Research on position correction method for AUV large depth navigation based on ranging positioning

Achieving accurate navigation and localization is crucial for Autonomous Underwater Vehicle (AUV). Traditional navigation algorithms, such as Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), require the system model and measurement model for state estimation to obtain the AUV position. However, this may introduce modeling errors and state estimation errors which will affect the final precision of AUV navigation system to a certain extent. To avoid these problems, in this paper, we proposed a deep framework - NavNet - by taking AUV navigation as a deep sequential learning problem. Firstly, the proposed NavNet can take raw sensor data at different frequencies as input, which benefits from the sequential learning capability of Recurrent Neural Network (RNN). Secondly, NavNet takes advantage of a simplified attention mechanism and Fully Connected (FC) layers to output AUV displacements per unit time, which accomplishes low-frequency AUV navigation by accumulation of it. More importantly, there is no need for the model building and state estimation with NavNet, which avoids the import of relevant errors. We compare the performance of NavNet to EKF and UKF using collected data by running Sailfish in the sea. Experimental results show that NavNet has an excellent performance in terms of both the navigation accuracy and fault tolerance. In addition, a reliable fusion strategy of NavNet and conventional method is applied to achieve high-frequency AUV navigation. The experimental results show that the proposed architecture can be a reliable supplement to limit the error growth of conventional algorithms.

In recent years, precious observations by autonomous underwater vehicle (AUV) have been performed. Tracking and navigation of AUV are important for precious observations. In Japan agency for Marine-earth Science and Technology (JAMSTEC) the observations with AUV ldquoURASHIMArdquo have been implemented in deep ocean area. AUV URASHIMA is navigated by an inertial navigation system (INS) mainly. The absolute position of AUV is tracked on the support vessel with super short base-line (SSBL) method of acoustic positioning. And sometime the own position in AUV is updated by the SSBL estimation result to correct the drift error of INS. However the SSBL is not so accurate. So the position correction includes the SSBL error and the position correction is performed drastically. In this paper, a method for accurate tracking of AUV and rapid correction of the INS status is proposed. The tracking is mainly achieved by SSBL on the support vessel. Additionally the INS data is transmitted from AUV including the acoustic positioning signal. The INS output of relative position to past own position is very accurate within short time. The INS data is used for tracking. Furthermore the position result estimated on the vessel is transmitted to AUV with next interrogation signal. And the own position in AUV is rapidly updated with accurate estimation. Some digital data including INS data is modulated with phase shift keying (PSK) method and direct sequence spread spectrum (DSSS) technique for transmission with acoustic positioning signal. The proposed method was considered by simulation. And good results were obtained.

The primary objective of this work is to solve the problem that the long baseline (LBL) positioning system has poor real-time positioning results during the navigation for autonomous underwater vehicles (AUVs). In this paper, it is proposed to use the unscented Kalman filter (UKF) to enhance the navigation and positioning accuracy of the AUV, using the speed information measured by the Doppler Velocity Log (DVL) and the ranging information obtained by the acoustic beacons. Utilizing the unscented transformation to perform integrated navigation, it can avoid linearization truncation errors and Jacobi matrix computation compared with extended Kalman filtering (EKF). The experimental results verify that in the multi-beacon aided AUV integrated navigation system, compared with the conventional LBL and EKF, UKF provides more stable and accurate position information in real time.

