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As the hydrodynamic model of a surface vehicle is highly nonlinear and of high dimension, an extensive set of experiments is required to estimate the parameters of the model. Typically, the dynamic behavior of a surface vehicle is investigated through experimental tests such as planar motion mechanisms or free running tests in specialized test facilities. However, the cost of such tests is very high, and it is challenging to design effective and efficient test conditions, especially when the model is represented by numerous parameters such as neural networks. In this paper, we propose an efficient data collection strategy for modeling the dynamics of a floating robotic vehicle using neural networks. For generalized modeling, it is important to collect diverse data by exploring all the reachable state space of the vehicle. To achieve this, a data collection policy is built based on sampling-based planning toward reducing the uncertainty of the neural network-based model. The proposed method allows for collecting more comprehensive data than other commonly used random-based data collection policies. We show experimentally that the proposed method enables more accurate modeling of both fully actuated and under-actuated floating robotic vehicles, as demonstrated by the effective execution of various tasks. [ABSTRACT FROM AUTHOR]

Station keeping is an essential maneuver for autonomous surface vehicles (ASVs), mainly when used in confined spaces, to carry out surveys that require the ASV to keep its position or in collaboration with other vehicles where the relative position has an impact over the mission. However, this maneuver can become challenging for classic feedback controllers due to the need for an accurate model of the ASV dynamics and the environmental disturbances. This work proposes a model predictive controller using neural network simulation error minimization (NNSEM–MPC) to accurately predict the dynamics of the ASV under wind disturbances. The performance of the proposed scheme under wind disturbances is tested and compared against other controllers in simulation, using the robotics operating system and the multipurpose simulation environment Gazebo. A set of six tests was conducted by combining two varying wind speeds that are modeled as the Harris spectrum and three wind directions (0° ${0}^{^\circ }$, 90° ${90}^{^\circ }$, and 180° ${180}^{^\circ }$). The simulation results clearly show the advantage of the NNSEM–MPC over the following methods: backstepping controller, sliding mode controller, simplified dynamics MPC (SD‐MPC), neural ordinary differential equation MPC (NODE‐MPC), and knowledge‐based NODE MPC. The proposed NNSEM–MPC approach performs better than the rest in five out of the six test conditions, and it is the second best in the remaining test case, reducing the mean position and heading error by at least 27.08 $27.08$% (0.38 $0.38$ m) and 41.02 $41.02$% (0.93° $0.9{3}^{^\circ }$), respectively, across all the test cases. In terms of execution speed, the proposed NNSEM–MPC is at least 36% faster than the rest of the MPC controllers. The field experiments on two different ASV platforms showed that ASVs can effectively keep the station utilizing the proposed method, with a position error as low as 1.68 $1.68$ m and a heading error as low as 6.14° $6.1{4}^{^\circ }$ within time windows of at least 150 $150$ s. This would increase the potential applications of ASVs for launch, recovery, and replenishment in long‐term surveys in collaboration with other autonomous systems. [ABSTRACT FROM AUTHOR]

Digital Twins of the Ocean (DTO) are a rapidly emerging topic that has attracted significant interest from scientists in recent years. The initiative, strongly driven by the EU, aims to create a digital replica of the ocean to better understand and manage marine environments. The Iliad project, funded under the EU Green Deal call, is developing a framework to support multiple interoperable DTO using a federated systems-of-systems approach across various fields of applications and ocean areas, called pilots. This paper presents the results of a Water Quality DTO pilot located in the Trondheim fjord in Norway. This paper details the building blocks of DTO, specific to this environmental monitoring pilot. A crucial aspect of any DTO is data, which can be sourced internally, externally, or through a hybrid approach utilizing both. To realistically twin ocean processes, the Water Quality pilot acquires data from both surface and benthic observatories, as well as from mobile sensor platforms for on-demand data collection. Data ingested into an InfluxDB are made available to users via an API or an interface for interacting with the DTO and setting up alerts or events to support 'what-if' scenarios. Grafana, an interactive visualization application, is used to visualize and interact with not only time-series data but also more complex data such as video streams, maps, and embedded applications. An additional visualization approach leverages game technology based on Unity and Cesium, utilizing their advanced rendering capabilities and physical computations to integrate and dynamically render real-time data from the pilot and diverse sources. This paper includes two case studies that illustrate the use of particle sensors to detect microplastics and monitor algae blooms in the fjord. Numerical models for particle fate and transport, OpenDrift and DREAM, are used to forecast the evolution of these events, simulating the distribution of observed plankton and microplastics during the forecasting period. [ABSTRACT FROM AUTHOR]

Disposal of industrial and hazardous waste in the ocean was a pervasive global practice in the 20th century. Uncertainty in the quantity, location, and contents of dumped materials underscores ongoing risks to marine ecosystems and human health. This study presents an analysis of a wide-area side-scan sonar survey conducted with autonomous underwater vehicles (AUVs) at a dump site in the San Pedro Basin, California. Previous camera surveys located 60 barrels and other debris. Sediment analysis in the region showed varying concentrations of the insecticidal chemical dichlorodiphenyltrichloroethane (DDT), of which an estimated 350-700 t were discarded in the San Pedro Basin between 1947 and 1961. A lack of primary historical documents specifying DDT acid waste disposal methods has contributed to the ambiguity surrounding whether dumping occurred via bulk discharge or containerized units. Barrels and debris observed during previous surveys were used for ground truth classification algorithms based on size and acoustic intensity characteristics. Image and signal processing techniques identified over 74,000 debris targets within the survey region. Statistical, spectral, and machine learning methods characterize seabed variability and classify bottom-type. These analytical techniques combined with AUV capabilities provide a framework for efficient mapping and characterization of uncharted deep-water disposal sites.

