Autonomous Underwater Vehicle Data Research Articles

Adaptive Whale Optimization Algorithm–DBiLSTM for Autonomous Underwater Vehicle (AUV) Trajectory Prediction

AUVs are autonomous underwater robots equipped with advanced sensors and navigation systems. Due to the complexity and uncertainty of the marine environment, AUVs are susceptible to the effects of the marine environment and may experience communication delays or even accidents. Based on the aforementioned issues, this paper proposes a prediction method for lost AUVs based on an adaptive optimization depth BiLSTM (AWOA-DBiLSTM) neural network model. To enhance prediction accuracy, AWOA-DBiLSTM employs a double BiLSTM to extract AUV features from positional information and physical attitude. Additionally, AWOA-DBiLSTM utilizes a gating mechanism to filter and reset physical attitude feature information to obtain features associated with positional information. After undergoing filtering operations, the physical attitude information of the AUV is fused with the position information to achieve trajectory prediction. For the first time, the differentiation and stratified extraction of AUV data features are presented in this paper. The experimental results demonstrate that the model achieves significant improvements in prediction accuracy and generalization, and the present study is of great significance for application in the task of predicting the trajectories of lost AUVs.

An AUV-Assisted Data Gathering Scheme Based on Deep Reinforcement Learning for IoUT

The Underwater Internet of Things (IoUT) shows significant future potential in enabling a smart ocean. Underwater sensor network (UWSN) is a major form of IoUT, but it faces the problem of reliable data collection. To address these issues, this paper considers the use of the autonomous underwater vehicles (AUV) as mobile collectors to build reliable collection systems, while the value of information (VoI) is used as the primary measure of information quality. This paper first builds a realistic model to characterize the behavior of sensor nodes and the AUV together with challenging environments. Then, improved deep reinforcement learning (DRL) is used to dynamically plan the AUV’s navigation route by jointly considering the location of nodes, the data value of nodes, and the status of the AUV to maximize the data collection efficiency of the AUV. The results of the simulation show the dynamic data collection scheme is superior to the traditional path planning scheme, which only considers the node location, and greatly improves the efficiency of AUV data collection.

Data collection optimization of ocean observation network based on AUV path planning and communication

The Ocean Observation Network (OON) is an ocean science and engineering observation platform which integrates underwater wireless sensors, acoustic buoys, submersible buoys, and sonars. The complex underwater environment makes it challenging to place the observation nodes flexibly. In this research, firstly, a novel OON data collection optimization scheme is proposed, which is based on Autonomous Underwater Vehicle (AUV) path planning and communication. By combining the Voronoi diagram and K-means algorithm, an optimized clustering algorithm (VorK-means) is proposed to optimize the location of junction boxes and reduce the losses in the transmission energy of submarine cables in the OON simultaneously. Subsequently, a computational algorithm for AUV data collection position is proposed based on Voronoi vertices and communication circles. Moreover, an improved Grey Wolf Optimization (GWO) algorithm, based on the Levy flight and dimension (LD) search strategy, is proposed and applied for the AUV path planning, which is named LD-GWO. The accuracy and efficiency are validated by applying the proposed algorithms to the Canadian North-East Pacific Time-series Underwater Networked Experiment (NEPTUNE). Compared with the K-means algorithm, the VorK-means algorithm can reduce the total length of submarine cables by nearly 14%, and compared with the GWO algorithm, LD-GWO can obtain shorter data collection paths under identical parameter settings.

A Fault Diagnosis Method for the Autonomous Underwater Vehicle via Meta-Self-Attention Multi-Scale CNN

Autonomous underwater vehicles (AUVs) are an important equipment for ocean investigation. Actuator fault diagnosis is essential to ensure the sailing safety of AUVs. However, the lack of failure data for training due to unknown ocean environments and unpredictable failure occurrences is challenging for fault diagnosis. In this paper, a meta-self-attention multi-scale convolution neural network (MSAMS–CNN) is proposed for the actuator fault diagnosis of AUVs. Specifically, a two-dimensional spectrogram of the vibration signals obtained by a vibration sensor is used as the neural network’s inputs. The diagnostic model is fitted by executing a subtask-based gradient optimization procedure to generate more general degradation knowledge. A self-attentive multi-scale feature extraction approach is used to utilize both global and local features for learning important parameters autonomously. In addition, a meta-learning method is utilized to train the diagnostic model without a large amount of labeled data, which enhances the generalization ability and allows for cross-task training. Experimental studies with real AUV data collected by vibration sensors are conducted to validate the effectiveness of the MSAMS–CNN. The results show that the proposed method can diagnose the rudder and thruster faults of AUVs in the cases of few-shot diagnosis.