The objective of this thesis is to contribute to Autonomous Underwater Vehicle (AUV) navigation by improving vehicle localisation accuracy when Doppler Velocity Log (DVL) bottom-tracking is unavailable. The Inertial Navigation System (INS) based localisation solution is prone to extreme uncertainties due to double integration of inherent errors within the INS acceleration measurements, unless the solution being externally aided (e.g. velocity measurements using the DVL bottom-track). As a solution for this, an improved model-aided INS localisation technique is introduced, which is complimented with the development of a novel model calibration and new water column velocity estimation method. The techniques established in this project are tested and validated using experimental data from a set of field manoeuvres using a <i>Gavia</i> class AUV and the performance is compared against other commonly used localisation methods. A baseline mathematical model was developed in this work using system identification to predict the motion response of the AUV based on its control commands. However, such models are generally calibrated for low water column velocities and a standard vehicle configuration, and are limited in application for variations in environmental conditions. To address this limitation, a novel model calibration technique was established to field calibrate the parameters within the baseline model to the current operating condition and vehicle configuration. Model calibration improved the results of the baseline model up to 73% when operating in low energy environments and the AUV position can be computed within an uncertainty range of 1.5% of the distance travelled. In comparison, uncertainties of conventional non-bottom-tracking localisation techniques could be up to 10% in similar environmental conditions. A secondary approach is also presented to determine the hydrodynamic coefficients of the mathematical model using Computational Fluid Dynamics (CFD) simulations and captive model experiments when the AUV is operating in complex flow conditions. A non-acoustic method was introduced to estimate the velocity of the water column using the motion response of the AUV. This method is capable of accurately estimating water column velocities in proximity to the AUV (i.e., the water column velocities at the same depth as the vehicle is), which is not typically resolved with existing methods such as acoustic Doppler current profilers. When the mathematical model-aided localisation solution is complimented with water column velocity prediction method, the localisation error is limited to less than 6% of the distance travelled even in extremely high currents (i.e. &gt;2 m s-1 in tested conditions). In such environmental conditions, the uncertainties of other commonly used non-bottom-tracking localisation methods, as tested against in this work, such as DVL water-track mode and unaided INS could be above 30%. One of the key advantages of the proposed localisation technique is that it could be applied to any torpedo shaped AUV (for example, platforms such as <i>REMUS, Iver, Bluefin, Explorer</i>, etc.) of any configuration by simply conducting a set of established field manoeuvres to identify its mathematical model parameters. Further, additional sensors beyond a typical AUV navigational payload (i.e. global positioning, accelerometers and gyroscopes) are not required to implement this technique in an AUV. The localisation technique developed in this thesis is capable of improving the motion control and navigation solution of the AUV in the absence of DVL bottom-tracking, which is critical for the expansion of vehicle performance in extreme environmental conditions.

Hybrid model navigation method for autonomous underwater vehicle

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

The stochastic nature of noise signals affects the vehicle’s internal states and the outputs, resulting in the difficulty in estimation. The unknown or time-varying nature of noise signals if not taken into account for estimation, the results will diverge and be highly deteriorated. In this paper, a maximum likelihood principle (MLP) based adaptive robust extended kalman filter for estimating the states of the adopted non-linear vehicle model is proposed. An observability test is done for the purpose of estimation. A covariance matching (CM) based robust adaptive high forgetting factor based fault tolerant technique is also employed on the robust adaptive extended and unscented kalman filters for comparison purpose. The Robustness of the filter is analyzed by varying the parameter of the vehicle through a local sensitivity analysis. The results show that the MLP based approach to the extended kalman filter performs well in three simulations for sinusoidal steering, Double Lance Change, J-Turn, Fishhook, Slalom maneuver in comparison to robust adaptive unscented kalman filter. Friction coefficient of 0.8 (dry road) and 0.4 (wet road) is chosen for the simulation. The sideslip angle RMSE value for MLP based estimation is obtained as 2.62e-05, 4.545e-06 for Sine and DLC maneuver.

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.

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.

This paper addresses the region tracking control problem for an autonomous underwater vehicle (AUV) and proposes a prediction-based region tracking control (PRTC) scheme for AUV. In the PRTC scheme, the idea of prediction is adopted to solve the problems of overshoot and high energy consumption due to the lack of consideration of the large inertia of the AUV in the traditional scheme. The PRTC scheme predicts the future position of AUV through the past time-series position of AUV and the outer boundary of the desired region, and then designs the controller depending on the predicted results. Furthermore, the relationship between the desired region and the control output of the proposed PRTC scheme is studied. It is found that its control output amplitude is susceptible to the desired region range, resulting in output saturation. Therefore, this paper proposes a control law optimization scheme considering the desired region. This optimization scheme modifies the error signal in the control law of the PRTC scheme so that it is only related to the relative position of the desired region where the AUV is located. Finally, the proposed schemes are applied on the ODIN AUV, and the simulation results verify the feasibility of the proposed schemes.

