A hybrid underwater robot for multidisciplinary investigation of the ocean twilight zone

Advances in environmental DNA monitoring: standardization, automation, and emerging technologies in aquatic ecosystems

Environmental DNA (eDNA) monitoring, a rapidly advancing technique for assessing biodiversity and ecosystem health, offers a noninvasive approach for detecting and quantifying species from various environmental samples. In this review, a comprehensive overview of current eDNA collection and detection technologies is provided, emphasizing the necessity for standardization and automation in aquatic ecological monitoring. Furthermore, the intricacies of water bodies, from streams to the deep sea, and the associated challenges they pose for eDNA capture and analysis are explored. The paper delineates three primary eDNA survey methods, namely, bringing back water, bringing back filters, and bringing back data, each with specific advantages and constraints in terms of labor, transport, and data acquisition. Additionally, innovations in eDNA sampling equipment, including autonomous drones, subsurface samplers, and in-situ filtration devices, and their applications in monitoring diverse taxa are discussed. Moreover, recent advancements in species-specific detection and eDNA metabarcoding are addressed, highlighting the integration of novel techniques such as CRISPR-Cas and nanopore sequencing that enable precise and rapid detection of biodiversity. The implications of environmental RNA and epigenetic modifications are considered for future applications in providing nuanced ecological data. Lastly, the review stresses the critical role of standardization and automation in enhancing data consistency and comparability for robust long-term biomonitoring. We propose that the amalgamation of these technologies represents a paradigm shift in ecological monitoring, aligning with the urgent call for biodiversity conservation and sustainable management of aquatic ecosystems.

An in situ digital synthesis strategy for the discovery and description of ocean life

Revolutionary advancements in underwater imaging, robotics, and genomic sequencing have reshaped marine exploration. We present and demonstrate an interdisciplinary approach that uses emerging quantitative imaging technologies, an innovative robotic encapsulation system with in situ RNA preservation and next-generation genomic sequencing to gain comprehensive biological, biophysical, and genomic data from deep-sea organisms. The synthesis of these data provides rich morphological and genetic information for species description, surpassing traditional passive observation methods and preserved specimens, particularly for gelatinous zooplankton. Our approach enhances our ability to study delicate mid-water animals, improving research in the world's oceans.

Salt-Adaptively Conductive Ionogel Sensor for Marine Sensing

Hydrophobic ionogel has attracted much attention in underwater sensing as the artificial electronic skins and wearable sensors. However, when the low conductive ionogel-based sensor works in the marine environment, the salty seawater weakens its sensing performance, which is difficult to recognize. Herein, a salt-adaptively conductive ionogel with high submarine strain sensitivity is reported. Based on the preliminary improvement via the proton conduction mechanism, the conductivity of the ionogel further increases with the surrounding salinity rising up since the salt-induced dissociation phenomenon, which is described as the environmental salt-adaptive feature. In seawater, the conductivity of the ionogel is as high as 2.90 × 10-1 S m-1 . Significantly, with its long-term underwater stability and adhesion, the resultant ionogel-based sensor features prominent strain sensing performance (gauge factor: 1.12) while combining with various soft actuators in the marine environment. The ionogel-based sensor is capable of monitoring human breath frequency, human actions, and the locomotion of soft actuators, demonstrating its great potential in diving detection and intelligent preceptive soft robotics for marine environmental protection and exploration.

Bioinspired soft robots for deep-sea exploration

The deep ocean, Earth's untouched expanse, presents immense challenges for exploration due to its extreme pressure, temperature, and darkness. Unlike traditional marine robots that require specialized metallic vessels for protection, deep-sea species thrive without such cumbersome pressure-resistant designs. Their pressure-adaptive forms, unique propulsion methods, and advanced senses have inspired innovation in designing lightweight, compact soft machines. This perspective addresses challenges, recent strides, and design strategies for bioinspired deep-sea soft robots. Drawing from abyssal life, it explores the actuation, sensing, power, and pressure resilience of multifunctional deep-sea soft robots, offering game-changing solutions for profound exploration and operation in harsh conditions.

Dynamic robotic tracking of underwater targets using reinforcement learning

To realize the potential of autonomous underwater robots that scale up our observational capacity in the ocean, new techniques are needed. Fleets of autonomous robots could be used to study complex marine systems and animals with either new imaging configurations or by tracking tagged animals to study their behavior. These activities can then inform and create new policies for community conservation. The role of animal connectivity via active movement of animals represents a major knowledge gap related to the distribution of deep ocean populations. Tracking underwater targets represents a major challenge for observing biological processes in situ, and methods to robustly respond to a changing environment during monitoring missions are needed. Analytical techniques for optimal sensor placement and path planning to locate underwater targets are not straightforward in such cases. The aim of this study was to investigate the use of reinforcement learning as a tool for range-only underwater target-tracking optimization, whose promising capabilities have been demonstrated in terrestrial scenarios. To evaluate its usefulness, a reinforcement learning method was implemented as a path planning system for an autonomous surface vehicle while tracking an underwater mobile target. A complete description of an open-source model, performance metrics in simulated environments, and evaluated algorithms based on more than 15 hours of at-sea field experiments are presented. These efforts demonstrate that deep reinforcement learning is a powerful approach that enhances the abilities of autonomous robots in the ocean and encourages the deployment of algorithms like these for monitoring marine biological systems in the future.

Autonomous agents for observing marine life

Using artificial intelligence to facilitate autonomy in robotics will provide new insights into marine life.

