Low-Altitude and High-Speed Terrain Tracking Method for Lightweight 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.

The Monterey Bay Aquarium Institute (MBARI) has conducted high-resolution multibeam bathymetry, sidescan, and subbottom sonar surveys in the Gulf of California using an autonomous underwater vehicle (AUV) as part of a three-month, two-ship oceanographic expedition. The seafloor mapping AUV D. Allan B. was operated from R/V Zephyr, achieving 22 successful 17-hour missions during March and April 2012. The 1-m lateral resolution seafloor maps and subbottom profiles were used to guide subsequent ROV Doc Ricketts dives made from MBARI's R/V Western Flyer, which combined visual surveys, sampling, and in situ experimentation by several different science groups. The vehicle is a modularized Dorado Class 54 cm (21 inch) diameter, 5.18 meter (17 feet) length, 733 kg (1616 pounds) air weight. The interior of the AUV floods with water when launched; electronics are contained in either one-atmosphere hermetic housings or in pressure-compensated enclosures. The AUV is ballasted to be about 3.63 kg (8 pounds) positive in seawater. Flotation is accomplished with 6000 meter rated syntactic foam. The D. Allan B. has three mapping sonar systems onboard: a Reson 200 kHz multibeam echosounder, an Edgetech FS-AU Sonar Package which comprises a 100 kHz chirp sidescan, a 410 kHz chirp sidescan, and a 2-16 kHz chirp subbottom profiler. With the onboard Lithium Polymer battery packs, the Mapping AUV has an endurance of approximately 17 hours at a speed of 1.5 meters per second, providing a range of 55 to 85 kilometers. The mapping AUV is equipped with a Kearfott inertial navigation system (INS) with Doppler velocity log, a Paroscientific pressure sensor, and an ultrashort baseline (USBL) and acoustic modem for communications. It also has an Iridium Satellite Short Burst Data modem, a drop weight, radio direction finder, Homer Pro and visible strobe location aids. Propulsion is accomplished with a duct-protected articulated propeller which provides 52 Newtons (12lbf) of thrust at 300 rpm as well as elevator and rudder functions. Turning diameter is less than 20 meters; climb/dive rate is approximately 30 meters per minute. Launches and recoveries were made from the RV Zephyr, a 24m (79 foot) former pilot boat that operates with four crew and four science personnel. The Zephyr is outfitted with a Brook Ocean Technologies J-Frame crane for AUV launch and recovery. Once the AUV is in the water, the mission is sent from a computer on the Zephyr to the AUV over a wireless Freewave Ethernet link. At the start of the mission, the AUV spirals down to the start-of-line while being position-updated with GPS data from the Zephyr aided by the USBL system. A Bottom Pressure Recorder used to provide in situ tide data for bathymetry correction was deployed prior to all AUV missions, and recovered afterwards, using a Benthos Recoverable Transponder mounted to an MBARI-designed frame. The seafloor mapping data were processed using MB-System, an open source software package developed at MBARI and the Lamont-Doherty Earth Observatory. The first half of the mapping AUV surveys focused largely on four transform faults in the central and northern gulf. The combination of high-resolution bathymetry and subbottom profiles enabled the identification of active fault traces and zones of subsurface gas and hydrate. The second half of the Zephyr expedition focused on the Alarcon Rise, a volcanic spreading center. The new surveys provide the first 1-m scale bathymetry coverage of the neovolcanic zone along an entire ocean spreading center segment. The mapping data also identified the locations of several fields of hydrothermal vent chimneys, two of which were confirmed to be currently active by ROV dives.

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.

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.

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.

Autonomous underwater vehicles (AUV) have a wide variety of applications in the marine domain, including exploration, surveying, and mapping. Their navigation systems rely heavily on fusing data from inertial sensors and a Doppler velocity log (DVL), typically via nonlinear filtering. The DVL estimates the AUV's velocity vector by transmitting acoustic beams to the seabed and analyzing the Doppler shift from the reflected signals. However, due to environmental challenges, DVL beams can deflect or fail in real-world settings, causing signal outages. In such cases, the AUV relies solely on inertial data, leading to accumulated navigation errors and mission terminations. To cope with these outages, we adopted ST-BeamsNet, a deep learning approach that uses inertial readings and prior DVL data to estimate AUV velocity during isolated outages. In this work, we extend ST-BeamsNet to address prolonged DVL outages and evaluate its impact within an extended Kalman filter framework. Experiments demonstrate that the proposed framework improves velocity RMSE by up to 63% and reduces final position error by up to 95% compared to pure inertial navigation. This is in scenarios involving up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{5 0}$</tex> seconds of complete DVL outage.