Feature Space Exploration For Planning Initial Benthic AUV Surveys

Special-purpose Autonomous Underwater Vehicles (AUVs) are utilized for benthic (seafloor) surveys, where the vehicle collects optical imagery of the seafloor. Due to the small-sensor footprint of the cameras and the vast areas to be surveyed, these AUVs can not feasibly collect full coverage imagery of areas larger than a few tens of thousands of square meters. Therefore it is necessary for AUV paths to sample the surveys areas sparsely, yet effectively. Broad-scale acoustic bathymetric data are readily available over large areas and are often a useful prior of seafloor cover. As such, prior bathymetry can be used to guide AUV data collection. This research proposes methods for planning initial AUV surveys that efficiently explore a feature space representation of the bathymetry, in order to sample from a diverse set of bathymetric terrain. This will enable the AUV to visit areas that likely contain unique habitats and are representative of the entire survey site. We propose several information gathering planners that utilize a feature space exploration reward, to plan freeform paths or to optimize the placement of a survey template. The suitability of these methods to plan AUV surveys is evaluated based on the coverage of the feature space and also the ability to visit all classes of benthic habitat on the initial dive. Informative planners based on Rapidly expanding Random Trees (RRT) and Monte Carlo Tree Search (MCTS) were found to be the most effective. This is a valuable tool for AUV surveys as it increases the utility of initial dives. It also delivers a comprehensive training set to learn the relationship between acoustic bathymetry and visually derived seafloor classifications.

Power-Efficient Data Collection Scheme for AUV-Assisted Magnetic Induction and Acoustic Hybrid Internet of Underwater Things

Power efficiency is a big concern in the Internet of Underwater Things (IoUT). The power consumption of underwater acoustic communications is typically in the scale of watts, which may drain the battery of underwater devices quickly. Whereas, the power consumption of underwater magnetic induction (MI) wireless communications is in the scale of milliwatt. Therefore, this article devotes to combine the underwater MI and acoustic communications to form a power-efficient underwater hybrid wireless network. Specifically, we investigate the power-efficient autonomous underwater vehicle (AUV) data collection schemes in an underwater MI and acoustic hybrid sensor network. We propose an alternating anchor nodes selection and flow routing (AANSFR) AUV data collection method, which alternately optimizes the AUV path planning and network data flow routing. The simulation results show that the proposed hybrid data collection scheme can significantly prolong the lifespan of underwater sensor networks.

Quantifying Scales of Spatial Variability of Cyanobacteria in a Large, Eutrophic Lake Using Multiplatform Remote Sensing Tools

Harmful algal blooms of cyanobacteria are increasing in magnitude and frequency globally, degrading inland and coastal aquatic ecosystems and adversely affecting public health. Efforts to understand the structure and natural variability of these blooms range from point sampling methods to a wide array of remote sensing tools. This study aims to provide a comprehensive view of cyanobacterial blooms in Clear Lake, California — a shallow, polymictic, naturally eutrophic lake with a long record of episodic cyanobacteria blooms. To understand the spatial heterogeneity and temporal dynamics of cyanobacterial blooms, we evaluated a satellite remote sensing tool for estimating coarse cyanobacteria distribution with coincident, in situ measurements at varying scales and resolutions. The Cyanobacteria Index (CI) remote sensing algorithm was used to estimate cyanobacterial abundance in the top portion of the water column from data acquired from the Ocean and Land Color Instrument (OLCI) sensor on the Sentinel-3a satellite. We collected hyperspectral data from a handheld spectroradiometer; discrete 1 m integrated surface samples for chlorophyll-a and phycocyanin; multispectral imagery from small Unmanned Aerial System (sUAS) flights (∼12 cm resolution); Autonomous Underwater Vehicle (AUV) measurements of chlorophyll-a, turbidity, and colored dissolved organic matter (∼10 cm horizontal spacing, 1 m below the water surface); and meteorological forcing and lake temperature data to provide context to our cyanobacteria measurements. A semivariogram analysis of the high resolution AUV and sUAS data found the Critical Scale of Variability for cyanobacterial blooms to range from 70 to 175 m, which is finer than what is resolvable by the satellite data. We thus observed high spatial variability within each 300 m satellite pixel. Finally, we used the field spectroscopy data to evaluate the accuracy of both the original and revised CI algorithm. We found the revised CI algorithm was not effective in estimating cyanobacterial abundance for our study site. Satellite-based remote sensing tools are vital to researchers and water managers as they provide consistent, high-coverage data at a low cost and sampling effort. The findings of this research support continued development and refinement of remote sensing tools, which are essential for satellite monitoring of harmful algal blooms in lakes and reservoirs.

