Remotely Operated Underwater Vehicle Research Articles

Structural design evaluation for Underwater Remotely Operated Vehicle (ROV), case study: Madura Straits

Using Remotely Operated Vehicles (ROV) is very important, especially in underwater investigations. The Madura Strait, located between the Java islands and Madura, is used as an industrial area, especially for oil and gas sources. Therefore, the ROV structure must be designed in such a way that it can withstand the hydrostatic loads it receives during operations in the deep sea. The study was conducted to determine the ROV’s ability to withstand hydrostatic loads using materials such as steel, aluminium, and HDPE. The pre-designed ROV is converted and modeled in 3D. The model formed and detailed as the basic form of the ROV is imported to the Finite Element Analysis tools to obtain the value of the stress and deformation of the ROV. Therefore, the load is adjusted to the hydrostatic pressure, with the seawater depth up to 200 m. The study structural design evaluation is able used as a reference for selecting materials used in the ROV.

Mapping of marine litter on the seafloor using WASSP S3 multibeam echo sounder and Chasing M2 ROV

Marine litter is a growing threat to the marine environment. Mapping of marine litter is becoming increasingly important to detect its potential hotspots and prevent their spread. In this paper, the applicability of the multibeam echo sounder (MBES) WASSP S3 and remotely operated underwater vehicle (ROV) Chasing M2 was tested in the detection and mapping of marine litter on the seafloor within the wider area of the St. Ante Channel (Šibenik, Croatia). Also, the precision assessment of WASSP S3 was tested at different cruising speeds. Results have shown that Chasing M2 can be used effectively for the initial detection of marine debris in shallow waters. However, if the underwater navigation and positioning system and auxiliary measurement scales are not used, the ROV has limited capabilities in deriving morphometric parameters of marine litter on the seafloor. This was determined by comparing the 3D model of a tire which was derived using video photogrammetry captured with ROV and the 3D model of a tire which was produced using a hand-held 3D scanner. Furthermore, the results have shown the WASSP S3 is not suitable for identifying marine litter smaller than 1 m at depths up to 10 m. The MBES WASSP S3 can detect marine litter that has a minimum area of 100 * 100 cm and a height of around 40 cm at depths up to 10 m. The results pointed to the need for caution when choosing an adequate sensor to detect and map marine litter on the seafloor. In addition, MBES interval measurements have shown that WASSP S3 precision is in the centimeter range (<10 cm) at different cruising speeds. The obtained results have helped to establish the guidelines for the integrated use of MBES, ROV, and UAV in the detection of marine litter on the seafloor.

An Ultracompact Underwater Pulsed Digital Holographic Camera With Rapid Particle Image Extraction Suite

This article presents the design, development, and testing of an ultracompact underwater pulsed digital holographic camera (named weeHoloCam) that has been successfully deployed in the North Sea. With a footprint of 9 cm diameter × 60 cm long and weighing just 3.5 kg, to our knowledge, this is the lightest and most compact system of its kind in the world. It can be easily adapted for mounting on remotely operated underwater vehicles (ROVs) and autonomous underwater vehicles (AUVs). weeHoloCam can record 12 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> of the water column per hologram at 20 Hz and has a capacity to continuously record up to 200 000 holograms, each 5 MB in size. Along with the holographic camera, a field-programmable gate array (FPGA)-accelerated hologram analysis suite has been developed. The 838 megapixel/s reconstruction speed demonstrated is the highest reported speed for a single FPGA implementation to date. This is an extremely important development as rapid processing of recorded holograms is vital for the viability of subsea holographic cameras. weeHoloCam was successfully trialed in the North Sea, recording close to a hundred thousand holograms and extracting particle images within those holograms at a rate of 190 per min. weeHoloCam has the potential to play an important role in several oceanic studies: spatial and temporal monitoring of plankton species; study of plankton dynamics; study of vertical transport of floc; and monitoring microplastic pollution in the ocean, to name but a few.

