Research and Implementation on Multi-beacon Aided AUV Integrated Navigation Algorithm Based on UKF

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.

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).

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.

Near underwater structures or the surface of shallow water, acoustic sensing based navigation of autonomous underwater vehicles (AUVs) suffers from inaccurate positioning which is caused by acoustic sensing such as multi-pass and noisy data. In the paper a new underwater navigation method for AUVs is proposed based on artificial underwater landmarks which are recognized by a vision system implemented in the robot. An underwater image processing strategy and an underwater vision environment analysis method are introduced to improve reliability of the underwater vision system. Experiments were carried out to demonstrate the efficiency of the proposed navigation method of AUVs.

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.

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.

One-way-travel-time (OWTT) acoustic ranging has received considerable attention as improvements to acoustic modems and electronic clocks have made it a feasible navigation tool. This paper reports the results of simulations investigating the effect of utilizing inter-vehicle ranging for autonomous underwater vehicle (AUV) navigation. In these simulations, a fleet of AUVs operates in shallow water with a pair of fixed transponders. A rigid timing cycle for acoustic communications was implemented with a message queuing approach to simulate the handicaps of underwater acoustic communication. Furthermore, a simple path following algorithm was used to navigate the AUVs through a waypoint course, and a kinematic motion model was used to simulate AUV movement. The position of each vehicle in the fleet was estimated independently by combining the propagation steps of an extended Kalman filter with the update equations of an extended information filter. Results of the simulations showed the addition of inter-vehicle ranging improved accuracy by 1–2 cm when navigating using four fixed transponders and by 9–24 cm when navigating using two fixed transponders.

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.

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

Single-beacon navigation for group autonomous underwater vehicles (AUVs) suffers from long navigation period and asynchronization. This paper addresses the problem by introducing the single-input-multiple-output (SIMO) model, where, a single AUV transmits the signal and other AUVs receive the signal. Utilizing the time-delay of signals from the beacon and the sender, the navigation for all AUVs is achieved simultaneously, and the navigation period is dramatically decreased. Moreover, based on the new SIMO model, a tracking method utilizing extended Kalman filter is also put forward to increase the navigation precision and reduce the computational burden. Finally, a coordinate fusion algorithm based on the location, error, and time-delay is presented, which fuses the independent AUV coordinates to be a higher precision holistic coordinate system for group AUVs. Simulation results show the effectiveness of the proposed method.

With the increasing exploration of the marine resources, cooperative navigation (CN) of multi autonomous underwater vehicles (AUVs) can improve exploration efficiency and accuracy. In order to improve the positioning accuracy in the CN, we propose the Information Entropy (IE) algorithm, which can evaluate the measurement informations, and choosing the optimal measurement informations to update the positions of the AUVs, decreasing the influences of the low quality measurement informations to the CN. In this paper, we study the influences of choosing different AUVs' states and the amounts of the measurement informations, and we consider two scenarios, the first is that AUVs equip the equal accuracy navigation equipment; the second is that AUVs equip the different accuracy navigation equipment, which there are several AUVs have the high accuracy equipment, and the others have the low accuracy equipment. Combining with the nonlinear filtering technique, we find using the IE algorithm can reduce the positioning errors of the AUVs compared with selecting measurement informations randomly by the simulation results.

In this paper, we propose a state estimation method for navigation of multiple Autonomous Underwater Vehicles (AUVs) with limited communication traffic. Several positioning methods for a single hovering type AUV have been proposed in order to achieve different kind of missions. We had proposed a navigation method of multiple AUVs aimed at unexplored and wide seafloor surveys. In this method, the AUVs need to share their estimated states. However, typical data rates of acoustic communications in underwater environments are too small to share the states with each other constantly. In order to overcome the problem, we propose a state estimation method where the stochastic state estimator “Particle Filter” is applied to multiple AUVs. The information about the estimated states is approximated by “Particle Clustering” using a clustering method (k-means) and a model evaluation method by AIC (Akaike Information Criterion) to select the optimal size of clusters and reduce the required communication data size. Through simulations, the proposed method succeeded in stable positioning and reduction of the data size required to share.

A novel reinforcement learning based tuna swarm optimization algorithm for autonomous underwater vehicle path planning

This paper reports a preliminary study for use of a fully dynamic vehicle process model in combined underwater communication and navigation (cooperative navigation) of underwater vehicles equipped with an acoustic modem, attitude, and depth sensors, but lacking a Doppler velocity log (DVL), and a surface vehicle equipped with an acoustic modem and GPS. We report both simulation and at-sea experimental trials with the JHU Iver3 autonomous underwater vehicle (AUV). The case of underwater vehicle navigation without a DVL is of interest in several use-cases including (a) small and low-cost underwater vehicles for which DVLs may be impractical or infeasible due to their size and cost and (b) for missions in which the vehicle's altitude above the sea floor (or depth beneath overhead ice) exceeds the DVL acoustic bottom-lock range. To the best of our knowledge, all previous studies on cooperative navigation have reported use of a kinematic process model, which works well in the presence of frequent, high-accuracy velocity measurements, as is the case when the vehicle is equipped with a DVL. This preliminary study suggests that the dynamical process model may offer a significant advantage over the purely kinematic model in the absence of frequent, high-accuracy velocity measurements, as is the case when the submerged vehicle is not equipped with a DVL.

Bathymetric terrain maps generated from acoustic data offer an attractive alternative for reducing the submerged pose error estimates for autonomous underwater vehicles (AUVs). The goal of this work is to determine the extent of improvement in the navigational accuracy of an AUV equipped with an echo sounder for near-seafloor, shallow water applications. Given bathymetric variations of a certain terrain, this paper analyzes the best achievable positioning accuracy for AUVs. To counter for the strong non-linearity and the non-Gaussian nature of the problem, an optimal Bayesian estimator is initially derived. The fundamental limitations in the pose uncertainty using this approach is encompassed by the Posterior Cramer-Rao bound (PCRB), that is interpreted in terms of the sonar sensor accuracy and the bathymetric variations. The PCRB on the position error covariance is determined and it is shown that the Bayesian Bootstrap filter closely follows this bound using real inferometric sonar data.