Recent fishing footprint of the high-seas bottom trawl fisheries on the Northwestern Hawaiian Ridge and Emperor Seamount Chain: A finer-scale approach to a large-scale issue

A standing data gap for management of high-seas seamounts of the Northwestern Hawaiian Ridge and Emperor Seamounts (ES-NHR) by the North Pacific Fisheries Commission is the footprint of fisheries activities on these seamounts. Using satellite AIS data and the algorithms of the publicly available Global Fishing Watch database, a spatial map of trawling in a 0.01-degree latitude by 0.01-degree longitude square grid was created to review the data available to map this footprint. From 2012 to 2018 much of the trawling effort for all countries focused on Koko, Yuryaku, Kammu, and Colahan Seamounts at depths between 400 m (summits) and the depth limit currently set by the North Pacific Fisheries Commission of 1500 m. Additional seamounts with fishing activity included Annei (North Koko), Kinmei, Jingu, and Suiko. The remaining ES-NHR seamount locations show no trawling in those years. Bottom contact fishing was predominately carried out by ships with flag states of Japan and Korea. To date there appears to be compliance with the recent small-scale closures on C–H seamount and Koko.An additional source of data comes from scientific Autonomous Underwater Vehicle (AUV) surveys in 2014 and 2015 from three of these seamounts, in which scars from bottom contact gear are readily visible. These cover a smaller spatial area and depth range than the satellite data, but indicate the full footprint is not encompassed by the satellite data, suggesting either the fishing footprint is not fully captured by the AIS approach or that the footprint has shifted through time. AUV surveys also provide data on areas where abundant megafauna occur, which can provide candidate starting points for VME management efforts and further closures, similar to ones already in effect in the ES-NHR. The combination of satellite and AUV data provide a finer-scale fisheries footprint for this region that can aid in management of these sites.

SSLP: A Stratification-Based Source Location Privacy Scheme in Underwater Acoustic Sensor Networks

Source location privacy (SLP) protection is an important means of security protection in wireless sensor networks (WSNs). With the development of underwater acoustic sensor networks (UASNs), security and privacy have attracted increasing attention. In this study, we incorporated SLP into UASNs as the basis for a novel stratification-based source location privacy (SSLP) scheme. In the SSLP scheme, SLP is protected through the cooperation of autonomous underwater vehicles (AUVs) in each network layer. Because fake source nodes and fake data streams are commonly used in WSNs, methods that have similar functions in UASNs are required. Hence, we incorporate a fake source node into the underwater cluster structure to add randomness to the underwater network. Furthermore, fake data streams have been included within the AUV data collection and transmission for each cluster. Simulation results confirm that the SSLP scheme offers improved security in comparison with existing underwater data transmission schemes.