Robust automatic net damage detection and tracking on real aquaculture environment using computer vision

Sea cage inspection is a crucial task in marine aquaculture for both controlling the quality in the fish development process and the environmental impact of these facilities. Net damage in sea cages can cause fish to escape from fish farms to the open sea, producing serious economic losses and compromising the surrounding marine ecosystem. Currently, the most sophisticated inspection processes used in the industry rely on human visual inspection of underwater videos captured with tele-operated underwater ROV (Remotely Operated Vehicles). This process is tedious, time consuming, imprecise, highly dependant on the operator level of expertise, and has low verifiability.This article presents a comprehensive algorithm for automatic net damage detection in sea cages for aquaculture oriented to on-ROV processing and real-time processing. The proposed approach takes a video stream from an on-ROV camera, segments the image frames to separate the net of the sea cage from the background, and applies noise reduction tuned for underwater conditions. Then, to perform net damage detection, the mesh net hole areas are analyzed for detecting outliers that represent potential damage. Finally, holes neighboring to the outliers are analyzed to reduce perspective errors, and a spatial-temporal criterion using tracking is applied, to reduce the chance of false positives. The algorithm was first tested using a set of public images for comparison with state-of-the-art approaches and repeatability of the tests, and then using a dataset of real ROV inspection sequences to evaluate its effectiveness in real-world scenarios. Results show that our approach presents high levels of accuracy even for adverse scenarios and is adequate for real-time processing in embedded platforms.

ROUV HEADING BY A FRACTIONAL-ORDER PI CONTROLLER

To control a remotely operated underwater vehicle (ROUV) of the observation ROUVs class, the interactions among mechanical, electronic and information processing elements call for an integrated approach at all design and development stages. Different methodologies must be combined in a multi-formalism modelling approach supported by a suitable simulation and prototyping environment. The proposed approach further involves the development of a digital twin ROUV prototype in the Gazebo-based simulator to assess performance reliably, and the utilization of a ROUV controller board (stm32F407, Quad-core Cortex-A7) with a digital camera and an inertial measurement unit (3D accelerometer, 3D gyroscope, compass) to implement and run the designed control algorithms in real-time. A mathematical model of the system is derived to design a fractional-order PI controller of the ROUV heading in the horizontal plane.

Cooperative path-following control of a remotely operated underwater vehicle for human visual inspection task

In recent years, significant attention has been paid to remotely operated underwater vehicles (ROVs) in the performance of underwater tasks, such as the inspection and maintenance of the underwater infrastructure. The workload of ROV operators tends to be high, even for the skilled operators. Therefore, assistance methods for the operators are desired. This study focuses on a task in which a human operator controls an underwater robot to follow a certain path while visually inspecting objects in the vicinity of the path. In such a task, it is desirable to achieve the speed of trajectory control manually because the visual inspection is performed by a human operator. However, to allocate resources to visual inspection, it is desirable to minimize the workload on the path-following by assisting with the automatic control. Therefore, the objective of this study is to develop a cooperative path-following control method that achieves the above-mentioned task by expanding a robust path-following control law of non-holonomic wheeled vehicles. To simplify this problem, we considered a path-following and visual objects recognition task in a two-dimensional plane. We conducted an experiment with participants (n = 16) who completed the task using the proposed method and manual control. We compared results in terms of object recognition success rate, tracking error, completion time, attention distribution, and workload. The results showed that both the path-following errors and workload of the participants were significantly smaller with the proposed method than with manual control. In addition, subjective responses demonstrated that operator attention tended to be allocated to object recognition rather than robot operation tasks with the proposed method. These results demonstrate the effectiveness of the proposed cooperative path-following control method.

Autonomous Control of the Underwater Remotely Operated Vehicle in Collision Situation with Stationary Obstacle

Abstract The article considers the problem of autonomous control of the underwater remotely operated vehicle mini Remotely Operated Vehicle (ROV) in a collision situation with a stationary obstacle. The control of the collision avoidance process is presented as a synthesis of fuzzy proportional-differential controllers for the control of distance and orientation concerning the detected stationary obstacle. The control of the submergence depth of the underwater vehicle has been adopted as a separate control flow. A method to obtain the main motion parameters of the underwater vehicle relative to the detected stationary obstacle using a Laser-based Vision System (LVS) and a pressure sensor coupled to an Inertial Measurement Unit (IMU) is described and discussed. The result of computer implementation of the designed fuzzy controllers for collision avoidance is demonstrated in simulation tests and experiments carried out with the mini ROV in the test pool.