The design of an autonomous underwater vehicle (AUV) with physical dimensions of 1100 mm × 700 mm × 330 mm, and weight of 55 kg, is introduced herein. This paper describes the design, materials, hydrodynamics, and system architecture of an AUV prototype named Synoris, developed as a low-cost and medium-scale testbed platform. Synoris moves via six brushless motors, can reach up to 200 m depth, has an autonomy estimated around 6 hours and a modular design for multiple payload options. Stability control, autonomous movement, obstacle avoidance temperature/pressure sensing, and video/image capturing are simultaneously performed by exploiting a set of onboard computers that are described briefly in Section 4. The whole platform is built on top of the open source software called ROS (robotic operating system) that provides a flexible framework for writing robot software by providing services such as low-level device control, message parsing, data fusion, and system integration. Synoris is ideal for underwater applications and missions, involving machine learning and computer vision features. AUV development in general meets high-cost solutions due to the complexity and harshness of the operational environment. Even the most cost-effective solutions demand plentiful resources. This paper describes the entire process of development and how a relatively low-cost approach can provide a reliable AUV for many underwater applications, involving AI and machine-learning capabilities. [ABSTRACT FROM AUTHOR]

Remotely operated vehicles (ROVs) provide practical solutions for a wide range of activities in a particularly challenging domain, despite their dependence on support ships and operators. Recent advancements in AI, machine learning, predictive analytics, control theories, and sensor technologies offer opportunities to make ROVs (semi) autonomous in their operations and to remotely test and monitor their dynamics. This study moves towards that goal by formulating a complete navigation, guidance, and control (NGC) system for a six DoF BlueROV2, offering a solution to the current challenges in the field of marine robotics, particularly in the areas of power supply, communication, stability, operational autonomy, localization, and trajectory planning. The vehicle can operate (semi) autonomously, relying on a sensor acoustic USBL localization system, tethered communication with the surface vessel for power, and a line of sight (LOS) guidance system. This strategy transforms the path control problem into a heading control problem, aligning the vehicle's movement with a dynamically calculated reference point along the desired path. The control system uses PID controllers implemented in the navigator flight controller board. Additionally, an infrastructure has been developed that synchronizes and communicates between the real ROV and its digital twin within the Unity environment. The digital twin acts as a visual representation of the ROV's movements and considers hydrodynamic behaviors. This approach combines the physical properties of the ROV with the advanced simulation and analysis capabilities of its digital counterpart. All findings were validated at the Point Rouge port located in Marseille and at the port of Ancona. The NGC implemented has proven positive vehicle stability and trajectory tracking in time despite external interferences. Additionally, the digital part has proven to be a reliable infrastructure for a future bidirectional communication system. [ABSTRACT FROM AUTHOR]

Shallow underwater environments around the world are contaminated with unexploded ordnances (UXOs). Current state-of-the-art methods for UXO detection and localization use remote sensing systems. Furthermore, human divers are often tasked with confirming UXO existence and retrieval which poses health and safety hazards. In this paper, we describe the application of a crab robot with leg-embedded Hall effect-based sensors to detect and distinguish between UXOs and non-magnetic objects partially buried in sand. The sensors consist of Hall-effect magnetometers and permanent magnets embedded in load bearing compliant segments. The magnetometers are sensitive to magnetic objects in close proximity to the legs and their movement relative to embedded magnets, allowing for both proximity and force-related feedback in dynamically obtained measurements. A dataset of three-axis measurements is collected as the robot steps near and over different UXOs and UXO-like objects, and a convolutional neural network is trained on time domain inputs and evaluated by 5-fold cross validation. Additionally, we propose a novel method for interpreting the importance of measurements in the time domain for the trained classifier. The results demonstrate the potential for accurate and efficient UXO and non-UXO discrimination in the field. [ABSTRACT FROM AUTHOR]

Marine robotics is a complex field with a potentially high demanding logistics and high deployment cost. This is a barrier to many research groups. On the other hand, some research groups have substantial equipment and infrastructure that are typically underutilized. The push for sharing infrastructures was recently accentuated due to the COVID-19 pandemic. In particular, remote access experiments became the norm during the lockdown periods. LABUST completed new infrastructure during the COVID-19 pandemic and prepared it for state-of-the-art remote access from anywhere in the globe. This is important both for research and educational purposes. This article describes the infrastructure, equipment, and methods used for implementing the remote access including the calibration procedures. It also reports on real remote access trials for different applications such as Autonomous ships, diver–robot interaction, and Remotely Operated Vehicle (ROV) pilots. These examples include both pool and at-sea remote trials, proving the capabilities of the infrastructure. [ABSTRACT FROM AUTHOR]

Mechanical Scanning Sonars (MSS) are popular underwater sensors for Unmanned Underwater Vehicles (UUV) due to their low cost, small size, and low power consumption. But due to their simplicity, there are also many research challenges related to their usage. Unfortunately, there is also a lack of data with ground truth UUV localization. We provide MSS datasets using a UUV with standard navigation sensors, i.e., an Inertial Measurement Unit (IMU) and a Doppler Velocity Log (DVL). The UUV is globally localized with a high precision optical tracking system in a large research pool to provide ground truth. The data is of interest for multiple research areas related to MSS, e.g., extraction of range information, registration of sonar scans, and especially mapping including Simultaneous Localization and Mapping (SLAM). Different parameter settings and environment conditions are covered, e.g., dynamics in the scene. The IMU and DVL data is also of interest for research on navigation independent of the MSS data. Results from navigation and mapping with an Extended Kalman Filter (EKF) are in addition provided as baseline solutions.

