Long-range Autonomous Underwater Vehicle Research Articles

Using a long‐range autonomous underwater vehicle to find and sample harmful algal blooms in Lake Erie

AbstractCyanobacterial harmful algal blooms (CyanoHABs) in the Great Lakes pose risks to residential drinking water use, fisheries, and recreation. Active mitigation of these risks requires rapid detection of CyanoHABs and quantification of the toxins they produce. Here, we present a method of using a long‐range autonomous underwater vehicle (LRAUV) equipped with a 3rd‐generation Environmental Sample Processor (3G‐ESP) to search for and adaptively sample areas of high chlorophyll potentially representative of CyanoHAB biomass. In August 2021, this method was used in western Lake Erie. The experiment highlighted the effectiveness of the LRAUV autonomous search‐and‐sample methodology, and demonstrated how an interdisciplinary team located in different states virtually coordinated LRAUV operations and directed sampling activities via Internet connectivity using shared, web‐based situational awareness tools. The advancements made provide a foundation for future work to increase LRAUV autonomy and adaptiveness for CyanoHAB studies and monitoring in both freshwater and marine settings.

Adaptive Zigzag Mapping of a Patchy Field by a Long-Range Autonomous Underwater Vehicle

Adaptive Zigzag Mapping of a Patchy Field by a Long-Range Autonomous Underwater Vehicle

Terrain-Aided Navigation of Long-Range AUV Based on Cubature Particle Filter

Terrain-Aided Navigation of Long-Range AUV Based on Cubature Particle Filter

A robust fusion terrain-aided navigation method with a single beam echo sounder

The underwater terrain-aided navigation (TAN) method with a single beam echo sounder (SBES) can achieve long-term underwater navigation. However, due to the limited bathymetric measurements collected using a SBES, the accuracy and robustness of the TAN method are affected by a variety of factors, such as the richness of terrain features and the gradient of underwater terrain. Consequently, the SBES-TAN methods with gridded or contour priori maps cannot yield robust navigational results for long-range autonomous underwater vehicles (AUVs). To address this issue, this paper proposed a robust fusion TAN (RFTAN) method with a SBES on the basis of particle filter (PF) theory to locate a vehicle accurately. In the proposed method, both terrain positioning results provided by gridded-TAN and contour-TAN were fused to weigh a particle, with the fuzzy-theory-based fusion weight division (FWD) method. Playback experiments were conducted using 25-h-length field data collected from an at-sea experiment, and experimental results show that the proposed method can provide both robust and accurate long-term navigational results for AUVs.

Autosub Long Range 1500: A continuous 2000 km field trial

Long Range Autonomous Underwater Vehicles (LRAUVs) offer the potential to monitor the ocean at higher spatial and temporal resolutions compared to conventional ship-based techniques. The multi-week to multi-month endurance of LRAUVs enables them to operate independently of a support vessel, creating novel opportunities for ocean observation. The National Oceanography Centre’s Autosub Long Range is one of a small number of vehicles designed for a multi-month endurance. The latest iteration, Autosub Long Range 1500 (ALR1500), is a 1500 m depth-rated LRAUV developed for ocean science in coastal and shelf seas or in the epipelagic and meteorologic regions of the ocean. This paper presents the design of the ALR1500 and results from a five week continuous deployment from Plymouth, UK, to the continental shelf break and back again, a distance of approximately 2000km which consumed half of the installed energy. The LRAUV was unaccompanied throughout the mission and operated continuously beyond visual line of sight.

Sailing efficiency optimization and experimental validation of a Petrel long-range autonomous underwater vehicle

Long-range autonomous underwater vehicles (LRAUVs) generally carry limited energy during observation missions, making it critical to enhance sailing efficiency through optimization design. Therefore, it is essential to understand the relationship between the shape parameters, motion modes, and sailing efficiency. This study proposes a LRAUV energy consumption model considering the variation in seawater density and drive loss, to obtain higher accuracy. In the optimization design, a surrogate model based on the response surface method is applied to establish the functional relationship between shape parameters, sailing parameters, and sailing efficiency. According to the optimization results, the AUV mode shows better efficiency in shallow sea, while the glider mode is more suitable for deep-sea operation. For the AUV mode, the applicable working band increases with the working time at fixed depth and/but decreases with the increase of the angle of attack at fixed depth. For the glider mode, the applicable working band increases the buoyancy compensation coefficient. The hotel load has less impact on the applicable working band for both modes. Based on the proposed method, a Petrel LRAUV with an optimized shape and sailing efficiency is developed, which can also serve as a reference for the preliminary design of other LRAUVs.