Position Tracking of an Underwater Robot Based on Floating-Downing PI Control

A remotely operated underwater vehicle (ROV) is crucial in ocean exploration and underwater missions. An ROV is manipulated through a tether cable by an operator on shore or mother boat, and it can be used for underwater observations or as a robotic arm to take samples back. The position control and movement of an ROV are not stable due to buoyancy, ocean current, and surge waves. To overcome the influence of these disturbances on the ROV, we propose a switch proportional-integral (PI) controller combined with a buck-boost converter (BBC) to process the ROV’s position following. In this paper, a six-axis ROV was designed and implemented. The ROV controller was designed by a NI-roboRIO-based embedded system, which includes a pressure sensor, an accelerometer, six thrusters, and two webcams. The LabVIEW human–machine interface was designed to integrate the control system, sensors, and thrusters. The PI controller was employed to perform the station-keeping and trajectory following. Different PI control parameters were used for the ROV floating-up and diving-down in the sine-wave trajectory following. Experimental results showed that the proposed switch PI control scheme is robust for the position tracking of the underwater robot. The contribution of this study is that we proposed a novel switch proportional-integral controller combined with a buck-boost converter and applied the controller to a natural underwater vehicle, not a mathematical model. The experiments showed that the proposed controller can resist the disturbance of the aquatic environment.

Hierarchical trajectory tracking control for ROVs subject to disturbances and parametric uncertainties

Trajectory tracking control is crucial for a remotely operated underwater vehicle (ROV), but the performance can be degraded due to disturbances, process noises, and parametric uncertainties. Therefore, a hierarchical control scheme is proposed to handle this problem. Firstly, a 4DOF kinematic model and dynamic model of the ROV are constructed. Ocean current disturbance is considered in the kinematic model and the dynamic model when tracking the reference trajectory. Parametric uncertainties are also considered on the dynamic model. Secondly, model predictive control using extended state based Kalman filter (ESKF-MPC) is used to design a kinematic controller (velocity layer) based on the kinematic model to generate virtual expected velocities. ESKF-MPC can not only solve constraints on velocities, but also estimate states and ocean current disturbance, which is beneficial for improving the robustness to ocean current disturbance. Thirdly, adaptive sliding mode control (ASMC) is applied to construct a dynamic controller (position layer) to compute optimal forces and moments based on the dynamic model and virtual expected velocities output by the kinematic controller, which is robust to parametric uncertainties and can solve the chattering problem. Finally, simulations are performed to validate the performance of the proposed hierarchical control scheme.

Mesophotic Reefs of the Largest Brazilian Coastal Protected Area: Mapping, Characterization and Biodiversity

Mesophotic reefs are poorly known worldwide despite their great ecological relevance and management importance for coral reef conservation strategies. To aid in filling this gap, we conducted a pioneering, large-scale survey, covering a total of around 315 miles in length, in the largest Brazilian coastal Marine Protected Area (MPA) Costa dos Corais. From the digital bathymetry model (30 to 50 m depth) generated by a multibeam echo sounder, we selected areas of greater geomorphological diversity for a detailed investigative expedition of mesophotic ecosystems. Various sampling techniques were used: single-beam echo sounders for detailing the relief, a remotely operated underwater vehicle (ROV) for habitat type investigation, baited remote underwater video (BRUV) for collecting images of the fish community, and scuba diving to perform transects describing the benthic and fish community. We analyzed reef environments from 20 to 68 m deep. As a result, we present the mapping and geomorphological characterization of two compartments of mesophotic reefs at 21–45 m depth and an image library of mesophotic ecosystems with the species description and indications of whether it is a new record in the region. Biodiversity data were collected covering eight species of sponges, with greater abundance for Ircinia spp., Aplysina spp., and Xestospongia muta; eight from corals, mainly Siderastrea spp. And Montastrea cavernosa; and 68 species of reef fish, with the Labridae family (including Scarinae—11 species) being the richest. Our results demonstrate the importance of mesophotic reefs for MPA Costa dos Corais reef biodiversity and, with that, the need to protect these areas through the application of local conservation strategies, such as the creation of “no-take zones”.