Digital Twins of the Ocean (DTO) are a rapidly emerging topic that has attracted significant interest from scientists in recent years. The initiative, strongly driven by the EU, aims to create a digital replica of the ocean to better understand and manage marine environments. The Iliad project, funded under the EU Green Deal call, is developing a framework to support multiple interoperable DTO using a federated systems-of-systems approach across various fields of applications and ocean areas, called pilots. This paper presents the results of a Water Quality DTO pilot located in the Trondheim fjord in Norway. This paper details the building blocks of DTO, specific to this environmental monitoring pilot. A crucial aspect of any DTO is data, which can be sourced internally, externally, or through a hybrid approach utilizing both. To realistically twin ocean processes, the Water Quality pilot acquires data from both surface and benthic observatories, as well as from mobile sensor platforms for on-demand data collection. Data ingested into an InfluxDB are made available to users via an API or an interface for interacting with the DTO and setting up alerts or events to support ’what-if’ scenarios. Grafana, an interactive visualization application, is used to visualize and interact with not only time-series data but also more complex data such as video streams, maps, and embedded applications. An additional visualization approach leverages game technology based on Unity and Cesium, utilizing their advanced rendering capabilities and physical computations to integrate and dynamically render real-time data from the pilot and diverse sources. This paper includes two case studies that illustrate the use of particle sensors to detect microplastics and monitor algae blooms in the fjord. Numerical models for particle fate and transport, OpenDrift and DREAM, are used to forecast the evolution of these events, simulating the distribution of observed plankton and microplastics during the forecasting period.

This paper presents a novel method for the visual-based 3D mapping of underwater cultural heritage sites through marine robotic operations. The proposed methodology addresses the three main stages of an underwater robotic mission, specifically the planning phase, the mission-time and the offline processing phase. Initially, we approach mission planning through multi-vision sensor configurations and simulations of the underwater medium’s effects. Subsequently, we demonstrate a possibility for real-time 3D surface reconstruction and hole detection by using Poisson Surface Reconstruction (PSR) and the Ball Pivoting Algorithm (BPA), that allows for real-time quality assessment of the acquired data and control of the coverage of the site. Last, an offline photogrammetric workflow is discussed in terms of geometric reliability and visual appearance of the results. The presented three-step methodological framework has been developed and tested in both simulation and real-world environments for three wreck sites in the fjord of Trondheim, Norway, introducing among others novel marine robotic technology like the articulated robot Eelume.

Getting students motivated and interested in their education can be challenging in any classroom setting, even more so in an online learning environment. In this spectrum, educational robotics (ER) has demonstrated numerous advantages in the educational environment, not only by facilitating teaching, but also enabling the cultivation of manyfold skills, including creativity, problem‐solving, and teamwork. Meanwhile, many methods have been developed with the aid of technology to improve the teaching process and boost students' ability to learn. Blended learning is one approach that integrates conventional classroom methods with digital resources in an effort to foster students' creativity. But how can blended learning be combined with robotics? The objective of this paper is to evaluate the impact of employing an underwater vehicle, called educational underwater vehicle (EDUV), in conjunction with a dedicated programming learning platform within the context of a programming course that is offered at the high school level. In this work, this platform is utilized by students in secondary education, and a survey was conducted prior and after using the underwater vehicle's platform based on two questionnaires. The survey included 112 Greek participants, 64 males and 48 females in the age range of 14–18 years old. The experimental results show an increase in their motivation and creativity. In other words, they are more engaged in the classroom and the lesson becomes more enjoyable. More specifically, the survey revealed that most participants are familiar with computers but have limited knowledge of robotics and programming. After training on the EDUV platform, participants showed a significant increase in correct responses for Python and Blockly environments, with an average of 50.7% in four programming‐related questions. The platform also reduced "do not know" replies, which means that the student's self‐esteem increased. The paired sample T‐test showed that the EDUV platform positively influenced participants' perceptions of robotics and motivated them to further their education. In this paper, the related work is discussed, and the architecture of the vehicle is analyzed, along with the integration with the online platform. In addition, the methodology performed is explained and divided into steps. Finally, the experimental results are discussed. Instructions, 3D models, and code can be found in the github repository https://github.com/MariosVasileiou/EDUV. [ABSTRACT FROM AUTHOR]

This report summarizes the recent research projects carried out at the Collaborative Robotics and Adaptive Machines Laboratory, Old Dominion University. The topics include collaborative robotics strategies for non-repetitive micro-drilling tasks, hand-held robots for spillage-free specimen retrieval tasks, development of a three-dimensional simulator for autonomous underwater vehicles, and enhancing search efficiency of glowworm-inspired robotic swarms for multiple source localization tasks. Research in these areas has applications in autonomous systems, advanced manufacturing, and health care. [ABSTRACT FROM AUTHOR]