Microstructure Turbulence Measurement in the Northern South China Sea from a Long-Range Hybrid AUV.

This study describes the development of a long-range hybrid autonomous underwater vehicle (AUV) for ocean turbulence measurement. It is a unique instrument, combining the characteristics of the conventional AUV and the buoyancy-driven glider, with a variety of flexible motion modes, such as cruise mode, glider mode, drift mode, and combination of multiple motion modes. The hybrid AUV was used for continuous turbulence measurement in the continental slope of the northern South China Sea in 2020. A total of ten continuous profiles were completed covering a horizontal span of 25 Km and a depth of 200 m. The hybrid AUV was operated in the combined glider and cruise mode. The hybrid AUV's flight performance was stable and satisfied the requirement for turbulence observation. The measured velocity shears from both probes were in good agreement, and the noise-reduced shear spectra were in excellent agreement with the Nasmyth spectrum. The water column in the study area was highly stratified, with a thick thermocline. The dissipation rate (ε) varied from 1.41 × 10-10 to 4.18 × 10-7 W·kg-1. In the surface mixed layer, high values of ε (10-9∼10-8 W·kg-1) were observed toward the water surface. In the thermocline, ε was 10-9.5∼10-9 W·kg-1, which was smaller than the level of the surface mixed layer. This result was mainly because of the strong "barrier"-like thermocline, which damped the transmission of wind and heat energy from the surface mixed layer to the deep layer. Overall, this study demonstrates the utility of hybrid AUVs for collecting oceanic turbulence measurements. They are a powerful addition to traditional turbulence instruments, as they make it possible to survey large areas to obtain high-quality and high-resolution data in both vertical and horizontal directions over long durations.

Guided Trajectory Filtering for Challenging Long-Range AUV Navigation

Aiming at the challenging underwater navigation of autonomous underwater vehicle (AUV), this paper develops a novel filtering method with guided trajectory. The relatively low frequency of ultra-short baseline positioning system (USBL) measurement and the noise caused by complex scenes have a significant impact on the accuracy of AUV motion estimation. Although the existing adaptive extended Kalman filter (EKF) and adaptive unscented Kalman filter (UKF) which can roughly eliminate these outliers, the filtering results of these algorithms are not smooth and have the serious degree of the jitter, and then greatly affect the local consistency and quality of topographic map seamless stitching. We exploit the relatively smooth strap-down inertial navigation system (SINS) as the guided trajectory for motion estimation, and propose a trajectory smoothness preserving filter algorithm. This system has the advantage of maintaining local gradient consistency, which not only improves the navigation accuracy, but also ensures the smoothness of the estimation system. Simulation results show that our approach has high robustness and good smoothness. The effectiveness and practicability of the method are demonstrated by real experiments in multiple deep-sea areas with an average depth of more than 1000 meters in the southwest Indian Ocean.

Lake Erie field trials to advance autonomous monitoring of cyanobacterial harmful algal blooms

Biomolecular analyses are used to investigate the dynamics of cyanobacterial harmful algal blooms (cyanoHABs), with samples collected during monitoring often analyzed by qPCR and sometimes amplicon and metagenomic sequencing. However, cyanoHAB research and monitoring programs face operational constraints due to the reliance on human resources for sample collections. To address this impediment, a third-generation Environmental Sample Processor (3G ESP) integrated with a long-range autonomous underwater vehicle (LRAUV) was tested during seasonal blooms of Microcystis in western Lake Erie (WLE) in 2018 and 2019. The LRAUV-3G ESP successfully performed flexible, autonomous sampling across a wide range of cyanoHAB conditions, and results indicated equivalency between autonomous and manual methods. No significant differences were found between LRAUV-3G ESP and manual sample collection and handling methods in the 12 parameters tested. Analyzed parameters included concentrations of total cyanobacteria and microcystin toxin gene via qPCR; relative abundances of bacterial amplicon sequence variants (ASVs) from 16S rRNA gene amplicon sequencing; and community diversity measures from both 16S amplicon and metagenomic sequencing. The LRAUV-3G ESP provided additional sampling capacity and revealed differences between field seasons for bacterial taxa and concentrations of total cyanobacteria and microcystin toxin gene. Metagenomic analysis of multiple microcystin toxin genes corroborated the use of the mcyE gene as a proxy for the genomic potential of WLE cyanoHABs to produce microcystin. Overall, this study provides support for the use of autonomous ‘omics capability in WLE to help expand the spatial and temporal coverage of cyanoHAB monitoring operations.