Simultaneous Control and Guidance of an AUV Based on Soft Actor-Critic.

The marine environment is a hostile setting for robotics. It is strongly unstructured, uncertain, and includes many external disturbances that cannot be easily predicted or modeled. In this work, we attempt to control an autonomous underwater vehicle (AUV) to perform a waypoint tracking task, using a machine learning-based controller. There has been great progress in machine learning (in many different domains) in recent years; in the subfield of deep reinforcement learning, several algorithms suitable for the continuous control of dynamical systems have been designed. We implemented the soft actor–critic (SAC) algorithm, an entropy-regularized deep reinforcement learning algorithm that allows fulfilling a learning task and encourages the exploration of the environment simultaneously. We compared a SAC-based controller with a proportional integral derivative (PID) controller on a waypoint tracking task using specific performance metrics. All tests were simulated via the UUV simulator. We applied these two controllers to the RexROV 2, a six degrees of freedom cube-shaped remotely operated underwater Vehicle (ROV) converted in an AUV. We propose several interesting contributions as a result of these tests, such as making the SAC control and guiding the AUV simultaneously, outperforming the PID controller in terms of energy saving, and reducing the amount of information needed by the SAC algorithm inputs. Moreover, our implementation of this controller allows facilitating the transfer towards real-world robots. The code corresponding to this work is available on GitHub.

ULO: An Underwater Light-Weight Object Detector for Edge Computing

Recent studies on underwater object detection have progressed with the development of deep-learning methods. Generally, the model performance increase is accompanied by an increase in computation. However, a significant fraction of remotely operated underwater vehicles (ROVs) and autonomous underwater vehicles (AUVs) operate in environments with limited power and computation resources, making large models inapplicable. In this paper, we propose a fast and compact object detector—namely, the Underwater Light-weight Object detector (ULO)—for several marine products, such as scallops, starfish, echinus, and holothurians. ULO achieves comparable results to YOLO-v3 with less than 7% of its computation. ULO is modified based on the YOLO Nano architecture, and some modern architectures are used to optimize it, such as the Ghost module and decoupled head design in detection. We propose an adaptive pre-processing module for the image degradation problem that is common in underwater images. The module is lightweight and simple to use, and ablation experiments verify its effectiveness. Moreover, ULO Tiny, a lite version of ULO, is proposed to achieve further computation reduction. Furthermore, we optimize the annotations of the URPC2019 dataset, and the modified annotations are more accurate in localization and classification. The refined annotations are available to the public for research use.

Offshore inspection mission modelling for an ASV/ROV system

To reduce the human exposure risk during the offshore structure inspection, the Remotely Operated underwater Vehicle (ROV) is a widely used solution. This paper couples an inspection class ROV with an autonomous surface vehicle (ASV) via a launch and recovery system (LARS) in a nonlinear numerical model. Operational inspection missions with both static and moving targets are modelled for this ASV/ROV system. The paper reports the following distinctive mission profiles: i) pipeline inspection, ii) floating offshore wind turbine (FOWT) mooring line inspection and iii) circumferential weld surface scan at the FOWT spar. The results provide important parameters to design and implement autonomous inspection missions. During the scan/check stage, the distance between the ROV and the target will be varying, due to the relative motion between the ASV and ROV. Important model results are that missions with following ASV will allow to reduce the ROV umbilical tension for a given mission profile. Results also determined that the net buoyancy of the ROV will lead to a position offset. In the surface scan cases, it is found that the drag force caused by the tidal current can contribute to ROV rotation and should be considered in detail ahead of any mission.

