AUV Adaptive Sampling Methods: A Review

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.

This paper proposes a new method for cruising-type autonomous underwater vehicles (AUVs) to track rough seafloors at low altitudes while also maintaining a high surge velocity. Low altitudes are required for visual observation of the seafloor. The operation of AUVs at low altitudes and high surge velocities permits rapid seafloor imaging over a wide area. This method works without high-grade sensors, such as inertial navigation systems (INS), Doppler velocity logs (DVL), or multi-beam sonars, and it can be implemented in lightweight AUVs. The seafloor position is estimated based on a reflection intensity map defined on a vertical plane, using measurements from scanning sonar and basic sensors of depth, attitude, and surge velocity. Then, based on the potential method, a reference pitch angle is generated that allows the AUV to follow the seafloor at a constant altitude. This method was implemented in the AUV HATTORI, and a series of sea experiments were carried out to evaluate its performance. HATTORI (Highly Agile Terrain Tracker for Ocean Research and Investigation) is a lightweight and low-cost testbed designed for rapid and efficient imaging of rugged seafloors, such as those containing coral reefs. The vehicle succeeded in following a rocky terrain at an altitude of approximately 2 m with a surge velocity of approximately 0.8 m/s. This paper also presents the results of sea trials conducted at Ishigaki Island in 2017, where the vehicle succeeded in surveying the irregular, coral-covered seafloor.

Path planning plays an extremely important role in the operation of Autonomous Underwater Vehicles (AUVs) to accomplish the underwater task safely, successfully and efficiently. This paper introduces the Fast Marching method for the path planning of AUVs in 3D large-scale battlefield environments to solve the problem that AUVs require low probability of being found, navigation security and low fuel consumption. The algorithm is able to find the optimal path between two waypoints in the work space and comprehensively takes factors such as the risk of collision with obstacles and mine, detection probability, sailing depth and path length into account. It also considers the maneuverability constraints of the AUV, including the safety depth and turning radius, to obtain the final flyable path. Finally, the proposed algorithm is tested in a large-scale area containing underwater terrain elevation map, sonar and mine density map to evaluate its feasibility, stability and efficiency.

To achieve efficient and cost-effective sensing coverage of the vast under-sampled 3D aquatic volume, intelligent adaptive sampling strategies involving a team of Autonomous Underwater Vehicles (AUVs) endowed with underwater wireless communication capabilities become essential. Given a 3D field of interest to sample, the AUVs should coordinate to take measurements using minimal resources (time or energy) in order to reconstruct the field at an onshore station with admissible error. A novel distributed adaptive sampling solution that can minimize the sampling cost (in terms of time or energy expenditure) is proposed along with underwater acoustic communication protocols that facilitate the coordination of the vehicles. The proposed solution operates in two distinct phases in which it employs random compressive sensing (Phase I) and adaptive sampling (Phase II). Phase I captures the spatial distribution of the field of interest while Phase II tracks the temporal variation of the same. A distributed framework for multi-vehicle adaptive sampling that facilitates the movement of data between AUVs and enables compute intensive adaptive sampling algorithms is proposed. Simulation results on real data traces show that the proposed adaptive sampling solution significantly outperforms existing solutions in terms of reconstruction accuracy and energy expenditure.

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.

Autonomous Underwater Vehicle (AUV) technology has matured over the past few decades but commercial AUVs today remain complex, proprietary and expensive. Modularity in AUVs at a software, electronics and mechanical level allows users to configure AUVs for specific missions by only including the required components. With multiple base AUVs, users may easily configure heterogeneous teams of AUVs for collaborative missions. Modular AUVs are also easier to maintain. We expect that open-architecture AUVs with open software/hardware interfaces, changeable modules and open source components will become widely available in the future. However AUV configuration management and module compatibility are issues that arise with modularity. An initiative at the Acoustic Research Laboratory (ARL) of the National University of Singapore (NUS) has yielded an open- architecture collaborative prototype AUV known as STARFISH. The software components in this AUV are based on the DSAAV architecture. DSAAV has been designed ground up with modular AUVs in mind. In a DSAAV compliant AUV, each module provides a uniform software interface that other AUV modules can access. This interface allows configuration of the module, logging of critical information, discovery of services, access to sensor & actuator services, health monitoring and automated software update functionality. The interface is rich in functionality, yet light weight and portable to ensure that even low power micro-controllers can easily implement it. DSAAV can be implemented on any underlying communication backbone such as Ethernet, UDP/IP, etc. The software components running under DSAAV are independent of the underlying communication backbone and function without change in various AUVs and simulation environments. In this paper, we describe the basic philosophy and concepts behind DSAAV. We also outline the Application Programming Interface (API) for DSAAV compliant systems and describe its key functionality. It is our hope that DSAAV will be adopted and extended by other AUVs in the future.

