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Nowadays, more frequently, it is necessary to perform underwater operations such as surveying an area or inspecting and intervening on industrial infrastructures such as offshore oil and gas rigs or pipeline networks. Recently, the use of Autonomous Underwater Vehicles (AUV) has grown as a way to automate these tasks, reducing risks and execution time. One of the used sensing modalities is vision, providing RGB high-quality information in the mid to low range, making it appropriate for manipulation or detail inspection tasks. This work presents the use of a deep neural network to perform pixel-wise 3D segmentation of pipes and valves on underwater point clouds generated using a stereo pair of cameras. In addition, two novel algorithms are built to extract information from the detected instances, providing pipe vectors, gripping points, the position of structural elements such as elbows or connections, and valve type and orientation. The information extracted on spatially referenced point clouds can be unified to form an information map of an inspected area. Results show outstanding performance on the network segmentation task, achieving a mean F1-score value of 88.0% at a pixel-wise level and of 95.3% at an instance level. The information extraction algorithm also showcased excellent metrics when extracting information from pipe instances and their structural elements and good enough metrics when extracting data from valves. Finally, the neural network and information algorithms are implemented on an AUV and executed in real-time, validating that the output information stream frame rate of 0.72 fps is high enough to perform manipulation tasks and to ensure full seabed coverage during inspection tasks. The used dataset, along with a trained model and the information algorithms, are provided to the scientific community. [ABSTRACT FROM AUTHOR]

This paper presents a survey on optical sensors and methods for 3D reconstruction in underwater environments. The techniques to obtain range data have been listed and explained, together with the different sensor hardware that makes them possible. The literature has been reviewed, and a classification has been proposed for the existing solutions. New developments, commercial solutions and previous reviews in this topic have also been gathered and considered. [ABSTRACT FROM AUTHOR]

This article focuses on the coordination and communication aspects of multi-robot systems (MRS) and explores what, how and when robots should coordinate and communicate in marine environment. It includes a comparative analysis of MRS coordination under communication constraints in aerial, terrestrial, and marine environments. In the light of the gaps identified in the current MRS literature, we focus on how to tackle the inherent communication constraints presents in marine environments. Thus, a review of Marine Multi-Robot systems (MMRS) is presented, proposing a taxonomy that classifies them according to how they communicate and coordinate. In this line, the classification of different communication strategies found in the literature has received significant attention. Additionally, a MMRS coordination approach is proposed where a heterogeneous group of marine vehicles must explore an unknown area maximizing communication. The proposed approach uses a real characterization of the acoustic communication signal quality as input to the decision making process. To validate its effectiveness, our approach has been subjected to a series of highly realistic experiments. • Review of the state of the art of multi-robot systems with communication limitations. • Wireless communication technologies used marine environment and their constraints • A new taxonomy of MMRS is proposed focusing on the coordination and the communication constraints. • A novel MMRS coordination algorithm is proposed that takes into account communication constraints. • Algorithm validation through simulation experiments implemented on Robot Operating System (ROS). [ABSTRACT FROM AUTHOR]

This paper describes an advanced methodology to monitor and assess, in temporal series, meadows of the seagrass Posidonia oceanica. The process includes, the following steps: (a) exploring marine regions of certain biological interest with Autonomous Underwater Vehicles equipped with cameras looking downwards, (b) taking images continuously during missions of preprogrammed trajectories, (c) processing the images off-line to build colour photomosaics, (d) segmenting seagrass from the background in every image using a pretrained Neural Network, (e) computing the same photomosaics but using the segmented images, and (f) computing automatically the bottom coverage of the seagrass counting the proportion of pixels labelled positively. This procedure avoids the involvement of divers, allows increasing depth, extension and duration of missions and offers 2D maps of the whole inspected areas in a single view, which allows us to get more accurate coverage measurements than those obtained with traditional techniques. Experiments have been performed with datasets collected in areas of the Balearic Islands colonized with P. oceanica seagrass and subject to low and high touristic and anchoring pressure during high season, repeating the same transects in consecutive years in order to obtain interannual results. Data obtained with this methodology permit a direct biological qualitative, quantitative and temporal analysis and interpretation, such as the percentage of temporal decline of seagrass coverage in some of the surveyed areas and the annual increase of the meadows extension in others. • Classical methods to explore and obtain data from submarine habitats are based, either on divers, remote sensing or ROVs. • Diver-based data is limited in depth, temporal and espatial range, and the post-processed information is obtained through extrapolations.. • Visual remote sensing is limited by the capacity of cameras to perceive the bottom as the depths increases.. • ROVs permit "in situ" observations, but they are costly, need to be attached to a support vessel and cannot perform pre-programmed trajectories. • Here, interannual submarine data is obtained with an AUV and seagrass coverage is computed using advanced image processing techniques and photo-mosaics. [ABSTRACT FROM AUTHOR]