Dynamic Magnetic Induction Wireless Communications for Autonomous-Underwater-Vehicle-Assisted Underwater IoT

Leveraging the mobility of autonomous underwater vehicles (AUVs) to collect and deliver data among different underwater devices enables numerous underwater Internet-of-Things (UW-IoT) applications. However, the most versatile underwater acoustic communications (UACs) may not be suitable in the AUV-assisted UW-IoT scenarios, considering the high cost and high power consumption of acoustic transducers, as well as high error rates of UACs due to the complex underwater acoustic channel conditions. Alternatively, we propose to apply the low-power magnetic induction (MI)-based wireless communications for AUV data dissemination and collection. Due to the mobility of AUVs and the underwater turbulence, MI channels between AUVs and other underwater devices are no longer stable and static, which poses great challenges to establish reliable MI links. To tackle this problem, we investigate the dynamic MI wireless communications in this article. We first mathematically characterize the dynamic MI channel when an AUV approaches its target for data collection. Based on this dynamic channel model, the dynamic communication range and available bandwidth of MI are derived. We also build an MI wireless communication system that can work within a dynamic range. The communication performances are evaluated through numerical simulations as well as underwater experiments.

Exploring Spatial Heterogeneity of Antarctic Sea Ice Algae Using an Autonomous Underwater Vehicle Mounted Irradiance Sensor

Sea ice algae represent a key energy source for many organisms in polar food webs, but estimating their biomass at ecologically appropriate spatiotemporal scales remains a challenge. Attempts to extend ice-core derived biomass to broader scales using remote sensing approaches has largely focused on the use of under-ice spectral irradiance. Normalized Difference Index (NDI) based algorithms that relate the attenuation of irradiance by the snow-ice-algal ensemble at specific wavelengths to biomass have been used to explain up to 79% of the biomass of algae in limited areas. Application of these algorithms to datasets collected using tethered Remotely Operated Vehicles (ROVs) has begun, generating methods for spatial sampling at scales and spatial resolution not achievable with ice-core sampling. Successful integration of radiometers with untethered Autonomous Underwater Vehicles (AUVs) offers even greater capability to survey broader regions to explore the spatial heterogeneity of sea ice algal communities. This work describes the pilot use of an AUV fitted with a multispectral irradiance sensor to estimate ice-algal biomass along transects beneath land-fast sea ice (~2 m thick with minimal snow cover) in McMurdo Sound, Antarctica. The AUV obtained continuous, repeatable, multi-band irradiance data, suitable for NDI-type approaches, over transects of 500 m, with an instrument footprint of 4 m in diameter. Algorithms were developed using local measurements of ice algae biomass and spectral attenuation of sea ice and were able to explain 40% of biomass variability. Relatively poor performance of the algorithms in predicting biomass limited the confidence that could be placed in biomass estimates from AUV data. This was attributed to the larger footprint size of the optical sensors integrating small-scale biomass variability more effectively than the ice core in the platelet-dominated ice algal habitat. Our results support continued development of remote-sensing of sea ice algal biomass at m – km spatial scales using optical methods, but caution that footprint sizes of calibration data (e.g. coring) must be compatible with optical sensors used. AUVs offer autonomous survey techniques that could be applied to better understand the horizontal variability of sea ice algae from nearshore ice out to the marginal ice zone.

Tracking the Movements of Juvenile Chinook Salmon using an Autonomous Underwater Vehicle under Payload Control

An autonomous underwater vehicle (AUV) under payload control (PC) was used to map the movements of juvenile Chinook salmon (Oncorhynchus tshawytscha) tagged with acoustic transmitters. After detecting a tag, the AUV deviated from its pre-programmed route and performed a maneuver designed to enhance the location estimate of the fish and to move closer to collect proximal environmental data. Nineteen fish were released into marine waters of southeastern Alaska. Seven missions with concurrent AUV and vessel-based surveys were conducted with two to nine fish present in the area per mission. The AUV was able to repeatedly detect and estimate the location of the fish, even when multiple individuals were present. Although less effective at detecting the fish, location estimates from the vessel-based surveys helped verify the veracity of the AUV data. All of the fish left the area within 48 h of release. Most fish exhibited localized movements (milling behavior) before leaving the area. Dispersal rates calculated for the fish suggest that error associated with the location estimates was minimal. The average movement rate was 0.62 body length per second and was comparable to marine movement rates reported for other Chinook salmon stocks. These results suggest that AUV-based payload control can provide an effective method for mapping the movements of marine fish.