Semi-supervised Visual Tracking of Marine Animals Using Autonomous Underwater Vehicles

In-situ visual observations of marine organisms is crucial to developing behavioural understandings and their relations to their surrounding ecosystem. Typically, these observations are collected via divers, tags, and remotely-operated or human-piloted vehicles. Recently, however, autonomous underwater vehicles equipped with cameras and embedded computers with GPU capabilities are being developed for a variety of applications, and in particular, can be used to supplement these existing data collection mechanisms where human operation or tags are more difficult. Existing approaches have focused on using fully-supervised tracking methods, but labelled data for many underwater species are severely lacking. Semi-supervised trackers may offer alternative tracking solutions because they require less data than fully-supervised counterparts. However, because there are not existing realistic underwater tracking datasets, the performance of semi-supervised tracking algorithms in the marine domain is not well understood. To better evaluate their performance and utility, in this paper we provide (1) a novel dataset specific to marine animals located at http://warp.whoi.edu/vmat/, (2) an evaluation of state-of-the-art semi-supervised algorithms in the context of underwater animal tracking, and (3) an evaluation of real-world performance through demonstrations using a semi-supervised algorithm on-board an autonomous underwater vehicle to track marine animals in the wild.

Multicomponent and multifunctional integrated miniature soft robots

Miniature soft robots with elaborate structures and programmable physical properties could conduct micromanipulation with high precision as well as access confined and tortuous spaces, which promise benefits in medical tasks and environmental monitoring. To improve the functionalities and adaptability of miniature soft robots, a variety of integrated design and fabrication strategies have been proposed for the development of miniaturized soft robotic systems integrated with multicomponents and multifunctionalities. Combining the latest advancement in fabrication technologies, intelligent materials and active control methods enable these integrated robotic systems to adapt to increasingly complex application scenarios including precision medicine, intelligent electronics, and environmental and proprioceptive sensing. Herein, this review delivers an overview of various integration strategies applicable for miniature soft robotic systems, including semiconductor and microelectronic techniques, modular assembly based on self-healing and welding, modular assembly based on bonding agents, laser machining techniques, template assisted methods with modular material design, and 3D printing techniques. Emerging applications of the integrated miniature soft robots and perspectives for the future design of small-scale intelligent robots are discussed.

Battery-free wireless imaging of underwater environments

Imaging underwater environments is of great importance to marine sciences, sustainability, climatology, defense, robotics, geology, space exploration, and food security. Despite advances in underwater imaging, most of the ocean and marine organisms remain unobserved and undiscovered. Existing methods for underwater imaging are unsuitable for scalable, long-term, in situ observations because they require tethering for power and communication. Here we describe underwater backscatter imaging, a method for scalable, real-time wireless imaging of underwater environments using fully-submerged battery-free cameras. The cameras power up from harvested acoustic energy, capture color images using ultra-low-power active illumination and a monochrome image sensor, and communicate wirelessly at net-zero-power via acoustic backscatter. We demonstrate wireless battery-free imaging of animals, plants, pollutants, and localization tags in enclosed and open-water environments. The method’s self-sustaining nature makes it desirable for massive, continuous, and long-term ocean deployments with many applications including marine life discovery, submarine surveillance, and underwater climate change monitoring.

The authors present an approach to underwater imaging, which does not require tethering or batteries. The low-power camera uses power from harvested acoustic energy and communicates colour images wirelessly via acoustic backscatter.

AURORA, a multi-sensor dataset for robotic ocean exploration

The current maturity of autonomous underwater vehicles (AUVs) has made their deployment practical and cost-effective, such that many scientific, industrial and military applications now include AUV operations. However, the logistical difficulties and high costs of operating at sea are still critical limiting factors in further technology development, the benchmarking of new techniques and the reproducibility of research results. To overcome this problem, this paper presents a freely available dataset suitable to test control, navigation, sensor processing algorithms and others tasks. This dataset combines AUV navigation data, sidescan sonar, multibeam echosounder data and seafloor camera image data, and associated sensor acquisition metadata to provide a detailed characterisation of surveys carried out by the National Oceanography Centre (NOC) in the Greater Haig Fras Marine Conservation Zone (MCZ) of the U.K in 2015.

Modeling characterization of the vertical and temporal variability of environmental DNA in the mesopelagic ocean

Increasingly, researchers are using innovative methods to census marine life, including identification of environmental DNA (eDNA) left behind by organisms in the water column. However, little is understood about how eDNA is distributed in the ocean, given that organisms are mobile and that physical and biological processes can transport eDNA after release from a host. Particularly in the vast mesopelagic ocean where many species vertically migrate hundreds of meters diurnally, it is important to link the location at which eDNA was shed by a host organism to the location at which eDNA was collected in a water sample. Here, we present a one-dimensional mechanistic model to simulate the eDNA vertical distribution after its release and to compare the impact of key biological and physical parameters on the eDNA vertical and temporal distribution. The modeled vertical eDNA profiles allow us to quantify spatial and temporal variability in eDNA concentration and to identify the most important parameters to consider when interpreting eDNA signals. We find that the vertical displacement by advection, dispersion, and settling has limited influence on the eDNA distribution, and the depth at which eDNA is found is generally within tens of meters of the depth at which the eDNA was originally shed from the organism. Thus, using information about representative vertical migration patterns, eDNA concentration variability can be used to answer ecological questions about migrating organisms such as what depths species can be found in the daytime and nighttime and what percentage of individuals within a species diurnally migrate. These findings are critical both to advance the understanding of the vertical distribution of eDNA in the water column and to link eDNA detection to organism presence in the mesopelagic ocean as well as other aquatic environments.

Stealthy tracking of deep ocean organisms with Mesobot

Tracking deep ocean animals over their daily cycles will revolutionize our understanding of the largest biome on Earth.