Autonomous underwater vehicle (AUV) navigation and localization in underwater environments is particularly challenging due to the rapid attenuation of Global Positioning System (GPS) and radio-frequency signals. Underwater communications are low bandwidth and unreliable, and there is no access to a global positioning system. Past approaches to solve the AUV localization problem have employed expensive inertial sensors, used installed beacons in the region of interest, or required periodic surfacing of the AUV. While these methods are useful, their performance is fundamentally limited. Advances in underwater communications and the application of simultaneous localization and mapping (SLAM) technology to the underwater realm have yielded new possibilities in the field. This paper presents a review of the state of the art of AUV navigation and localization, as well as a description of some of the more commonly used methods. In addition, we highlight areas of future research potential.

This paper focuses on mobile positioning for autonomous underwater vehicle (AUV) in complex underwater environments. The positioning algorithm based on time of arrival (TOA) assumes that the underwater acoustic communication between AUV and underwater sensors is real-time and reliable. The distance between the AUV and underwater sensors can be obtained through the time delay of acoustic signal transmission. However, the uncertain and complex nature of the underwater environment can cause underwater sensors to fail to receive acoustic signals from the AUV reliably. This leads to an insufficient effective observation data in the positioning solution process, causing underdetermined problems in the solution equation, and thus making it impossible to solve the AUV’s position information stably in real time. To overcome this problem, we frist established a positioning model considering the navigation velocity of the AUV. Based on this Model, we then proposed a two-step weighted least squares algorithm combined with a fast horizontal distance model for the deep sea (TWLS-HD-V). Furthermore, we derived the Cram´er-Rao Lower Bound (CRLB) of the proposed model and compared it with that of the basic model. Finally, the effectiveness of the proposed algorithm was tested on both numerical simulations and deep-sea data. In conclusion, this algorithm effectively overcomes two key challenges in complex underwater environments: sound line bending and insufficient time delay information. As a result, it improves AUV positioning accuracy and ensures reliable positioning trajectories.

In the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) of non-linear system, the mathematical model and the noise statistics are essential; this limitation was settled by adaptive algorithm. The adaptive extended Kalman filter (AEKF) and adaptive unscented filter (AUKF) were proposed to solve the filtering problem of the system with unknown mathematical model or noise statistics in information fusion. The simulation result shows that the adaptive algorithm in EKF and UKF can improve the performance of filter and decrease the computing time.

In this paper, an adaptive unscented Kalman filter (AUKF) is developed for state estimation on a tractor-trailer system. For nonlinear systems, the unscented Kalman filter (UKF) is a well-known approach for observer design. Some research has shown the UKF can be more accurate than extended Kalman filter (EKF) with similar computation burden. However, the performance of traditional Kalman filter may deteriorate when process noise changes or sudden disturbance occurs due to the fixed process noise covariance (Q matrix) in the filter. An adaptive gain is calculated to compensate for the mismatch between the actual residual covariance and the deduced value from the filter, ensuring that the sequence of residual is uncorrelated. An estimated Q matrix is referred in the adaptive gain calculation at each sample. Simulation is done in various situations. The positions of trailer are used as measurements. Compared with the standard UKF (SUKF) and another adaptive UKF, the proposed algorithm has better result.

Autonomous underwater vehicle (AUV) navigation and localization is crucial and challenging in Global Positioning System-denied environments. AUV carrying synthetic aperture sonars (SASs) can provide precise motion estimation of the platforms. This study focuses on enhancing the performance of AUV localization using dual-sided SASs. The two-dimensional velocities of AUV are estimated using dual-sided SAS echoes. An extended Kalman filter (EKF) is then adopted for dead reckoning using the estimated velocities. The experimental results demonstrate that the proposed method has better localization accuracy than that of the Doppler velocity log (DVL).