A multiple autonomous underwater vehicles hazard decision method based on information fusion

Autonomous underwater vehicle (AUV) plays an important role in ocean research. Compared with single AUV system, multi-AUV system has higher stability, robustness and high efficiency. Multiple AUVs give greater credibility than a single AUV in decision making. In this paper, the problem of multiple AUV making dangerous decisions in dangerous environments is studied. Single AUV can not make an accurate decision when facing some complex situations. We propose to fuse multiple AUV data to obtain more accurate dangerous decisions. The multi-AUV system adopts a distributed multi-robot structure, each AUV is an individual. A transferable information model and Dempster-Shafer evidence theory are used. A new multi-AUV hazard discrimination model is proposed for the final hazard decisionmaking. Verification by calculation, new decision-making method can effectively improve the discriminant ability of multi-AUV system when facing danger. This new hazard decision method improves the survivability of multiple AUVs in actual ocean exploration.

Quantitative mapping and predictive modeling of Mn nodules' distribution from hydroacoustic and optical AUV data linked by random forests machine learning

Abstract. In this study, high-resolution bathymetric multibeam and optical image data, both obtained within the Belgian manganese (Mn) nodule mining license area by the autonomous underwater vehicle (AUV) Abyss, were combined in order to create a predictive random forests (RF) machine learning model. AUV bathymetry reveals small-scale terrain variations, allowing slope estimations and calculation of bathymetric derivatives such as slope, curvature, and ruggedness. Optical AUV imagery provides quantitative information regarding the distribution (number and median size) of Mn nodules. Within the area considered in this study, Mn nodules show a heterogeneous and spatially clustered pattern, and their number per square meter is negatively correlated with their median size. A prediction of the number of Mn nodules was achieved by combining information derived from the acoustic and optical data using a RF model. This model was tuned by examining the influence of the training set size, the number of growing trees (ntree), and the number of predictor variables to be randomly selected at each node (mtry) on the RF prediction accuracy. The use of larger training data sets with higher ntree and mtry values increases the accuracy. To estimate the Mn-nodule abundance, these predictions were linked to ground-truth data acquired by box coring. Linking optical and hydroacoustic data revealed a nonlinear relationship between the Mn-nodule distribution and topographic characteristics. This highlights the importance of a detailed terrain reconstruction for a predictive modeling of Mn-nodule abundance. In addition, this study underlines the necessity of a sufficient spatial distribution of the optical data to provide reliable modeling input for the RF.

A Stratification-Based Data Collection Scheme in Underwater Acoustic Sensor Networks

Underwater acoustic sensor networks (UASNs) are widely used in a variety of ocean applications, such as exploring ocean resources or monitoring abnormal ocean environments. However, data collection schemes in UASNs are significantly different from those in wireless sensor networks due to high power consumption, severe propagation delay, and so on. Furthermore, previous research has overlooked practical conditions, such as characteristics of water delamination and energy constraint on autonomous underwater vehicles (AUVs). In this paper, a stratification-based data collection scheme for three-dimensional UASNs is proposed to solve these problems. In this scheme, the network is divided into two layers on the basis of the Ekman drift current model. The upper layer, called the Ekman layer, suffers large water velocity. Thus, nodes in the upper layer will follow the water flow. In this case, we employ a forward set-based multihop forwarding algorithm for data collection. The lower layer suffers small water velocity so that nodes in this layer are considered as relatively static. A neighbor density clustering-based AUV data gathering algorithm is applied in this layer for data collection. By employing different data collection algorithms in different layers, we can integrate the advantages of a multihop transmission scheme and AUV-aided data collection scheme to reduce network consumption and improve network lifetime. The simulation results also confirm the proposed method has good performance.