TOUCH is a real-time computer vision system that has been developed in order to aid the operation of autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). The algorithm is based on a modified 4-connectivity approach that scans the incoming frames for a target hue range and based upon the largest connected blobs of hue, tracks an object and provides the two dimensional Cartesian coordinates of the desired object. Since few algorithms exist for tracking and recognizing objects in underwater environments, this algorithm provides a way for AUVs and ROVs to acquire, track or recognize objects by using a relatively fast and inexpensive vision system. This paper presents the details on TOUCH, its testing, some areas that warrant improvement and possible course of action for the future.

High accuracy and reliable navigation in the underwater environment is very critical for the operations of autonomous underwater vehicles (AUVs). This paper proposes an adaptive federated interacting multiple model (IMM) filter, which combines adaptive federated filter and IMM algorithm for AUV in complex underwater environments. Based on the performance of each local system, the information sharing coefficient of the adaptive federated IMM filter is adaptively determined. Meanwhile, the adaptive federated IMM filter designs different models for each local system. When the external disturbances change, the model of each local system can switch in real-time. Furthermore, an AUV integrated navigation system model is constructed, which includes the dynamic model of the system error and the measurement models of strapdown inertial navigation system/Doppler velocity log (SINS/DVL) and SINS/terrain aided navigation (SINS/TAN). The integrated navigation experiments demonstrate that the proposed filter can dramatically improve the accuracy and reliability of the integrated navigation system. Additionally, it has obvious advantages compared with the federated Kalman filter and the adaptive federated Kalman filter.

The operation of Autonomous Underwater Vehicles (AUVs) within underwater sensor network fields provides an opportunity to reuse the network infrastructure for long baseline localisation of the AUV. Computationally efficient localisation can be accomplished using off-the-shelf hardware that is comparatively inexpensive and which could already be deployed in the environment for monitoring purposes. This paper describes the development of a particle filter based localisation system which is implemented onboard an AUV in real-time using ranging information obtained from an ad-hoc underwater sensor network. An experimental demonstration of this approach was conducted in a lake with results presented illustrating network communication and localisation performance.KeywordsSensor NodeCommunication RangeInertial Measurement UnitRound Trip TimeAutonomous Underwater VehicleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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

Monitoring and M A Record-Breakin Geophysical Mon Copyright 2011 b 10.1029/2011GM Following destruction of the Deepwater Horizon drilling rig, while unmitigated blowout from the Macondo well was ongoing, NOAA scientific response cruise GU-10-02 (27 May to 4 June 2010) employed coordinated ship and autonomous underwater vehicle (AUV) operations to locate and study deep hydrocarbon plumes. The ship hydrocast survey localized maximum optical signals of a deep plume, centered at ~1150 m depth, approximately 13 km southwest of the blowout. Deployed at this location, the AUV conducted a high-resolution survey of plume structure, which indicated small-scale topographic influences on plume transport. Maximum plume intensity was observed along the western slope of Biloxi Dome. The orientation of gradients in plume intensity relative to isobaths indicated flow from the dome slope onto the dome top. In terms of the relative proportions of major hydrocarbon groups, all plume samples southwest of the blowout exhibited similar composition. The chemical composition of the plume southwest of the blowout was significantly different from the composition of a weaker deep plume observed southeast of the blowout. Variation in optical signal from a colored dissolved organic matter (CDOM) fluorometer (FCDOM) explained up to 97% (median 88%) of the variance in the concentrations of individual hydrocarbon compounds. AUV data also showed that FCDOM was highly correlated with three other optical measurements (r > 0.97) and oxygen measurements (r = 0.95). The results provide unique perspective on small-scale dynamics of a deep plume and illustrate the potential for studying subsurface plumes of dispersed oil using AUVs with off-the-shelf sensors.