Remotely operated vehicles (ROVs) provide practical solutions for a wide range of activities in a particularly challenging domain, despite their dependence on support ships and operators. Recent advancements in AI, machine learning, predictive analytics, control theories, and sensor technologies offer opportunities to make ROVs (semi) autonomous in their operations and to remotely test and monitor their dynamics. This study moves towards that goal by formulating a complete navigation, guidance, and control (NGC) system for a six DoF BlueROV2, offering a solution to the current challenges in the field of marine robotics, particularly in the areas of power supply, communication, stability, operational autonomy, localization, and trajectory planning. The vehicle can operate (semi) autonomously, relying on a sensor acoustic USBL localization system, tethered communication with the surface vessel for power, and a line of sight (LOS) guidance system. This strategy transforms the path control problem into a heading control problem, aligning the vehicle’s movement with a dynamically calculated reference point along the desired path. The control system uses PID controllers implemented in the navigator flight controller board. Additionally, an infrastructure has been developed that synchronizes and communicates between the real ROV and its digital twin within the Unity environment. The digital twin acts as a visual representation of the ROV’s movements and considers hydrodynamic behaviors. This approach combines the physical properties of the ROV with the advanced simulation and analysis capabilities of its digital counterpart. All findings were validated at the Point Rouge port located in Marseille and at the port of Ancona. The NGC implemented has proven positive vehicle stability and trajectory tracking in time despite external interferences. Additionally, the digital part has proven to be a reliable infrastructure for a future bidirectional communication system.

Hydrobatic autonomous underwater vehicles (AUVs) can be efficient in range and speed, as well as agile in maneuvering. They can be beneficial in scenarios such as obstacle avoidance, inspections, docking, and under‐ice operations. However, such AUVs are underactuated systems—this means exploiting the system dynamics is key to achieving elegant hydrobatic maneuvers with minimum controls. This paper explores the use of model predictive control (MPC) techniques to control underactuated AUVs in hydrobatic maneuvers and presents new simulation and experimental results with the small and hydrobatic SAM AUV. Simulations are performed using nonlinear model predictive control (NMPC) on the full AUV system to provide optimal control policies for several hydrobatic maneuvers in Matlab/Simulink. For implementation on AUV hardware in robot operating system, a linear time varying MPC (LTV‐MPC) is derived from the nonlinear model to enable real‐time control. In simulations, NMPC and LTV‐MPC shows promising results to offer much more efficient control strategies than what can be obtained with PID and linear quadratic regulator based controllers in terms of rise‐time, overshoot, steady‐state error, and robustness. The LTV‐MPC shows satisfactory real‐time performance in experimental validation. The paper further also demonstrates experimentally that LTV‐MPC can be run real‐time on the AUV in performing hydrobatic maneouvers. [ABSTRACT FROM AUTHOR]

Since 2017, NTNU's Applied Underwater Robotics Laboratory has been developing an infrastructure for remote marine/subsea operations in Trondheim Fjord. The infrastructure, named the OceanLab subsea node, allows remote experimentation for three groups of assets: seabed infrastructure, surface or subsea vehicles/robots, and assets at remote experimentation sites. To achieve this task, a shoreside control room serves as a hub that enables efficient and diverse communication with assets in the field as well as with remote participants/operators. Remote experimentation has become more popular in recent years due to technological developments and convenience, the COVID-19 pandemic, and travel restrictions that were imposed. This situation has shown us that physical presence at the experimentation site is not necessarily the only option. Sharing of the infrastructure among different experts, which are geographically distributed, but participating in a single, local, real-time experiment, increases the level of expertise available and the efficiency of the operations. This paper also elaborates on the development of a virtual experimentation environment that includes simulators and digital twins of various marine vehicles, infrastructures, and the operational marine environment. By leveraging remote and virtual experimentation technologies, users and experts can achieve relevant results in a shorter time frame and at a reduced cost. [ABSTRACT FROM AUTHOR]

Determining the dynamics of surface vehicles and marine robots is important for developing marine autopilot and autonomous navigation systems. However, this often requires extensive experimental data and intense effort because they are highly nonlinear and involve various uncertainties in real operating conditions. Herein, we propose an efficient data-driven approach for analyzing and predicting the motion of a surface vehicle in a real environment based on deep learning techniques. The proposed multistep model is robust to measurement uncertainty and overcomes compounding errors by eliminating the correlation between the prediction results. Additionally, latent state representation and mixup augmentation are introduced to make the model more consistent and accurate. The performance analysis reveals that the proposed method outperforms conventional methods and is robust against environmental disturbances. [ABSTRACT FROM AUTHOR]

Mechanical Scanning Sonars (MSS) are popular devices for Unmanned Underwater Vehicles (UUV), i.e., Remotely Operated Vehicles (ROV) and Autonomous Underwater Vehicles (AUV), as they function under low visibility conditions and over extended ranges. They are comparatively low-cost and easy to integrate. But they require motion-compensation due to the low updates rates caused by the mechanical scanning and the slow speed of sound. We present here a new form of scan formation for MSS where the data from single beams is embedded into a pose-graph. The rendering of scans is not as usual based on only core navigation sensors, but it can improve in the spirit of a synthetic aperture. To this end, online Simultaneous Localization and Mapping (SLAM) is used to form scans from the single beams. These can be optimized and improved scans in turn lead to improved registration results in subsequent steps. This Synthetic Scan Formation (SSF) leads to better mapping results than state-of-the-art SLAM with MSS. The method is validated with several real-world experiments. First, different trajectories with precise ground-truth in a pool with a gantry set-up are used. Second, results from field trials in a WW-II submarine bunker are presented. It is shown that there are clear quantitative and qualitative improvements, and that SSF can be used in real-time for mapping during a mission.

