A Flexible Autonomous Robotic Observatory Infrastructure for Bentho-Pelagic Monitoring

A maneuverable underwater vehicle for near-seabed observation

Underwater robots can collect comprehensive information on species and habitats when conducting seabed operations, enhancing localized insights and expanding underwater ecological understanding. One approach uses autonomous underwater vehicles, but proximity operations may disturb sediments and compromise observation quality. Another approach uses wheeled or legged benthic robots, but unavoidable contact limits their application in delicate ecosystems like coral reefs. To address these challenges, we propose a maneuverable underwater vehicle for near-seabed observations. This vehicle moves with minimal turbulence and shows strong resistance to external disturbances, enabling high-quality seabed observation as close as 20 cm. It rapidly detects intense disturbances like turbulence and wall effects, allowing real-time path planning to prevent bottoming. Multiple tests in various marine environments, including sandy areas, coral reefs, and sheer rock, show low sediment disturbance and improved adaptability to rugged underwater terrain.

Deep-sea cabled video-observatory provides insights into the behavior at depth of sub-adult male northern elephant seals, Mirounga angustirostris

The Ocean Networks Canada (ONC) cabled video-observatory at the Barkley Canyon Node (British Columbia, Canada) was recently the site of a Fish Acoustics and Attraction Experiment (FAAE), from May 21, 2022 to July 16, 2023, combining observations from High-Definition (HD) video, acoustic imaging sonar, and underwater sounds at a depth of 645 m, to examine the effects of light and bait on deep-sea fish and invertebrate behaviors. The unexpected presence of at least eight (six recurrent and two temporary) sub-adult male northern elephant seals (Mirounga angustirostris) was reported in 113 and 210 recordings out of 9737 HD and 2805 sonar videos at the site, respectively. Elephant seals were found at the site during seven distinct periods between June 22, 2022 and May 19, 2023. Ethograms provided insights into the seal's deep-sea resting and foraging strategies, including prey selection. We hypothesized that the ability of elephant seals to perform repeated visits to the same site over long periods (> 10 days) was due to the noise generated by the sonar, suggesting that they learned to use that anthropogenic source as an indicator of food location, also known as the "dinner bell" effect. One interpretation is that elephant seals are attracted to the FAAE site due to the availability of prey and use the infrastructure as a foraging and resting site, but then take advantage of fish disturbance caused by the camera lights to improve foraging success. Our video observations demonstrated that northern elephant seals primarily focused on actively swimming sablefish (Anoplopoma fimbria), ignoring stationary or drifting prey. Moreover, we found that elephant seals appear to produce (voluntary or involuntary) infrasonic sounds in a foraging context. This study highlights the utility of designing marine observatories with spatially and temporally cross-referenced data collection from instruments representing multiple modalities of observation.

Underwater legged robotics: review and perspectives

Nowadays, there is a growing awareness on the social and economic importance of the ocean. In this context, being able to carry out a diverse range of operations underwater is of paramount importance for many industrial sectors as well as for marine science and to enforce restoration and mitigation actions. Underwater robots allowed us to venture deeper and for longer time into the remote and hostile marine environment. However, traditional design concepts such as propeller driven remotely operated vehicles, autonomous underwater vehicles, or tracked benthic crawlers, present intrinsic limitations, especially when a close interaction with the environment is required. An increasing number of researchers are proposing legged robots as a bioinspired alternative to traditional designs, capable of yielding versatile multi-terrain locomotion, high stability, and low environmental disturbance. In this work, we aim at presenting the new field of underwater legged robotics in an organic way, discussing the prototypes in the state-of-the-art and highlighting technological and scientific challenges for the future. First, we will briefly recap the latest developments in traditional underwater robotics from which several technological solutions can be adapted, and on which the benchmarking of this new field should be set. Second, we will the retrace the evolution of terrestrial legged robotics, pinpointing the main achievements of the field. Third, we will report a complete state of the art on underwater legged robots focusing on the innovations with respect to the interaction with the environment, sensing and actuation, modelling and control, and autonomy and navigation. Finally, we will thoroughly discuss the reviewed literature by comparing traditional and legged underwater robots, highlighting interesting research opportunities, and presenting use case scenarios derived from marine science applications.

