Underwater Terrain-Aided Navigation Relocation Method in the Arctic

To improve the navigation accuracy of AUV (autonomous underwater vehicle), SINS (strapdown inertial navigation system), GPS (global positioning system), DVL (Doppler velocity log) and TAN (terrain aided navigation) are adopted in the AUV integrated navigation system. The mathematic model of the AUV integrated navigation system and the observation model of the chosen navigation sensors are built according to the system simulation experiments data. An improved filter based on RBF neural network for adjusting the information sharing factors is designed and implemented in the AUV integrated navigation system. Simulation experiments are carried out according to the mathematic model. It can be concluded from the simulation experiments that the navigation accuracy is improved substantially with the multiple sensors and federated filter in case that colored noise is engaged. The novel integrated navigation system is effective in prohibiting the divergence of the filter and improving fault tolerance ability and it greatly raises the precision of the navigation accuracy for the AUV integrated navigation system.

The ultra-low resolution underwater terrain maps of the Arctic region reduce the localization and navigation accuracy of the underwater vehicle relying on terrain-aided navigation. In this paper, we study the navigation ability of Autonomous Underwater Vehicles (AUVs) under the ultralow-resolution terrain map. Firstly, the low-resolution map is transformed into a continuous map by bilinear interpolation. Then, a Terrain-Aided Navigation (TAN) system based on the Particle Filter (PF) is constructed to estimate the state of AUV position by particles. Particles of a random distribution of fixed variance can effectively track targets. However, a fixed variance distribution is not well adapted to many different situations. To improve navigation accuracy and robustness, fuzzy logic is used to estimate the distribution variance of particles under the current terrain gradient dynamically. The simulation results show that our proposed Fuzzy-PF TAN system is robust under various current disturbance situations. The position error of our system is within a map resolution unit of 500 m.

In this chapter, the receding horizon Kalman filter is applied to underwater navigation systems. The ocean covers about two-thirds of the earth and has a great effect on human beings. However, the ocean is overlooked while we focus our attention on land and atmospheric issues; we have not been able to explore the full depths of the ocean, its abundant livings and non-living resources. For example, only recently we have discovered, by using manned submersibles, that a large amount of methane and carbon dioxide comes from the seafloor and extraordinary groups of organisms live in hydrothermal vent areas. However, a number of complex issues due to the unstructured and hazardous undersea environment make it difficult to survey in the ocean even though today’s technologies have allowed humans to land on the moon and robots to travel to Mars. Unmanned underwater vehicles (UUVs) can help us better understand marine and other environmental issues, protect the ocean resources of the earth from pollution, and efficiently utilize them for human welfare. The UUV is a platform for a variety of sensors: acoustic, magnetic, gravimetric and chemical ones. Most commercial UUVs are tethered and remotely operated, referred to as remotely operated vehicles (ROVs). Extensive use of manned submersibles and ROVs are currently limited to a few applications because of very high operational costs, operator fatigue and safety issues. The demand for advanced underwater vehicle technologies is growing and will eventually lead to fully autonomous and reliable underwater vehicles. Autonomous underwater vehicles (AUVs) were initially developed to perform missions that were not easy for ROVs and manned underwater vehicles. Since the autonomy allows AUVs to be used for risky missions such as a mine countermeasure (MCM) or under-ice operations, AUVs are replacing ROVs towed vehicles as well as manned underwater vehicles (Whitcomb, 2000). For detailed ocean surveys, an AUV acts as a more stable platform for precision sensors than ROVs or towed vehicles because an AUV is not subject to physical disturbances transmitted along the cable to the surface vessel. This absence of physical attachment also allows AUVs to measure ocean characteristics at specific depths and perform bottom-following missions as owing to its autonomy. In short, An AUV provides marine researchers with a new form of access to deeper ocean. For an AUV to successfully complete a typical survey mission, it must follow a path specified by the operator as closely as possible and arrive at a precise location for collecting data. When an AUV is not able to follow the path accurately during the mission, critical Source: Kalman Filter, Book edited by: Vedran Kordic, ISBN 978-953-307-094-0, pp. 390, May 2010, INTECH, Croatia, downloaded from SCIYO.COM

Deploying long‐range autonomous underwater vehicles (AUVs) mid‐water column in the deep ocean is one of the most challenging applications for these submersibles. Without external support and speed over the ground measurements, dead‐reckoning (DR) navigation inevitably experiences an error proportional to the mission range and the speed of the water currents. In response to this problem, a computationally feasible and low‐power terrain‐aided navigation (TAN) system is developed. A Rao‐Blackwellized Particle Filter robust to estimation divergence is designed to estimate the vehicle's position and the speed of water currents. To evaluate performance, field data from multiday AUV deployments in the Southern Ocean are used. These form a unique test case for assessing the TAN performance under extremely challenging conditions. Despite the use of a small number of low‐power sensors and a Doppler velocity log to enable TAN, the algorithm limits the localisation error to within a few hundreds of metres, as opposed to a DR error of 40 km, given a 50 m resolution bathymetric map. To evaluate further the effectiveness of the system under a varying map quality, grids of 100, 200, and 400 m resolution are generated by subsampling the original 50 m resolution map. Despite the high complexity of the navigation problem, the filter exhibits robust and relatively accurate behaviour. Given the current aim of the oceanographic community to develop maps of similar resolution, the results of this study suggest that TAN can enable AUV operations of the order of months using global bathymetric models.