Research on motion control systems for marine surface vessels has generated a vast academic literature and many industrial applications since the early 20th century. The recent focus on autonomous ships has sparked intensive research and innovation activities of dock-to-dock operations, including the final docking phase. Specifically, automated docking involves systems that enable vessels to dock safely, independently, and energy-efficiently at designated locations with a specific heading. Challenges include managing large sideslip angles, static and dynamic obstacles, and navigation in complex port geometries. Despite its complexity, the docking problem has received less attention compared to dynamic positioning or course-keeping systems. This paper therefore provides a thorough overview of the research on automated docking for marine surface vessels, from 1980 to June 2023. The paper introduces the Docking Characteristic Index (DCI) as a metric to identify the scope and approach of systems addressing various docking challenges, not as an evaluation of their overall quality. Using the calculated DCI-Scores, we discuss some of the works in more detail. The survey reveals a rising trend in publications and an increasing emphasis on experimental verification. Despite advancements, current methodologies exhibit trade-offs and limitations, particularly in handling dynamic obstacles, robustness against external forces, and situational awareness. The paper identifies the need for more comprehensive and integrated solutions to these challenges. As the demand for fully autonomous operations grows, the results suggest that future research should focus on developing holistic and robust docking strategies, that are verified experimentally, to achieve safe, efficient, and effective automated docking systems.

This article focuses on a small and cost-effective remotely operated unmanned underwater vehicle (ROV) that has the ability to move through shallow waters. The proposed ROV measures 35 cm × 26 cm × 23 cm and weighs 1.59 kg. The primary objective is to remotely communicate in real time using serial protocol to record the vehicle's orientation data. The three DC motors powering the ROV are controlled by a multi-parameter root-locus proportional-integral (MRPL-PI) controller. Utilizing the processing capabilities of the Atmega328 microcontroller, heading, depth control, and water condition monitoring are all accomplished simultaneously. The Arduino development environment is used to establish the ROV's remote control. The problem with human divers who can only descend to a depth of 30 meters is resolved by the proposed ROV's ability to dive up to 100 meters underwater. Also, the suggested vehicle can be beneficial in marine applications such as measurement, maintenance, operations, and surveillance. Furthermore, the preliminary testing carried out in the pool indicates that the vehicle is fully operational and responds to the pilot's commands efficiently. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm that provides operational strategies for multiple tethered autonomous underwater vehicle (T-AUV) systems for entanglement-free navigation. T-AUVs can perform underwater tasks under reliable communication and power supply, which is the most substantial benefit of their operation. Thus, if one can overcome the entanglement issues while utilizing multiple tethered vehicles, the potential applications of the system increase including ecosystem exploration, infrastructure inspection, maintenance, search and rescue, underwater construction, and surveillance. In this study, we focus on developing strategies for task allocation, path planning, and scheduling that ensure entanglement-free operations while considering workload balancing among the vehicles. We do not impose restrictions on the size or shape of the vehicles at this stage; our primary focus is on efficient tether management as an initial work on the topic. To achieve entanglement-free navigation, we propose a heuristic based on the primal-dual technique, which enables initial task allocation and path planning while minimizing the maximum travel cost of the vehicles. Although this heuristic often generates sectioned paths due to its workload-balancing nature, we also propose a mixed approach to provide feasible solutions for non-sectioned initial paths. This approach combines entanglement avoidance techniques with time scheduling and sectionalization methods. To evaluate the effectiveness of our algorithm, extensive simulations were conducted with varying problem sizes. The computational results demonstrate the potential of our algorithm to be applied in real-time operations, as it consistently generates reliable solutions within a reasonable time frame. [ABSTRACT FROM AUTHOR]

Marine robotics is a complex field with a potentially high demanding logistics and high deployment cost. This is a barrier to many research groups. On the other hand, some research groups have substantial equipment and infrastructure that are typically underutilized. The push for sharing infrastructures was recently accentuated due to the COVID-19 pandemic. In particular, remote access experiments became the norm during the lockdown periods. LABUST completed new infrastructure during the COVID-19 pandemic and prepared it for state-of-the-art remote access from anywhere in the globe. This is important both for research and educational purposes. This article describes the infrastructure, equipment, and methods used for implementing the remote access including the calibration procedures. It also reports on real remote access trials for different applications such as Autonomous ships, diver–robot interaction, and Remotely Operated Vehicle (ROV) pilots. These examples include both pool and at-sea remote trials, proving the capabilities of the infrastructure.

The Blue Economy, which is based on the sustainable use of the ocean, is demanding better understanding of marine ecosystems, which provide assets, goods, and services. Such understanding requires the use of modern exploration technologies, including unmanned underwater vehicles, in order to acquire quality information for decision-making processes. This paper addresses the design process for an underwater glider, to be used in oceanographic research, that was inspired by leatherback sea turtles (Dermochelys coriacea), which are known to have a superior diving ability and enhanced hydrodynamic performance. The design process combines elements from Systems Engineering and bioinspired design approaches. The conceptual and preliminary design stages are first described, and they allowed mapping the user's requirements into engineering characteristics, using quality function deployment to generate the functional architecture, which later facilitated the integration of the components and subsystems. Then, we emphasize the shell's bioinspired hydrodynamic design and provide the design solution for the desired vehicle's specifications. The bioinspired shell yielded a lift coefficient increase due to the effect of ridges and a decrease in the drag coefficient at low angles of attack. This led to a greater lift-to-drag ratio, a desirable condition for underwater gliders, since we obtained a greater lift while producing less drag than the shape without longitudinal ridges. [ABSTRACT FROM AUTHOR]

Abstract Artificial intelligence is penetrating various fields. The demand for interdisciplinary talent is increasingly important, while interdisciplinary educational activities for high school students are lagging behind. Project‐based learning (PBL) in artificial intelligence (AI) and robotic education activities supported by a robotic sailboat platform, the sailboat test arena (STAr), has been shown to popularise AI and robotic knowledge in young students. In the implementation of the programme, PBL was provided for students, and gamification pedagogy was applied to increase participants' learning motivation and engagement. The results show that the proposed STAr‐based programme is capable of delivering the desired knowledge and skills to students at high school levels. The assessment results suggest that most students achieve learning outcomes on average. Students showed more interest in AI and marine disciplines and were willing to participate in more such educational programs. The findings fill the research gap that few existing education platforms have facilitated the teaching and learning of AI and marine disciplines for high school students.