Autonomous Tracking of Salinity-Intrusion Fronts by a Long-Range Autonomous Underwater Vehicle

Shoreward intrusions of anomalously salty water along the continental shelf of the Middle Atlantic Bight are often observed in spring and summer. Exchange of heat, nutrients, and carbon across the salinity-intrusion front has a significant impact on the marine ecosystem and fisheries. In this article, we developed a method of using an autonomous underwater vehicle (AUV) to detect a salinity-intrusion front and track the front's movement. Autonomous front detection is based on the different vertical structures of salinity in the two distinct water types: the vertical difference of salinity is large in the intruding saltier water because of the salinity “tongue” at mid-depth, but is small in the nearshore fresher water due to absence of the salinity anomaly. Every time the AUV crosses and detects the front, the vehicle makes a turn at an oblique angle to cross the front, thus zigzagging through the front to map the frontal zone. The AUV's zigzags sweep back and forth to track the front as it moves over time. From June 25 to 30, 2021, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tethys</i> -class long-range AUV mapped and tracked a salinity-intrusion front on the southern New England shelf. The frontal tracking revealed the salinity intrusion's 3-D structure and temporal evolution with unprecedented detail.

Finding and Tracking a Phytoplankton Patch by a Long-Range Autonomous Underwater Vehicle

Phytoplankton (microscopic algae) play an important role in marine ecology. Resulting from a combination of physical, chemical, and biological processes, the distribution of phytoplankton is patchy, particularly in coastal marine ecosystems. Patches of high chlorophyll represent areas where enhanced primary productivity and biogeochemical cycling can occur. The scientific goal is to place observations within these biological hotspots to enable more extensive characterization of the environment and plankton populations. Aerial or satellite remote sensing can detect optical signal originating from phytoplankton within a limited depth range only near the ocean surface, and application of remote sensing is limited by atmospheric clarity. To observe the development of patchy phytoplankton communities <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> , we need the ability to locate and track individual patches. In this article, we present a method for an autonomous underwater vehicle (AUV) to autonomously find and climb on a positive horizontal gradient of chlorophyll to locate and track a phytoplankton patch. In two experiments in 2021, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tethys</i> -class long-range AUV autonomously located and tracked phytoplankton patches in southern Monterey Bay, CA, USA. The experiments demonstrated effectiveness of the method and pointed to the need for increased onboard adaptiveness in autonomous patch finding and tracking.

Design, analysis, and testing of Petrel acoustic autonomous underwater vehicle for marine monitoring

Autonomous underwater vehicles (AUVs) provide a unique and cost-effective platform for conducting ocean acoustic measurements and surveys such as marine mammal monitoring, extensive area seafloor topography survey, and fish stock survey. The small and medium-sized AUVs combine load-carrying capability, motion, and range and allow for timescale observations that are difficult to achieve with traditional acoustic observation platforms. However, self-noise is still an unavoidable challenge for AUVs when applied in passive acoustic monitoring. This paper presents the acoustic observation application of the Petrel acoustic AUV in marine monitoring. The layout design of a Petrel acoustic AUV and acoustic sensor integration are described in detail, and the self-noise characteristics of Petrel acoustic AUVs are evaluated by simulation and testing. The results showed that the self-noise level of Petrel acoustic AUVs is effectively controlled with its optimized design, and the maximum noise of the propulsion system is only 75 dB in the low-frequency band. Therefore, the Petrel acoustic AUV can be used as an ideal acoustic observation platform to carry out diverse observation tasks. Finally, a solution of fixed-depth motion is proposed to address the problem that frequent attitude adjustment affects the quality of acoustic data in fixed-depth navigation. According to a sea trial in the South China Sea, the method can significantly reduce the attitude adjustment frequency and, thus, provide an ideal environment for acoustic observation. This method also applies to other underwater mobile observation platforms. This study provides a reference for the acoustic integration design of underwater equipment such as long-range AUVs or hybrid-driven underwater gliders.

Conceptual design of a long-range autonomous underwater vehicle based on multidisciplinary optimization framework

The endurance of a deep-sea AUV is closely related to its sailing resistance, the amount of carrying batteries and equipment load, which involves interactions among multiple disciplines. In this paper, in order to develop the conceptual design of a long-range AUV in the early stage, a multidisciplinary optimization design framework is presented for decision-makers to explore the given design space, which takes into account the coupling between the disciplines of hull form, structural design and energy use. A Self-adaptive Surrogate Ensemble (SASE) method is proposed to replace the expensive process of hydrodynamic analysis, a recommended approach by the China Classification Society (CCS) specification is applied to carry out the design of metallic pressure hulls, and the classical lamination theory and Tsai–Wu criteria are adopted in the design of composite pressure hulls. Finally, the evaluation model of AUV endurance is created from the perspective of energy capacity and consumption. The conceptual design of a 200 kg-class AUV is executed to maximize the endurance based on the proposed multidisciplinary optimization design framework. The results show that the most important factors that affect AUV endurance are the velocity and diameter, and the optimum velocity of the AUV increases with the load power. The Sea-Whale 2000 AUV was developed based on the optimal result and the excellent endurance performance in the sea trial validated the effectiveness of the proposed design method in the preliminary design process.