Autonomous underwater vehicles (AUVs) are used to execute an increasingly challenging set of missions in commercial, environmental and defense industries. The resources available to the AUV in service of these missions are typically a limited power supply and onboard sensing of its local environment. Optimal path planning is needed to maximize the chances that these AUVs will successfully complete long-endurance missions within their power budget. A time-optimal path planner has been recently developed to minimize AUV mission time required to traverse a dynamic ocean environment at a specified speed through the water. For many missions, time minimization is appropriate because the AUVs operate at a fixed propeller speed. However, the ultimate limiting constraint on AUV operations is often the onboard power supply, rather than mission time. While an empirical or theoretical relationship between mission time and power could be applied to estimate power usage in the path planner, the real power usage and availability on an AUV varies mission-to-mission, as a result of multiple factors, including vehicle buoyancy, battery charge cycle, fin configuration, and water type or quality. In this work, we use data collected from two mid-size AUVs operating in various conditions to learn the mission-to-mission variability in the power budget so that it could be incorporated into the mission planner.

Underwater navigation methodology based on intelligent velocity model for standard AUV

Recent development and commercialization of relatively portable and inexpensive autonomous underwater vehicles (AUVs) have led to new methods for making hydrodynamic, chemical, and biological measurements in near-coastal, lake, and estuarine systems. Equipped with Doppler velocity log (DVL) technology to allow for deadreckoning navigation, this new class of AUVs has the potential to log not only vehicle motions, but water velocities as well. In this article, we assess the performance of the DVL acoustic Doppler current profiler (ADCP) equipped on the REMUS AUV (Hydroid) in three different environments: a tidally forced lake; a wave-forced, near-shore fringing coral reef; and a gently sloping continental shelf. In all three data sets, the water velocities measured by the AUV DVL compare favorably with measurements from nearby stationary acoustic Doppler current profilers. Nevertheless, the water velocity component measured parallel to the vehicle's tracks exhibited a bias in the direction of AUV motion, consistent with a previously reported bias found for the ship-mounted ADCP application in low-scattering environments. The fringing reef data suggest that DVLs are capable of partially resolving wave motions.

A Doppler velocity log (DVL) provides a three-dimensional representation of the velocity of an underwater vehicle to the vehicle’s DVL-aided inertial navigation system (INS). It is a key element in determining the precision of the INS. This paper presents an algorithm for reducing the error in the DVL as a way of improving the navigation of an autonomous underwater vehicle (AUV). We also investigate and analyze the effect that the seafloor’s deviations have on DVL measurement error. Finally, the performance of the proposed algorithm is examined through both AUV simulation and experimentally. The results show that the proposed technique can indeed effectively correct DVL errors.

Research on the influence of balance weight parameters on the motion performance of the seafloor mapping AUV in vertical plane

Conventional underwater pipeline inspections typically utilize Remotely Operated Vehicles (ROVs) tethered to Dynamic Positioning Class 2 (DP2) vessels, which entail significant expenses. To reduce these expenses, alternative inspection approaches have been explored. The Robotic Autonomous Underwater Vehicle (AUV) offers a cost-effective alternative by eliminating the need for a DP2 vessel and can instead be deployed using a more economical DP1 vessel, pioneering the underwater pipeline inspection. This game-changing Robotic AUV with Electric Field Gradient Sensor (EFGS) integration using a DP1 vessel can reduce inspection costs by up to 55% compared to using a ROV with a DP2 vessel. Additionally, it can reduce inspection durations from 19 days to just 7 days (a 60% reduction), significantly lowering the cost and carbon footprint due to the shorter execution time. The development of the AUV technology integrated with EFGS was a collaborative effort between PETRONAS and AUV developer, ROVULA. This innovative technology has successfully completed its pilot deployment in November 2024, inspecting a 30″ pipeline over a total length of 155 km offshore Sarawak. The Robotic AUV can achieve superior inspection coverage compared to conventional ROV methods and accomplishes the task in less time. This advantage is due to the untethered operation of the lightweight AUV, which allows it to move twice as fast as the ROV and eliminates the need for physical contact with the pipeline. The EFGS technology performs non-contact cathodic protection (CP) measurements. Unlike the conventional ROV CP stab method, which requires the ROV to frequently stop and stab the CP probe onto the anodes and bare metal (if exposed), the AUV with EFGS moves along the pipeline without stoppage. It accurately measures the field gradient along the entire pipeline without contacting the anodes or pipeline, even if it is buried.

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.

International audience

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.