This paper presents a teach-and-repeat path-following method for an autonomous underwater vehicle (AUV) navigating long distances in environments where external navigation aides are denied. This method utilizes sonar images to construct a series of reference views along a path, stored as a topological map. The AUV can then renavigate along this path, either to return to the start location or to repeat the route. Utilizing unique assumptions about the sonar image-generation process, this system exhibits robust image-matching capabilities, providing observations to a discrete Bayesian filter that maintains an estimate of progress along the path. Image-matching also provides an estimate of offset from the path, allowing the AUV to correct its heading and effectively close the gap. Over a series of field trials, this system demonstrated online control of an AUV in the ocean environment of Holyrood Arm, Newfoundland and Labrador, Canada. The system was implemented on an International Submarine Engineering Ltd. Explorer AUV and performed multiple path completions over both a 1 and 5 km track. These trials illustrated an AUV operating in a fully autonomous mode, in which navigation was driven solely by sensor feedback and adaptive control. Path-following performance was as desired, with the AUV maintaining close offset to the path.

This work presents the nonlinear dynamical model and motion controller of a system consisting of an unmanned aerial vehicle (UAV) that is tethered to a floating buoy in the three-dimensional (3D) space. Detailed models of the UAV, buoy, and the coupled tethered system dynamics are presented in a marine environment that includes surface water currents and oscillating gravity waves, in addition to wind gusts. This work extends the previously modeled planar (vertical) motion of this novel robotic system to allow its free motion in all three dimensions. Furthermore, a directional surge velocity control system (DSVCS) is proposed to allow both the free movement of the UAV around the buoy when the cable is slack, and the manipulation of the buoy's surge velocity when the cable is taut. Using a spherical coordinates system centered at the buoy, the control system commands the UAV to apply forces on the buoy at specific azimuth and elevation angles via the tether, which yields a more appropriate realization of the control problem as compared to the Cartesian coordinates, where the traditional x-, y-, and z-coordinates do not intuitively describe the tether's tension and orientation. The proposed robotic system and controller offer a new method of interaction and collaboration between UAVs and marine systems from a locomotion perspective, and offers a low-cost and low-logistics alternative to crewed vessels for marine missions. It has potential applications in search-and-rescue missions, monitoring and sensing operations, and floating objects manipulation and retrieval. The system is validated in a virtual high-fidelity simulation environment, which was specifically developed for this work, while considering various settings, operating conditions, and wave scenarios. [ABSTRACT FROM AUTHOR]

In underwater environments, ensuring people's safety is complicated, with potentially life-threatening outcomes, especially when divers have to work in deeper conditions. To improve the available solutions for working with robots in this kind of environment, we propose the validation of a control strategy for robots when taking objects from the seabed. The control strategy proposed is based on acceleration feedback in the model of the system. Using this model, the reference values for position, velocity and acceleration are estimated, and then the position error signal can be computed. When the desired position is obtained, it is possible to then obtain the position error. The validation was carried out using three different objects: a ball, a bottle, and a plant. The experiment consisted of using this control strategy to take those objects, which the robot carried for a moment to validate the stabilisation control and reference following the control in terms of angle and depth. The robot was operated by a pilot from outside of the pool and was guided using a camera and sonar in a teleoperated way. As an advantage of this control strategy, the model upon which the robot is based is decoupled, allowing control of the robot for each uncoupled plane, this being the main finding of these tests. This demonstrates that the robot can be controlled by a control strategy based on a decoupled model, taking into account the hydrodynamic parameters of the robot. [ABSTRACT FROM AUTHOR]

In recent years, more attention has been paid to water surface environment protection. Current water surface waste cleaning mainly relies on manual operations, which are low-efficiency and dangerous. Therefore, in this paper, we design a fully autonomous water surface cleaning robot, SMURF, which achieves high-efficiency water surface cleaning without human operation and adapts to be used in various types of real-world water bodies. In addition, we propose a novel coverage path planning method on water surfaces and an improved nonlinear model predictive controller. The real-world experiment shows that SMURF works well in different kinds of water bodies and achieves much higher efficiency than traditional water surface cleaning methods. [ABSTRACT FROM AUTHOR]

This paper presents the design and control method of the hybrid underwater glider (HUG) for hands-on investigative engineering. The HUG is a lightweight, small, modular, and low-cost underwater vehicle. The HUG uses a piston buoyancy system to control buoyancy and pitch angle and two thrusters to control yaw angle. A hybrid control method utilizing both a piston buoyancy system and a pair of horizontal thrusters is proposed. The novelty of this study is: 1) a two-hull design to facilitate modularity and ease of assembly, 2) an effective piston buoyancy and thrusters’ mechanism to reduce the weight and size of the vehicle, and 3) a pitch control method based on equilibrium identification to enables a fixable and convenient parameters selection and reduces model dependence. Experimental results show that the HUG design reduces the thrusters’ disturbance to pitch angle control, and pitch angle control with the hybrid control method has less overshoot compared to the PID controller. [ABSTRACT FROM AUTHOR]