The authors have implemented a simple solution to mission planning for an autonomous underwater vehicle (AUV) which, together with a layered control architecture due to Zheng (1992), results in a reliable and flexible AUV control system. Extending the concept of a highly configurable control system, the solution is to provide the capability for user-defined mission scripts to specify AUV missions. Mission scripts provide configurable, high-level sequence control. The mission script language has been used in the layer-based subsumption and cooperation environment during recent sea trials of the ARCS AUV. The ARCS AUV was the vehicle used for prototype demonstration and development of the technology. The mission script language and the online mission editor which allows the operator to modify mission plans and override mission parameters during mission execution are described. >

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.

The problem of designing an accurate and reliable control for an Autonomous Underwater Vehicle (AUV), which is being subjected to environmental disturbances as well as configuration related changes, is critical in order to accomplish a successful mission. Any real-world problem solving system must deal with the issue of uncertainty, since the system's knowledge of the world is always incomplete, imprecise, and uncertain. This situation is aggravated for an AUV, due to the complex oceanic environment, and the inevitable noise of the sensory system. Some major facts that contribute to the difficulty of the underwater vehicle control are: • the dynamic behavior of the vehicle is highly nonlinear, • hydrodynamic coefficients cannot be easily obtained, hence making up uncertainties in the model knowledge, • the vehicle main body can be disturbed due to the ocean currents and vehicle motion. Therefore, it is difficult to obtain high performance by using the conventional control strategies. The control system should be able to learn and adapt itself to the changes in the dynamics of the vehicle and its environment. Many control methods have been proposed by researchers during the last decade, and there still exists a trend towards finding a better control law to achieve exponential stability while accounting for environmental changes and vehicle uncertainties. Focusing on the low level motion control of AUVs, most of the proposed control schemes take into account the uncertainty in the model by resorting to an adaptive strategy ((Corradini & Orlando, 1997), (Fossen & Sagatun, 1991a) and (Narasimhan & Singh, 2006)), or a robust approach ((Marco & Healey, 2001) and (Healey & Lienard, 1993)). In (Healey & Lienard, 1993) an estimation of the dynamic parameters of the vehicle NPS AUV Phoenix is also provided. Other relevant works on the adaptive and robust control of underwater vehicles are (Cristi & Healey, 1989), and (Cristi et al., 1990). (Leonard & Krishnaprasad, 1994) considers the control of an AUV in the event of an actuator failure. Experimental results on underwater vehicle control have been addressed by many researchers (e.g. see (Antonelli et al., 1999), (Antonelli et al., 2001), and (Zhao & Yuh, 2005)). An overview of control techniques for AUVs is reported in (Fossen, 1994). The aim of this chapter is to design a control system that would achieve perfect tracking for all configuration variables (e.g. sway and yaw motions) for any desired trajectory. To this end, we present the application of nonlinear control methods to an AUV that would lead to O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

In autonomous underwater vehicle (AUV) chemical plume tracing applications, the mission objective is to trace chemical plumes to the chemical source in a turbulent flow environment and to accurately estimate the chemical source location. AUV's capable of such feats would have applicability in searching for environmentally interesting phenomena, unexploded ordnance, undersea wreckage, and sources of hazardous chemicals or pollutants. A typical AUV chemical plume tracing system includes an adaptive mission planner (AMP) that rapidly responds to the sensor inputs to generate a trajectory for the AUV to trace the plume. Because the AUV has velocity (< 2 m/s) and heading rate (< 10 degree/s) constraints and the vehicle navigation system has navigation fixes, a guidance system is necessary for the AUV to generate heading and speed commands within the constraints to achieve the trajectory desired by the AMP. This article describes the development and field test results of the guidance system for a REMUS AUV to find a chemical plume, trace the chemical plume to its source, maneuver to reliably declare the source location, and map the plume source area after declaration of the source location. Three sets of in-water field tests with the Navy successfully demonstrated the plume tracing and post-declaration mapping capability on a REMUS AUV with the guidance system described herein