The development of autonomous underwater vehicles (AUVs) for scientific, military and commercial purposes in applications such as ocean surveying, unexploded ordnance hunting and cable tracking and inspection requires the correspond ing development of navigation, guidance and control (NGC) systems, which should work in accord with each other for proper operation. Navigation systems are necessary to provide knowledge of vehicle position and attitude. The guidance systems manipulate the output of the navigation systems to generate suitable trajectories to be followed by the vehicle. This takes into account the target and any obstacles that may have been encountered during the course of a mission. The control systems are responsible for keeping the vehicle on course as specified by the guidance processor. In the Hammerhead AUV, this is achieved through manipulating the rudder and the hydroplanes (canards) of the vehicle. The need for accuracy in NGC systems is paramount. Erroneous position and attitude data in navigation systems can lead to a meaningless interpretation of the collected data, which in turn affects the accuracy of the corresponding guidance and control systems. This, if not contained properly may lead to a catastrophic failure of an AUV during a specific mission. The integrated NGC of the Hammerhead AUV is depicted.

Autonomous Underwater Vehicles (AUVs) are platforms suitable for a wide variety of applications in the marine environment with economic and operational advantages. In these applications an AUV performs a given task as a mission. During the mission execution, the AUV will move around the environment following paths that allow it to fulfill the mission's objectives. To achieve this, a reliable Navigation System (NS) is required. In addition to this, the current operating concept includes the deployment of multiple AUVs on a given area, thus a communication system between vehicles is also required. In the underwater environment both navigation and communication systems deals with the particular characteristics of the medium that limits the use of conventional techniques. In this work, a complete NS for an AUV is presented. The developed NS is based on an inertial navigation scheme with velocity and position aiding. The position aiding takes advantage of the communication system onboard the vehicle, which avoids the use of additional positioning systems. The fundamentals of the applied solutions are described and experimental results and implementation details are provided. Also conclusions and future works are presented.

Military Autonomous Underwater Vehicle (AUV) is utilized to search a mine under the sea. This paper presents design and performance verification of real-time hybrid navigation system for AUV. The navigation system uses Doppler Velocity Log (DVL) integration method to correct INS error in underwater. When the AUV is floated on the water, the accumulated error of navigation algorithm is corrected using position/velocity of GPS. The navigation algorithm is verified using 6 Degree Of Freedom (DOF) simulation, Program In the Loop Simulation (PILS). Finally, the experiments are performed in real sea environment to prove the reliability of real-time hybrid navigation algorithm.

As the exploration of marine resources continues to deepen, the utilization of Autonomous Underwater Vehicles (AUVs) for conducting marine resource surveys and underwater environmental mapping has become a common practice. In order to successfully accomplish exploration missions, AUVs require high-precision underwater navigation information as support. A Strapdown Inertial Navigation System (SINS) can provide AUVs with accurate attitude and heading information, while a Doppler Velocity Log (DVL) is capable of measuring the velocity vector of the AUVs. Therefore, the integrated SINS/DVL navigation system can furnish the necessary navigational information required by an AUV. In response to the issue of DVL being susceptible to external environmental interference, leading to reduced measurement accuracy, this paper proposes an end-to-end deep-learning approach to enhance the accuracy of DVL velocity vector measurements. The utilization of the raw measurement data from an Inertial Measurement Unit (IMU), which includes gyroscopes and accelerometers, to assist the DVL in velocity vector estimation and to refine it towards the Global Positioning System (GPS) velocity vector, compensates for the external environmental interference affecting the DVL, therefore enhancing the navigation accuracy. To evaluate the proposed method, we conducted lake experiments using SINS and DVL equipment, from which the collected data were organized into a dataset for training and assessing the model. The research results show that the DVL vector predicted by our model can achieve a maximum improvement of 69.26% in terms of root mean square error and a maximum improvement of 78.62% in terms of relative trajectory error.

Autosub is a project funded by the Natural Environment Research Council in the UK, with the aim of developing unmanned autonomous underwater vehicles to carry out long range scientific missions in the deep ocean. The navigation system is critical to the survival of the vehicle, and so it is important that appropriate software engineering methods are used. Structured analysis and design methods are useful in helping to capture the requirements of the system, and in producing a well structured design that encourages re-use of code and data. This is necessary but not sufficient. We must also address implementation issues such as multi-tasking, sharing of hardware resources, and message passing and testing. The author describes the methods that he has used in developing the navigation system and gives a personal account of the problems that he has encountered.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In recent years, terrain-aided navigation (TAN) has been rapidly developed for the localization of autonomous underwater vehicles (AUVs). TAN provides a position estimate for vehicles relative to a priori topographic map and is typically a nonlinear filtering model. Particle filtering has attracted considerable attention for addressing nonlinear and non-Gaussian challenges. However, it has problems with particle degradation and particle impoverishment. To tackle the problem in this study, the particle swarm optimization (PSO) optimization approach is employed to increase particle variety while driving particles towards the high probability zone through iterative updates. In addition, prior information is introduced to the particle fitness to reduce the interference of pseudo-peaks and increase the estimate precision. Finally, simulation tests are implemented to verify the effectiveness of the proposed method. Simulation results indicate that the improved GPSO-PF method performs better in the case of likelihood function with multiple peaks.