In this paper, we present the design, development and a practical use of an Autonomous Surface Vehicle (ASV) as a modular and flexible platform for a large variety of marine tasks including the coordination strategies with other marine robots. This work tackles the integration of an open-source Robot-Operating-System (ROS)-based control architecture that provides the ASV with a wide variety of navigation behaviors. These new ASV capabilities can be used to acquire useful data from the environment to survey, map, and characterize marine habitats. In addition, the ASV is used as a radio frequency relay point between an Autonomous Underwater Vehicle (AUV) and the ground station as well as to enhance the Acoustic Communication Link (ACL) with the AUV. In order to improve the quality of the ACL, a new Marine Multirobot System (MMRS) coordination strategy has been developed that aims to keep both vehicles close to each other. The entire system has been successfully designed, implemented, and tested in real marine environment robotic tasks. The experimental tests show satisfactory results both in ROS-based navigation architecture and the MMRS coordination strategy resulting in a significant improvement of the quality of the ACL. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for observing and analyzing marine ecosystems using images gathered from autonomous marine vehicles. Visual data is composed in photo-mosaics and classified using machine learning algorithms. The approach expands existing solutions, enabling extended monitoring in time, space, and depth. Imagery was collected during a field campaign in the Spanish marine and terrestrial protected area of Cabrera, Balearic Islands, colonized by the endemic seagrass species Posidonia oceanica (Po). The operations were performed using three distinct platforms, an Autonomous Underwater Vehicle (AUV), an Autonomous Surface Vehicle (ASV) and a Lagrangian Drifter (LD). Results are compared to prior habitat maps to assess seagrass meadow distribution. The proposed solution can be scaled and adapted to other locations and species, considering limitations in data storage and battery endurance. [Display omitted] • Seafloor habitat mapping with autonomous robots, filling gaps missed by divers. • Assessment of marine habitats using convolutional neural networks. • Methods apply to georeferenced imaging (e.g. drones, satellites) beyond subsea mapping. • Field campaign data from a marine area compared to previous habitat map data. [ABSTRACT FROM AUTHOR]

The use of commercially available autonomous underwater vehicles (AUVs) has increased during the last fifteen years. While they are mainly used for routine survey missions, there is a set of applications that nowadays can be only addressed by manned submersibles or work-class remotely operated vehicles (ROVs) equipped with teleoperated arms: the intervention applications. To allow these heavy vehicles controlled by human operators to perform intervention tasks, underwater structures like observatory facilities, subsea panels or oil-well Christmas trees have been adapted, making them more robust and easier to operate. The TRITON Spanish founded project proposes the use of a light-weight intervention AUV (I-AUV) to carry out intervention applications simplifying the adaptation of these underwater structures and drastically reducing the operational cost. To prove this concept, the Girona 500 I-AUV is used to autonomously dock into an adapted subsea panel and once docked perform an intervention composed of turning a valve and plugging in/unplugging a connector. The techniques used for the autonomous docking and manipulation as well as the design of an adapted subsea panel with a funnel-based docking system are presented in this article together with the results achieved in a water tank and at sea. [ABSTRACT FROM AUTHOR]

This paper presents a new solution for underwater observation, image recording, mapping and 3D reconstruction in shallow waters. The platform, designed as a research and testing tool, is based on a small underwater robot equipped with a MEMS-based IMU, two stereo cameras and a pressure sensor. The data given by the sensors are fused, adjusted and corrected in a multiplicative error state Kalman filter (MESKF), which returns a single vector with the pose and twist of the vehicle and the biases of the inertial sensors (the accelerometer and the gyroscope). The inclusion of these biases in the state vector permits their self-calibration and stabilization, improving the estimates of the robot orientation. Experiments in controlled underwater scenarios and in the sea have demonstrated a satisfactory performance and the capacity of the vehicle to operate in real environments and in real time. [ABSTRACT FROM AUTHOR]