A New Method for Calculating Water Quality Parameters by Integrating Space–Ground Hyperspectral Data and Spectral-In Situ Assay Data

The effective integration of aerial remote sensing data and ground multi-source data has always been one of the difficulties of quantitative remote sensing. A new monitoring mode is designed, which installs the hyperspectral imager on the UAV and places a buoy spectrometer on the river. Water samples are collected simultaneously to obtain in situ assay data of total phosphorus, total nitrogen, COD, turbidity, and chlorophyll during data collection. The cross-correlogram spectral matching (CCSM) algorithm is used to match the data of the buoy spectrometer with the UAV spectral data to significantly reduce the UAV data noise. An absorption characteristics recognition algorithm (ACR) is designed to realize a new method for comparing UAV data with laboratory data. This method takes into account the spectral characteristics and the correlation characteristics of test data synchronously. It is concluded that the most accurate water quality parameters can be calculated by using the regression method under five scales after the regression tests of the multiple linear regression method (MLR), support vector machine method (SVM), and neural network (NN) method. This new working mode of integrating spectral imager data with point spectrometer data will become a trend in water quality monitoring.

Autonomous Surface and Underwater Vehicles as Effective Ecosystem Monitoring and Research Platforms in the Arctic-The Glider Project

Effective ocean management requires integrated and sustainable ocean observing systems enabling us to map and understand ecosystem properties and the effects of human activities. Autonomous subsurface and surface vehicles, here collectively referred to as “gliders”, are part of such ocean observing systems providing high spatiotemporal resolution. In this paper, we present some of the results achieved through the project “Unmanned ocean vehicles, a flexible and cost-efficient offshore monitoring and data management approach—GLIDER”. In this project, three autonomous surface and underwater vehicles were deployed along the Lofoten–Vesterålen (LoVe) shelf-slope-oceanic system, in Arctic Norway. The aim of this effort was to test whether gliders equipped with novel sensors could effectively perform ecosystem surveys by recording physical, biogeochemical, and biological data simultaneously. From March to September 2018, a period of high biological activity in the area, the gliders were able to record a set of environmental parameters, including temperature, salinity, and oxygen, map the spatiotemporal distribution of zooplankton, and record cetacean vocalizations and anthropogenic noise. A subset of these parameters was effectively employed in near-real-time data assimilative ocean circulation models, improving their local predictive skills. The results presented here demonstrate that autonomous gliders can be effective long-term, remote, noninvasive ecosystem monitoring and research platforms capable of operating in high-latitude marine ecosystems. Accordingly, these platforms can record high-quality baseline environmental data in areas where extractive activities are planned and provide much-needed information for operational and management purposes.

An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions

Imaging technologies are being deployed on cabled observatory networks worldwide. They allow for the monitoring of the biological activity of deep-sea organisms on temporal scales that were never attained before. In this paper, we customized Convolutional Neural Network image processing to track behavioral activities in an iconic conservation deep-sea species—the bubblegum coral Paragorgia arborea—in response to ambient oceanographic conditions at the Lofoten-Vesterålen observatory. Images and concomitant oceanographic data were taken hourly from February to June 2018. We considered coral activity in terms of bloated, semi-bloated and non-bloated surfaces, as proxy for polyp filtering, retraction and transient activity, respectively. A test accuracy of 90.47% was obtained. Chronobiology-oriented statistics and advanced Artificial Neural Network (ANN) multivariate regression modeling proved that a daily coral filtering rhythm occurs within one major dusk phase, being independent from tides. Polyp activity, in particular extrusion, increased from March to June, and was able to cope with an increase in chlorophyll concentration, indicating the existence of seasonality. Our study shows that it is possible to establish a model for the development of automated pipelines that are able to extract biological information from times series of images. These are helpful to obtain multidisciplinary information from cabled observatory infrastructures.

ENDURUNS: An Integrated and Flexible Approach for Seabed Survey Through Autonomous Mobile Vehicles

The oceans cover more than two-thirds of the planet, representing the vastest part of natural resources. Nevertheless, only a fraction of the ocean depths has been explored. Within this context, this article presents the H2020 ENDURUNS project that describes a novel scientific and technological approach for prolonged underwater autonomous operations of seabed survey activities, either in the deep ocean or in coastal areas. The proposed approach combines a hybrid Autonomous Underwater Vehicle capable of moving using either thrusters or as a sea glider, combined with an Unmanned Surface Vehicle equipped with satellite communication facilities for interaction with a land station. Both vehicles are equipped with energy packs that combine hydrogen fuel cells and Li-ion batteries to provide extended duration of the survey operations. The Unmanned Surface Vehicle employs photovoltaic panels to increase the autonomy of the vehicle. Since these missions generate a large amount of data, both vehicles are equipped with onboard Central Processing units capable of executing data analysis and compression algorithms for the semantic classification and transmission of the acquired data.