Choosing the optimal method of stock assessment for Saccharina japonica in the northwestern Tatar Strait

Experiments on comparing the methods for assessment of Saccharina japonica commercial stock were conducted in 2021. In total, 11 sites in the northwestern Tatar Strait southward from the Sovetskaya Gavan Bay, along the 44 km stretch of coastline between Cape Krasny Partizan (48о58′ N 140о23′ E) and Cape Korovin (48о37′ N 140о11′ E), were surveyed aboard RV Ubezhdenny and small boats. This is a traditional area of Saccharina japonica commercial harvesting. Four methods were compared: 1) visual assessment of projective cover from the sea surface by two observers independently; 2) assessment of projective cover by two observers independently on the images obtained from a remotely operated underwater vehicle (ROV); 3) estimation of projective cover by a diver; and 4) assessment on the data of quantitative diving samples. The former three methods are comparable and differ by 1.2–1.7 times. The estimates made by the latter method of diving sampling exceed them by 2.4–4.1 times because of artifacts specific to this method. To eliminate inaccuracies, diving samples should be collected under control from the sea surface by ROV. Evaluation of the projective cover by diver is inadvisable. Visual assessments of the projective cover from the sea surface are simple and cheap but inconvenient for documentation. The stock assessment with micro-ROV is recognized as the optimal method provided an adequate accounting of the kelp abundance and complete documenting of the survey materials.

Specification Design and Performances Using Computational Fluid Dynamics for Mini-Remotely Operated Underwater Vehicle

Remotely Operated Underwater Vehicle (ROV) is a submersible robotic system, used to examine various underwater characteristics and controlled by operators from shore. The applications of ROV are widely diverse, especially for exploration and marine industry. This research is to investigate pressure and drag on underwater vehicles using the CFD approach. A low-cost mini ROV with a 300 mm length was used for the research and to determine the hardware and material type required for prototype production by observing pressure and drag from the CFD simulation result. Simulation is solved using CFD software with RANSE method and Shear Stress Transport based k- ɷ was used as turbulence setting. The result indicated that to pull the mini ROV from the surface to a depth of 50 m requires a thrust of 3,9 kg and the mini ROV receiving the pressure of 7,7 bar.

Integrating Angular Backscatter Response Analysis Derivatives Into a Hierarchical Classification for Habitat Mapping

Accurate maps of biological communities are essential for monitoring and managing marine protected areas but more information on the most effective methods for developing these maps is needed. In this study, we use Wilsons Promontory Marine National Park in southeast Australia as a case study to determine the best combination of variables and scales for producing accurate habitat maps across the site. Wilsons Promontory has full multibeam echosounder (MBES) coverage coupled with towed video, remotely operated underwater vehicle (ROV) and drop video observations. Our study used an image segmentation approach incorporating MBES backscatter angular response curve and bathymetry derivatives to identify benthic community types using a hierarchical habitat classification scheme. The angular response curve data were extracted from MBES data using two different methods: 1) angular range analysis (ARA) and 2) backscatter angular response (AR). Habitat distributions were predicted using a supervised Random Forest approach combining bathymetry, ARA, and AR derivatives. Variable importance metrics indicated that ARA derivatives, such as grain size, impedance and volume heterogeneity were more important to model performance than AR derivatives mean, skewness, and kurtosis. Additionally, this study investigated the impact of segmentation software settings when creating segmented surfaces and their impact on overall model accuracy. We found using fine scale segmentation resulted in the best model performance. These results indicate the importance of incorporating backscatter derivatives into biological habitat maps and the need to consider scale to increase the accuracy of the outputs to help improve the spatial management of marine environments.

Effects of transition turbulence modeling on the hydrodynamic performance prediction of a rim-driven thruster under different duct designs