Terrain-Aided Navigation With Coarse Maps—Toward an Arctic Crossing With an AUV

The desire to conduct research in the Arctic on an ever-larger spatiotemporal scale has led to the development of long-range autonomous underwater vehicles (AUVs), such as the Autosub Long-Range 1500 (ALR1500). While these platforms open up a world of new applications, their actual use is limited in GPS-denied environments since self-contained navigation remains yet unavailable. In response, this study evaluates whether terrain-aided navigation (TAN) can enable multimonth deployments using basic navigation sensors and sparse bathymetric maps. To evaluate the potential, ALR1500 undertakes a hypothetical science-driven mission from Svalbard (Norway) to Point Barrow (Alaska, USA) under the sea ice (a mission over 3200 km). Therefore, a simulated environment is developed, which integrates a state-of-the-art model of water circulation, error models for heading estimation at high latitudes, and an Arctic bathymetric map. Recognizing that this map is constructed based on sparse depth measurements and interpolation techniques, a bathymetric uncertainty model is developed. The performance of the TAN algorithm is examined with respect to the type of the heading sensor utilized and a range of vertical map distortions, calculated using the developed bathymetric uncertainty model. Simulations show that unaided navigation experiences an error of hundreds of kilometers, whereas TAN provides acceptable accuracy given a moderate map distortion. By degrading the quality of the map further, it appears that the navigation filter may diverge when traversing large regions subject to interpolation. Therefore, a rapidly-exploring random tree star algorithm is used to design a new path such that the AUV traverses reliable and rich in topographic information areas.

A Strategy for Sizing and Optimizing the Energy System on Long-Range AUVs

Autonomous underwater vehicles (AUVs) are utilized in a variety of unmanned missions, such as environmental surveys or guarding coastal waters. Currently, the most commonly used energy storage is a secondary battery pack. A potential solution for increasing the range even further on larger AUVs is to utilize hybrid fuel cell/battery systems. Fuel cell systems can have a significantly increased specific energy compared to batteries and are now starting to become well-developed technologies. However, in an underwater environment, both hydrogen and oxygen need to be stored for the fuel cell. This study considers the sizing of a hybrid fuel cell/battery system with respect to operational power profiles, i.e., how to select the best combination of fuel cell stack, battery pack, and reactant storage, and compares its volume and weight with conventional battery systems. This article presents a sizing strategy developed for this purpose, where the optimum combination of fuel cell output power and battery size is analyzed. The strategy is implemented on power profiles from real AUV missions. The sizing strategy will outline the techniques to reach the optimum hybrid configuration capable of meeting the power demand at any time of the mission. Results show that for longer missions, the volume of the hybrid system becomes significantly lower than for the battery system, meaning that the endurance can be increased thanks to the use of fuel cells.

Towards Energy-Aware Feedback Planning for Long-Range Autonomous Underwater Vehicles

Ocean ecosystems have spatiotemporal variability and dynamic complexity that require a long-term deployment of an autonomous underwater vehicle for data collection. A new generation of long-range autonomous underwater vehicles (LRAUVs), such as the Slocum glider and Tethys-class AUV, has emerged with high endurance, long-range, and energy-aware capabilities. These new vehicles provide an effective solution to study different oceanic phenomena across multiple spatial and temporal scales. For these vehicles, the ocean environment has forces and moments from changing water currents which are generally on the order of magnitude of the operational vehicle velocity. Therefore, it is not practical to generate a simple trajectory from an initial location to a goal location in an uncertain ocean, as the vehicle can deviate significantly from the prescribed trajectory due to disturbances resulted from water currents. Since state estimation remains challenging in underwater conditions, feedback planning must incorporate state uncertainty that can be framed into a stochastic energy-aware path planning problem. This article presents an energy-aware feedback planning method for an LRAUV utilizing its kinematic model in an underwater environment under motion and sensor uncertainties. Our method uses ocean dynamics from a predictive ocean model to understand the water flow pattern and introduces a goal-constrained belief space to make the feedback plan synthesis computationally tractable. Energy-aware feedback plans for different water current layers are synthesized through sampling and ocean dynamics. The synthesized feedback plans provide strategies for the vehicle that drive it from an environment’s initial location toward the goal location. We validate our method through extensive simulations involving the Tethys vehicle’s kinematic model and incorporating actual ocean model prediction data.