Abstract Coral reefs are under increasing threat, and the loss of reef‐associated fishes providing valuable ecosystem services is accelerating. The monitoring of such rapid changes has become a challenge for ecologists and ecosystems managers using traditional approaches like scuba divers performing underwater visual censuses (UVC) or diver operated video recording (DOV). However, the use of small, low‐cost robots could help tackle the challenge of such monitoring, provided that they perform at least as well as diver‐based methods. To address this question, tropical fish assemblages from 13 fringing reefs around Mayotte Island (Indian Ocean) were monitored along 50 m‐long transects using stereo videos recorded by a semi‐autonomous underwater vehicle (SAUV) and by a scuba diver (Diver Operated stereo Video system, DOV). Differences between the methods were tested for complementary fish assemblage metrics (species richness, total biomass, total density, Shannon diversity and Pielou evenness) and for the number and size of nine targeted species. SAUV recorded on average 35% higher biomass than DOV which in turn recorded on average 12% higher species richness. Biomass differences were found to be due to SAUV monitoring larger fishes than DOV, a potential marker of human‐related fish avoidance behaviour. This study demonstrates that SAUV provides accurate metrics of coral reef fish biodiversity compared to diver‐based procedures. Given their ability to conduct video transects at high frequency, 100 m depth range and at a moderate cost, SAUV is a promising tool for monitoring fish assemblages in coral reef ecosystems.

Since 2017, NTNU’s Applied Underwater Robotics Laboratory has been developing an infrastructure for remote marine/subsea operations in Trondheim Fjord. The infrastructure, named the OceanLab subsea node, allows remote experimentation for three groups of assets: seabed infrastructure, surface or subsea vehicles/robots, and assets at remote experimentation sites. To achieve this task, a shoreside control room serves as a hub that enables efficient and diverse communication with assets in the field as well as with remote participants/operators. Remote experimentation has become more popular in recent years due to technological developments and convenience, the COVID-19 pandemic, and travel restrictions that were imposed. This situation has shown us that physical presence at the experimentation site is not necessarily the only option. Sharing of the infrastructure among different experts, which are geographically distributed, but participating in a single, local, real-time experiment, increases the level of expertise available and the efficiency of the operations. This paper also elaborates on the development of a virtual experimentation environment that includes simulators and digital twins of various marine vehicles, infrastructures, and the operational marine environment. By leveraging remote and virtual experimentation technologies, users and experts can achieve relevant results in a shorter time frame and at a reduced cost.

This paper presents SVIn2, a novel tightly-coupled keyframe-based Simultaneous Localization and Mapping (SLAM) system, which fuses Scanning Profiling Sonar, Visual, Inertial, and water-pressure information in a non-linear optimization framework for small and large scale challenging underwater environments. The developed real-time system features robust initialization, loop-closing, and relocalization capabilities, which make the system reliable in the presence of haze, blurriness, low light, and lighting variations, typically observed in underwater scenarios. Over the last decade, Visual-Inertial Odometry and SLAM systems have shown excellent performance for mobile robots in indoor and outdoor environments, but often fail underwater due to the inherent difficulties in such environments. Our approach combats the weaknesses of previous approaches by utilizing additional sensors and exploiting their complementary characteristics. In particular, we use (1) acoustic range information for improved reconstruction and localization, thanks to the reliable distance measurement; (2) depth information from water-pressure sensor for robust initialization, refining the scale, and assisting to limit the drift in the tightly-coupled integration. The developed software—made open source—has been successfully used to test and validate the proposed system in both benchmark datasets and numerous real world underwater scenarios, including datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle Aqua2. SVIn2 demonstrated outstanding performance in terms of accuracy and robustness on those datasets and enabled other robotic tasks, for example, planning for underwater robots in presence of obstacles. [ABSTRACT FROM AUTHOR]

Driven by advances in information and communication technologies, an increasing number of industries embrace unmanned and autonomous vehicles for services, such as public transportation, shipping, mapping, and remote surveillance. Unfortunately, these vehicles are vulnerable to passive and active cyber‐physical attacks that can be used for industrial espionage and hijacking attempts. Since attackers can use hijacked vehicles as weapons in terrorist attacks, ensuring the secure operation of such vehicles is critical to prevent the attacks from causing dire financial consequences, or worse, the loss of human lives. This study is motivated by the observation that most cybersecurity studies provide superficial, high‐level descriptions of vulnerabilities and attacks, and the true impact of the described attacks remains unclear. To address this problem, we demonstrate advanced manipulation attacks against an underactuated Unmanned Surface Vehicle (USV) which results in successful hijackings. Using state‐of‐the‐art cryptography, we also show how the signal transmission can be secured to avoid hijacking attempts actively steering the vehicle off course. Through field experiments, we demonstrate how the attacks affect the closed‐loop guidance, navigation, and control system and how the proposed countermeasures prevent these attacks from being successful. Our study is unique in that we provide a complete description of the attacked USV and give a detailed analysis of how spoofed navigation estimates affect the closed‐loop behavior of the underactuated USV. [ABSTRACT FROM AUTHOR]