Evolution of a physical and biological front from upwelling to relaxation

Fronts influence the structure and function of coastal marine ecosystems. Due to the complexity and dynamic nature of coastal environments and the small scales of frontal gradient zones, frontal research is difficult. To advance this challenging research we developed a method enabling an autonomous underwater vehicle (AUV) to detect and track fronts, thereby providing high-resolution observations in the moving reference frame of the front itself. This novel method was applied to studying the evolution of a frontal zone in the coastal upwelling environment of Monterey Bay, California, through a period of variability in upwelling intensity. Through 23 frontal crossings in four days, the AUV detected the front using real-time analysis of vertical thermal stratification to identify water types and the front between them, and the vehicle tracked the front as it moved more than 10 km offshore. The physical front coincided with a biological front between strongly stratified phytoplankton-enriched water inshore of the front, and weakly stratified phytoplankton-poor water offshore of the front. While stratification remained a consistent identifier, conditions on both sides of the front changed rapidly as regional circulation responded to relaxation of upwelling winds. The offshore water type transitioned from relatively cold and saline upwelled water to relatively warm and fresh coastal transition zone water. The inshore water type exhibited an order of magnitude increase in chlorophyll concentrations and an associated increase in oxygen and decrease in nitrate. It also warmed and freshened near the front, consistent with the cross-frontal exchange that was detected in the high-resolution AUV data. AUV-observed cross-frontal exchanges beneath the surface manifestation of the front emphasize the importance of AUV synoptic water column surveys in the frontal zone.

Integrating field survey data with satellite image data to improve shallow water seagrass maps: the role of AUV and snorkeller surveys?

Repeatable and accurate seagrass mapping is required for understanding seagrass ecology and supporting management decisions. For shallow (<5 m) seagrass habitats, these maps can be created by integrating high spatial resolution imagery with field survey data. Field survey data for seagrass are often collected via snorkelling or diving. However, these methods are limited by environmental and safety considerations. Autonomous underwater vehicles (AUVs) are used increasingly to collect field data for habitat mapping, albeit mostly in deeper waters (>20 m). Here, we demonstrate and evaluate the use and potential advantages of AUV field data collection for calibration and validation of seagrass habitat mapping of shallow waters (<5 m), from multispectral satellite imagery. The study was conducted in the seagrass habitats of the Eastern Banks (142 km2), Moreton Bay, Australia. In the field, georeferenced photographs of the seagrass were collected along transects via snorkelling or an AUV. Photographs from both collection methods were analysed manually for seagrass species composition and then used as calibration and validation data to map seagrass using an established semi-automated object-based mapping routine. A comparison of the relative advantages and disadvantages of AUV and snorkeller-collected field data-sets and their influence on the mapping routine was conducted. AUV data collection was more consistent, repeatable and safer in comparison with snorkeller transects. Inclusion of deeper water AUV data resulted in mapping of a larger extent of seagrass (~7 km2, 5% of study area) in the deeper waters of the site. Although overall map accuracies did not differ considerably, inclusion of the AUV data from deeper water transects corrected errors in seagrass mapped at depths to 5 m, but where the bottom is visible on satellite imagery. Our results demonstrate that further development of AUV technology is justified for the monitoring of seagrass habitats in ongoing management programmes.

Distributions of invertebrate larvae and phytoplankton in a coastal upwelling system retention zone and peripheral front

Near-coastal retention of larvae affects the ecology of many marine species. In coastal upwelling ecosystems having strong offshore transport, larval ecology is greatly influenced by nearshore retention in bays and in the lee of headlands. Further, frontal dynamics along the periphery of retention zones can drive larval accumulation and transport. The purpose of this study is to examine larval distributions and associated processes across a retention zone and its peripheral front in the coastal upwelling environment of Monterey Bay, California, USA. During fall 2009 an autonomous underwater vehicle (AUV) was used to observe environmental variability at high resolution and acquire targeted water samples. Invertebrate larvae in samples were subsequently identified and quantified using molecular methods. To infer ecological processes we examine larval distributions in relation to environmental processes revealed by the AUV data and the greater regional observing system. As a window into biological–physical interactions that may concentrate motile larvae in convergence zones, we examine more extensive in situ and remote sensing observations that describe distribution patterns of motile phytoplankton. During the 10-day study intensification of upwelling caused flow of cold water into the bay and formation of an upwelling front. Drifter and satellite observations showed retention of near-surface water within the bay inshore of the front, where a bloom of motile phytoplankton intensified. Larval distributions were related to processes inferred at a range of scales. At the scale of the retention zone, dense phytoplankton accumulations indicated concentration of motile plankton in a convergence zone created by flow toward the coast, as well as nutritional support for larvae. At the scale of the front, velocity and water property measurements indicated convergence between cold deep-shelf water transported shoreward along shoaling bathymetry and the overlying warm surface water, influencing plankton accumulation and vertical transport. At the finest scales resolved, aerial photography revealed banded accumulations of dense phytoplankton bloom patches and narrow foam lines, common indicators of small-scale convergence zones and consistent with internal wave processes. Exceptionally high larval concentrations were detected in samples from locations affected by frontal and internal wave dynamics. This study illustrates how autonomous feature recognition and targeted sampling with an AUV, applied within the greater context of multidisciplinary observation across regional to small scales, can advance plankton ecology research.

