUV platforms according to the application circumstances of sensors and algorithms.Findings – As real‐world applications have different requirements and specifications, it is necessary to select the most appropriate one by balancing various factors such as accuracy, cost, size, etc. Although highly accurate localization and mapping in an underwater environment is very difficult, more and more accurate and robust navigation solutions will be achieved with the development of both sensors and...

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Towards autonomous localization and mapping of AUVs: a survey

This paper addresses the development of the simulation of the low-level control system for the underwater remotely operated vehicle Visor3. The 6-DOF mathematical model of Visor3 is presented using two coordinated systems: Earth-fixed and body-fixed frames. The navigation, guidance, and control (NGC) structure is divided into three layers: the high level or the mission planner; the mid-level or the path planner; and the low level formed by the navigation and control systems. The nonlinear model-based observer is developed using the extended Kalman filter (EKF) which uses the linearization of the model to estimate the current state. The behavior of the observer is verified through simulations using Simulink®. An experiment was conducted with a trajectory that describes changes in the and and yaw components. To accomplish this task, two algorithms are compared: a multiloop PID and PID with gravity compensation. These controllers and the nonlinear observer are tested using the 6-DOF mathematical model of Visor3. The control and navigation systems are a fundamental part of the low-level control system that will allow Visor3’s operators to take advantage of more advanced vehicle’s capabilities during inspection tasks of port facilities, hydroelectric dams, and oceanographic research.

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Development of a Low-Level Control System for the ROV Visor3

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.

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

Hydro-Que/spl acute/bec developed a new underwater ROV which is use to inspect its dams. Since 2000, the ROV has allowed Hydro-Que/spl acute/bec to save 5M$ in inspection costs. This paper presents the study of a Kalman filter and its performances. Kalman filtering is used to merge data from fiber gyros, DVL, accelerometers and global positioning system in order to have a better estimation of the position and orientation of the ROV. In real systems, sensors do not send data at the same rate and data acquisition is often intermittent. One innovative aspect of this approach is that it accepts asynchronous information and delay from the sensors. Experimental results in a real environment are presented.

R. Houde

Papers

Implementation of a ROV navigation system using acoustic/Doppler sensors and Kalman filtering