Autonomous Underwater Vehicles (AUVs) are used by the scientific community for various applications, from collecting well‐distributed high‐quality data to mapping the seafloor or exploring unknown areas. Nonpredictable environmental conditions and sensor acquisitions make the design of AUV surveys challenging even for expert operators. Multiple attempts are required, and the collected data quality is not guaranteed: The AUV passively stores the sensors' acquisitions that are then analyzed offline after its recovery. In Forward‐Looking SONAR (FLS) seabed inspections, the vehicle follows lawnmower paths designed by an expert operator that considers the sensor characteristics. The performance of FLSs is affected by several environmental conditions and possible protruding objects. This paper presents a probabilistic framework for FLS‐based seabed inspections that endow the AUV with the ability to autonomously conducting the survey and ensure adequate coverage of the target area. A three‐dimensional probabilistic occupancy mapping system for FLS reconstructions to update the covered area map was developed. The map is used by the Coverage Path Planning (CPP) algorithm to evaluate the visibility of the viewpoints that are generated as nodes of a random tree. The Next‐Best Viewpoint (NBV) is selected as the first node in the branch expected to collect more data, and the path to reach the NBV is computed using the rapidly exploring random tree (RRT*) algorithm. The sensor‐driven coverage approach is used in a receding‐horizon manner. The proposed Receding‐Horizon Coverage Approach was validated with simulations and real prerecorded data. Finally, the framework was used online during an experimental campaign where several FLS seabed inspections were performed. [ABSTRACT FROM AUTHOR]

Soft actuators, such as dielectric elastomer (DE) and ionic polymer metal composite (IPMC), can generate large deformation under electric stimuli, which makes them promising in biomimetic robotic jellyfish applications. Although dielectric elastomers and ionic polymer metal composites (IPMCs) have been used in jellyfish-inspired robots, respectively, there is no research that combines both of them to develop a jellyfish robot capable of two-dimensionally (2-D) maneuvering capability. In our previous work, a jellyfish robot fabricated with DE can achieve effective locomotion. However, the robot requires a big bell to provide propulsion and can only move vertically. The jellyfish-inspired robot developed in this article exhibits contracting muscle-like behavior using a DE membrane to generate a periodic contraction on its eight fins to provide a thrust force, which propels the robot to transit through underwater. The robot utilizes an IPMC to generate a bending moment, which directs the heading angle of its swimming. This article presents the design, modeling, and experimental characterization of the 2-D maneuverable jellyfish robot. The preliminary results show that the jellyfish-inspired robot can swim underwater effectively in the vertical direction with different sinusoidal input signals, the direction of the robot can be changed by bending the IPMC. The average speed of the robot is about 4.8 mm/s when a sinusoidal signal with 5 kV amplitude and 1.4 Hz frequency is applied to the DE actuator. The maximum average heading angle change can reach to 3.02 $^{\circ }$ by actuating IPMC without voltage applied to DE. [ABSTRACT FROM AUTHOR]

We present OpenFish: an open source soft robotic fish which is optimized for speed and efficiency. The soft robotic fish uses a combination of an active and passive tail segment to accurately mimic the thunniform swimming mode. Through the implementation of a novel propulsion system that is capable of achieving higher oscillation frequencies with a more sinusoidal waveform, the open source soft robotic fish achieves a top speed of 0.85m/s. Hereby, it outperforms the previously reported fastest soft robotic fish by 27%. Besides the propulsion system, the optimization of the fish morphology played a crucial role in achieving this speed. In this work, a detailed description of the design, construction and customization of the soft robotic fish is presented. Hereby, we hope this open source design will accelerate future research and developments in soft robotic fish.

Cabled ocean observatory networks (COON) are used for long-term all-weather observation of submarine scientific data, which contribute to low-carbon ocean energy research. Autonomous underwater vehicles (AUV) with clean energy can provide active search capabilities by connecting with the docking station (DS) on the COON to complete energy and data transmission in long-term detection tasks. The AUV is guided by optical active landmarks and a vision system for short-range docking. In this study, we propose an active landmarks tracking framework to solve the problem of detecting failure caused by incomplete observation of landmarks. First, a two-stage docking algorithm based on CNN is used to estimate the 3D relative position and orientation between DS and AUV during docking, including detect phase and PnP pose estimator. Then extended Kalman filter and Hungarian matching algorithm are introduced to improve the robustness of the algorithm. The reliability of the vision-based short-range docking algorithm is verified in the pool, and the robustness of the algorithm to the field environment is shown in the lake field experiment combined with long-range guidance. The experimental results indicate that the algorithm framework can effectively leverage the landmarks information and enhance the scope of the visual guidance algorithm.

This paper proposes an algorithm that provides operational strategies for multiple tethered autonomous underwater vehicle (T-AUV) systems for entanglement-free navigation. T-AUVs can perform underwater tasks under reliable communication and power supply, which is the most substantial benefit of their operation. Thus, if one can overcome the entanglement issues while utilizing multiple tethered vehicles, the potential applications of the system increase including ecosystem exploration, infrastructure inspection, maintenance, search and rescue, underwater construction, and surveillance. In this study, we focus on developing strategies for task allocation, path planning, and scheduling that ensure entanglement-free operations while considering workload balancing among the vehicles. We do not impose restrictions on the size or shape of the vehicles at this stage; our primary focus is on efficient tether management as an initial work on the topic. To achieve entanglement-free navigation, we propose a heuristic based on the primal-dual technique, which enables initial task allocation and path planning while minimizing the maximum travel cost of the vehicles. Although this heuristic often generates sectioned paths due to its workload-balancing nature, we also propose a mixed approach to provide feasible solutions for non-sectioned initial paths. This approach combines entanglement avoidance techniques with time scheduling and sectionalization methods. To evaluate the effectiveness of our algorithm, extensive simulations were conducted with varying problem sizes. The computational results demonstrate the potential of our algorithm to be applied in real-time operations, as it consistently generates reliable solutions within a reasonable time frame.