Autonomous Underwater Vehicles (AUVs): Their past, present and future contributions to the advancement of marine geoscience

Autonomous Underwater Vehicles (AUVs) have a wide range of applications in marine geoscience, and are increasingly being used in the scientific, military, commercial, and policy sectors. Their ability to operate autonomously of a host vessel makes them well suited to exploration of extreme environments, from the World’s deepest hydrothermal vents to beneath polar ice sheets. They have revolutionized our ability to image the seafloor, providing higher resolution seafloor mapping data than can be achieved from surface vessels, particularly in deep water. This contribution focuses on the major advances in marine geoscience that have resulted from AUV data. The primary applications are i) submarine volcanism and hydrothermal vent studies, ii) mapping and monitoring of low-temperature fluid escape features and chemosynthetic ecosystems, iii) benthic habitat mapping in shallow- and deep-water environments, and iv) mapping of seafloor morphological features (e.g. bedforms generated beneath ice or sediment-gravity flows). A series of new datasets are presented that highlight the growing versatility of AUVs for marine geoscience studies, including i) multi-frequency acoustic imaging of trawling impacts on deep-water coral mounds, iii) collection of high-resolution seafloor photomosaics at abyssal depths, and iii) velocity measurements of active submarine density flows. Future developments in AUV technology of potential relevance to marine geoscience include new vehicles with enhanced hovering, long endurance, extreme depth, or rapid response capabilities, while development of new sensors will further expand the range of geochemical parameters that can be measured.

NEEMO 15: Evaluation of human exploration systems for near-Earth asteroids

The NASA Extreme Environment Mission Operations (NEEMO) 15 mission was focused on evaluating techniques for exploring near-Earth asteroids (NEAs). It began with a University of Delaware autonomous underwater vehicle (AUV) systematically mapping the coral reef for hundreds of meters surrounding the Aquarius habitat. This activity is akin to the type of “far-field survey” approach that may be used by a robotic precursor in advance of a human mission to a NEA. Data from the far-field survey were then examined by the NEEMO science team and follow-up exploration traverses were planned, which used Deepworker single-person submersibles. Science traverses at NEEMO 15 were planned according to a prioritized list of objectives developed by the science team. These objectives were based on review and discussion of previous related marine science research, including previous marine science saturation missions conducted at the Aquarius habitat. AUV data were used to select several areas of scientific interest. The Deepworker science traverses were then executed at these areas of interest during 4 days of the NEEMO 15 mission and provided higher resolution data such as coral species distribution and mortality. These traverses are analogous to the “near-field survey” approach that is expected to be performed by a Multi-Mission Space Exploration Vehicle (MMSEV) during a human mission to a NEA before extravehicular activities (EVAs) are conducted. In addition to the science objectives that were pursued, the NEEMO 15 traverses provided an opportunity to test newly developed software and techniques. Sample collection and instrument deployment on the NEA surface by EVA crew would follow the “near-field survey” in a human NEA mission. Sample collection was not necessary for the purposes of the NEEMO science objectives; however, the engineering and operations objectives during NEEMO 15 were to evaluate different combinations of vehicles, crew members, tools, and equipment that could be used to perform these science objectives on a NEA. Specifically, the productivity and acceptability of simulated NEA exploration activities were systematically quantified and compared when operating with different combinations of crew sizes and exploration systems including MMSEVs, EVA jet packs, and EVA translation devices. Data from NEEMO 15 will be used in conjunction with data from software simulations, parametric analysis, other analog field tests, anchoring models, and integrated testing at Johnson Space Center to inform the evolving architectures and exploration systems being developed by the Human Spaceflight Architecture Team.