Over recent decades, we have witnessed a widescale deterioration in seagrass habitats in the Mediterranean Sea. Although the causes underlying this regression are not yet clear, there is a broad consensus that new methods are required to generate high-quality data on seagrass meadows. Specifically, there is a lack of available data in order to assess changes over time in the spatial distribution of seagrass. This article introduces a new methodology to ease data-gathering operations in Posidonia oceanica meadows for benthic mapping, by using the latest developments in lightweight autonomous vehicles and image processing. The proposed methodology has been designed to build a series of online maps, by employing an input data feed based on an encoder–decoder convolutional neural network (CNN) that automatically segments the images recorded by the vehicle. In turn, it uses a sparse Gaussian Process (GP) to map uncertainty linked to the spatial distribution of seagrass. This article presents the most suitable CNN and GP, as well as performance validation results for the method, based on data acquired in three field tests. • Ease the generation of seagrass data on the spatial distribution. • Use encoder–decoder CNN to segment benthic images online. • Use sparse GP to map uncertainty. • Complete assessment of different sparse GPs and CNNs configurations. • Set of experiments conducted in marine scenarios colonised with Posidonia Oceanica. [ABSTRACT FROM AUTHOR]

In this study, we demonstrated a novel jumping robot that has the ability of accurate obstacle-crossing jumping and aerial pitch control. The novel robot can quickly leap high into the air with a powerful water jet thruster. The robot was designed to overcome multiple general obstacles via accurate jumping. Then a modified whale optimization algorithm (MWOA) was proposed to determine an optimized jumping trajectory according to the form of obstacles. By comparing with classical intelligent optimization algorithms, the MWOA revealed superiority in convergence rate and precision. Besides, the dynamics model of aerial pitch control was built and its effect was verified by the pitch control experiment. Lastly, the robot's obstacle-crossing experiments were performed and the results validated the robot's good ability of obstacle-crossing and aerial body righting. We believe the optimization of trajectory and the pitch control are of great help for the jumping robot's complex jumping and obstacle-crossing performance. [ABSTRACT FROM AUTHOR]

The concept of the Marine Autonomous Surface Ship (MASS) requires new solutions in many areas: from law, through economics, social sciences, environmental issues to the technology and even ethics. It also plays a central role in the work of numerous research teams dealing with the ship motion control systems. This article presents the results of the experiments with application of the selected control methods in automatic steering of the movement of an autonomous ship in the two regimes: during the maneuvers at low speed (in a harbor confined waters) and during the lake trials in open water conditions. In the first case, multidimensional state controller synthesized with Linear Matrix Inequalities (LMI) algorithms was used, while, in the second case, Model Predictive Control (MPC) control was adopted. The object for which the experiments were carried out was 1:24 scale model of the Liquefied Natural Gas (LNG) carrier. The paper presents also the design of the measurement and control system and the user interface. The experiments were conducted in the natural conditions on the lake. The results of the experiments indicate the fundamental role of the measurement system in the process of controlling an autonomous ship. [ABSTRACT FROM AUTHOR]

Adhesion systems are very important in robots for infrastructure inspection (especially in vertical walls). They present the challenge of optimizing the ratio vacuum/power consumption in battery-powered robots. In this paper, a CFD (computer fluid dynamics) modelling and optimization process of a robot adhesion system is carried out to determine the best performing configuration in terms of vacuum and power consumption. Analytical and numerical models were developed to predict the behaviour of the system for different configurations. The models were validated, using test rig measurements, by calibrating an arbitrary defined inlet height that simulates the leakage flow. Then, different geometric parameters were varied to determine the best performing configuration based on the vacuum/power consumption ratio value. The model presented in the paper was capable of predicting the behaviour of the system for different configurations, with a margin of error of 15% for the vacuum prediction and 25% for the motor power calculation. Finally, the model was used to optimize parameters of the system, like the number of blades of the impeller. The adhesion system was conceived for the modular autonomous climbing legged robot ROMERIN. [ABSTRACT FROM AUTHOR]