Underwater navigation methodology based on intelligent velocity model for standard AUV

We, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), are developing an autonomous underwater vehicle (AUV) whose main mission is monitoring a site at the sea bottom for the carbon dioxide capture and storage (CCS). The AUV cruises very near the sea bottom, and is equipped with chemical sensors in order to detect escape of CO2 from sub-bottom. Of course, the position information of the AUV is critical information for the monitoring. In this paper, a conceptual design of navigation of the AUV is described. Recently, navigation of AUV is implemented by integrating multiple navigation devices including inertial navigation system (INS), Doppler velocity log (DVL), depth sensor, acoustic navigation system, and others. The AUV under construction will be equipped with these navigation sensors, and will integrate those sensors’ outputs to navigate herself. In order to measure the absolute position of the AUV the acoustic method is one of fundamental technique. At the first step of development of the AUV, three acoustic methods are considered to adopt. The three methods are super short baseline (SSBL) method which is a tracking from support ship or other surface station, long baseline (LBL) which is navigation based on preplaced acoustic transponders, and virtual LBL (VLBL) which is navigation based on only single transponder. These acoustic methods are integrated with the navigation result of INS, depth sensor, and DVL. The three methods are used in each appropriate case. Which feature of observation is desired simplicity, accuracy, or independence from support ship and time efficiency? The acoustic method is influenced by environment, and also output of other sensors is depending on the environment, for example the DVL miss the data when the terrain is with many up-hills and down-hills. The integration or filtering parameters of the navigation should be adjusted depending on the influential environmental factor.

This article presents a comparison of different path-planning algorithms for autonomous underwater vehicles using terrain-aided navigation. Four different path-planning methods are discussed: the genetic algorithm, the A* algorithm, the rapidly exploring random tree* algorithm, and the ant colony algorithm. The goal of this article is to compare the four methods to determine how to obtain better positioning accuracy when using terrain-aided navigation as a means of navigation. Each algorithm combines terrain complexity to comprehensively consider the motion characteristics of the autonomous underwater vehicles, giving reachable path between the start and end points. Terrain-aided navigation overcomes the challenges of underwater domain, such as visual distortion and radio frequency signal attenuation, which make landmark-based localization infeasible. The path-planning algorithms improve the terrain-aided navigation positioning accuracy by considering terrain complexity. To evaluate the four algorithms, we designed simulation experiments that use real-word seabed bathymetry data. The results of autonomous underwater vehicle navigation by terrain-aided navigation in these four cases are obtained and analyzed.

Precise navigation of autonomous underwater vehicles (AUV) is one of the most important challenges for marine applications. Current 2-dimensional (2D) navigation systems (algorithms) are effective to autonomous surface vehicles (ASV) operations, but still have some limitations for AUV navigation in 3D underwater environment. In this paper, a 3D range navigation algorithm is proposed that is potentially useful for small AUVs that have severe volume and power constraints. This algorithm enables an AUV to compute its trajectory in the presence of unknown currents and simultaneously estimate the currents by using range measurements from a single known location (beacon). The principal work here is the observability analysis of the proposed navigation system that characterizes observable AUV trajectories using novel approaches towards uniform observability of linear time varying systems. Then, implementation of the 3D range navigation algorithm as a discrete time extended Kalman filter is given based on the observability results and kinematic model of the system. Finally, a set of comparative simulation results verify the validity of the navigation algorithm and the system's observability related to trajectories.

AUV (autonomous underwater vehicles) are required to have long-term and high-precision positioning capability relative to seabed targets in most deep-sea exploration tasks. However, acoustic positioning error is positively correlated with its operating range and inertial navigation has inevitable accumulated time errors, neither of which provide precise AUV positions. TAN (terrain aided navigation) directly calculates the AUV position to the seabed terrain coordinate system by tracking the seabed topographic characteristics, which can guide the AUV to seabed target accurately. However, the initial TAN positioning error will increase with the AUV operation depth, which causes a large PF (particle filter) initialization error and particle coverage interval, and will affect the convergence and stability of TAN. To solve this problem, we first propose a TAP (terrain aided position) confidence interval model. We then use the confidence interval to constrain the initial particles to a smaller range. Finally, the validity of the algorithm is verified by playback simulation with ship-borne multi-beam sonar sensor measured data. The results show that the TAP confidence interval can reduce the coverage of the initial particle, and can improve the convergence speed and filtering accuracy of the TAN.