Online plan modification in uncertain resource-constrained environments

This paper presents an approach to planning under uncertainty in resource-constrained environments. We describe our novel method for online plan modification and execution monitoring, which augments an existing plan with pre-computed plan fragments in response to observed resource availability. Our plan merging algorithm uses causal structure to interleave actions, creating solutions online using observations of the true state without introducing significant computational cost. Our system monitors resource availability, reasoning about the probability of successfully completing the goals. We show that when the probability of completing a plan decreases, by removing low-priority goals our system reduces the risk of plan failure, increasing mission success rate. Conversely, when resource availability allows, by including additional goals our system increases reward without adversely affecting success rate.We evaluate our approach using the example domain of long-range autonomous underwater vehicle (AUV) missions, in which a vehicle spends months at sea with little or no opportunity for intervention. We compare the performance to a state-of-the-art oversubscription planner. Planning within such domains is challenging because significant resource usage uncertainty means it is computationally infeasible to calculate the optimal strategy in advance. We also evaluate the applicability of our plan merging algorithm to existing IPC domains, presenting a discussion of the domain characteristics which favour the use of our approach.

Energy-saving control of long-range autonomous underwater vehicle vertical plane based on human simulating intelligent control method

The long-range autonomous underwater vehicle is a new underwater vehicle with capability of stereoscopic observation of the ocean over a wide range of time series. This article proposed a novel control strategy for the long-range autonomous underwater vehicle considering the energy consumption. The vertical motion model of long-range autonomous underwater vehicle and the mathematical model of energy consumption of motion actuators are established in this article, and the maneuverability simulation experiments were carried out to analyze its motion and energy consumption characteristics. A hybrid controller based on human simulating intelligent control and S-plane control is designed. Considering the moment caused by the asymmetry of the hull in motion, an adaptive dynamic control allocation strategy is designed. Simulation experiments are conducted to demonstrate the performance of the scheme proposed.

Persistent Sampling of Vertically Migrating Biological Layers by an Autonomous Underwater Vehicle Within the Beam of a Seabed-Mounted Echosounder

Many pelagic animals, such as krill, lanternfish, and cephalopods, migrate to deep water at dawn to avoid visual predators during daylight hours and move up toward the sea surface at dusk to search for food. This behavior is termed “diel vertical migration.” Migrating animals graze on phytoplankton or zooplankton and in turn serve as food for higher trophic levels, hence providing a key mechanism for carbon export via this migration. These animals are often observed as sound-scattering layers by echosounders, but the animals causing the acoustic scattering are difficult to identify using acoustics alone. In a spring 2019 experiment in Monterey Bay, we deployed autonomous underwater and surface vehicles over a seabed-mounted upward-looking echosounder to collect environmental DNA (eDNA) with the goal of identifying the vertically migrating animals. The echosounder was installed at 890-m depth on the Monterey Accelerated Research System (MARS) seabed cabled ocean observatory, providing real-time data of acoustic backscatter from the full water column. One long-range autonomous underwater vehicle (LRAUV) carrying a Third-Generation Environmental Sample Processor (3G-ESP) acquired water samples from a sequence of layers from near surface down to ~ 290 m as directed by the distribution of animals observed by the echosounder. During the sampling of each layer, the LRAUV ran on a tight circular yo-yo trajectory directly above the echosounder, remaining in its beam by acoustically tracking a station-keeping Wave Glider on the sea surface marking the echosounder's latitude and longitude. The persistent and simultaneous acoustic observation and eDNA acquisition enables identification of animals at precise locations to better understand their vertical migration behaviors. We present the methods and the system performance in the experiment.

Combined Depth Control Strategy for Low-Speed and Long-Range Autonomous Underwater Vehicles

Autonomous underwater vehicles (AUVs) are increasingly being applied to highly detailed survey and inspection tasks over large ocean regions. These vehicles are required to have underwater hovering and low-speed cruising capabilities, and energy-saving property to enable long-range missions. To this end, a combined depth control strategy is proposed in which an on-off type variable ballast system (VBS) is adopted for satisfactory hovering or fast descending/ascending without propulsion to reach the designated cruising depth, whereas the bow and stern fins act as the actuator to maintain the cruising depth for more energy saving. A hierarchical architecture-based VBS controller, which comprises a ballast water mass planner and an on-off mass flowrate controller, is developed to assure good hovering performance of the on-off type VBS. Both numerical studies and basin tests are conducted on a middle-sized AUV to verify the feasibility and validity of this depth control strategy.