The rim-driven thruster (RDT) is an innovative marine propulsion device. During the design process, scale models of RDTs are frequently used and also smaller sized units used in Underwater Remotely Operated Vehicles (ROVs) or Autonomous Underwater Vehicles (AUVs) are often likely to operate under conditions where the boundary layer on the surface of the propeller makes a transition from a laminar to a turbulent regime. Accurately resolving this transitional boundary layer can help improve the performance prediction of RDTs. Studies addressing this are scarce and hence the present work investigates the influence of transition turbulence modeling on the hydrodynamic performance prediction of an RDT with different duct configurations by means of computational fluid dynamics (CFD). The turbulence models employed in this study are the two-equation SST k−ω model and the four-equation γ−Reθ transition model. For this purpose, three duct designs are considered, notably two modified 19A and 37 ducts, and one symmetric duct design often seen in RDT designs. Numerical calculations are performed using ANSYS Fluent 19.1 whilst a verification and validation study is firstly carried out to ensure the accuracy of the numerical model. The results demonstrate that the γ−Reθ transition model can better capture the transitional flow on the blade’s surface, and substantial discrepancies are observed between both turbulence models with regard to the streamline patterns near the blades on the one hand and skin friction profiles on the other. The torque calculated using the transition model better fits the experimental data, suggesting that it is more suitable to estimate the RDT performance. Three RDT duct designs are simulated which enable different ways to guide the incoming flow and it is shown that they have an important influence on the propeller performance. The results of the open water performance of the three different duct profiles are presented and compared, along with detailed analyses of the flow fields.

Underwater Surveillance Device

Abstract: Remotely operated underwater vehicles (ROVs) are underwater robots driven by a remote control where an individual operates it from the surface. The series of tethered wires send signals between the operator and the ROV. Mostly the ROVs are equipped with a propulsion system, a camera to show live pictures of the underwater activities, and lights. Other equipment is added depending on the specifications required. Our underwater surveillance device includes sensors that measure parameters of water such as temperature and turbidity, an Esp-32 camera. Our team was determined to recreate such an ROV to fulfil any surveillance operation up to 30 meters’ depth which is a partially wireless device controlled by a joystick remote control. Keywords: ROVs, Portable, Cost efficient, Sensors, Underwater Surveillance Device.

A Modular Open-Source Continuum Manipulator for Underwater Remotely Operated Vehicles

Abstract Continuum manipulators are a class of robots with many degrees-of-freedom, leading to highly flexible motion with inherent compliance. These attributes make them well suited for manipulation tasks and physical interaction with the environment. A high impact yet challenging field for exploring continuum robot designs is free-floating underwater manipulation with a remotely operated vehicle (ROV). In this article, we propose a modular, reconfigurable, cable-driven continuum arm for free-floating underwater manipulation and present a corresponding kinematics, control, and computation framework. The mechanical design consists of a continuum arm, an actuation unit, and a waterproof enclosure. The kinematics model is introduced as two mappings between three spaces: the joint space, the configuration space, and the task space. The differential kinematics for each mapping is also derived. An electronics system design is proposed for underwater applications, including the communication framework between the topside computer (above surface), on-board computer, and manipulator mechatronics. Experimental validation is presented to demonstrate the robot’s underwater functionality, test the limits of its articulation, and evaluate the arm’s stiffness. Future work includes field testing with an ROV platform and development of advanced controls and planning for manipulation tasks.

Heterogeneous Autonomous Robotic System in Viticulture and Mariculture: Vehicles Development and Systems Integration.

There are activities in viticulture and mariculture that require extreme physical endurance from human workers, making them prime candidates for automation and robotization. This paper presents a novel, practical, heterogeneous, autonomous robotic system divided into two main parts, each dealing with respective scenarios in viticulture and mariculture. The robotic components and the subsystems that enable collaboration were developed as part of the ongoing HEKTOR project, and each specific scenario is presented. In viticulture, this includes vineyard surveillance, spraying and suckering with an all-terrain mobile manipulator (ATMM) and a lightweight autonomous aerial robot (LAAR) that can be used in very steep vineyards where other mechanization fails. In mariculture, scenarios include coordinated aerial and subsurface monitoring of fish net pens using the LAAR, an autonomous surface vehicle (ASV), and a remotely operated underwater vehicle (ROV). All robotic components communicate and coordinate their actions through the Robot Operating System (ROS). Field tests demonstrate the great capabilities of the HEKTOR system for the fully autonomous execution of very strenuous and hazardous work in viticulture and mariculture, while meeting the necessary conditions for the required quality and quantity of the work performed.