Search Results (41)

The ocean encompasses the majority of the Earth’s surface and harbors substantial energy resources. Nevertheless, the intricate and asymmetrically distributed underwater environment renders existing target detection performance inadequate. This paper presents an enhanced YOLOv8s approach for underwater robot object detection to address issues of subpar image quality and low recognition accuracy. The precise measures are enumerated as follows: initially, to address the issue of model parameters, we optimized the ninth convolutional layer by substituting certain conventional convolutions with adaptive deformable convolution DCN v4. This modification aims to more effectively capture the deformation and intricate features of underwater targets, while simultaneously decreasing the parameter count and enhancing the model’s ability to manage the deformation challenges presented by underwater images. Furthermore, the Triplet Attention module is implemented to augment the model’s capacity for detecting multi-scale targets. The integration of low-level superficial features with high-level semantic features enhances the feature expression capability. The original CIoU loss function was ultimately substituted with Shape IoU, enhancing the model’s performance. In the underwater robot grasping experiment, the system shows particular robustness in handling radial symmetry in marine organisms and reflection symmetry in artificial structures. The enhanced algorithm attained a mean Average Precision (mAP) of 87.6%, surpassing the original YOLOv8s model by 3.4%, resulting in a marked enhancement of the object detection model’s performance and fulfilling the real-time detection criteria for underwater robots. Full article

This study focuses on the remotely operated underwater vehicle (ROV) and addresses key issues in existing simulation systems, such as neglecting the influence of ocean currents on the ROV’s trajectory or only simulating the impact of ocean currents instead of combining wake flow and ocean currents. Additionally, the visualization capabilities of current simulation systems still have room for improvement. This paper develops a three-dimensional path simulation system for ocean inspection robots to tackle these challenges based on MATLAB and Simulink. The system optimizes the drag matrix of the original simulation model by decomposing the sea current into three directional components in three-dimensional space and simulating the relative velocity in each direction separately; it introduces the influence of the current wake, thus more accurately realizing the trajectory simulation of the ROV under the current perturbation. Experimental results demonstrate high consistency between the optimized model’s simulation outcomes and theoretical expectations. The proposed system significantly improves trajectory evolution stability and consistency, compared to traditional models. The findings of this study indicate that the proposed optimized simulation system not only effectively verifies the applicability of control algorithms but also provides reliable data support for ROV design and optimization. Additionally, it lays a solid foundation for further developing intelligent underwater robots based on Internet of Things (IoT) technology. Full article

Remotely operated vehicles (ROVs) face challenges in achieving optimal trajectory tracking performance during underwater movement due to external disturbances and parameter uncertainties. To address this issue, this paper proposes a position and attitude control strategy for underwater robots based on a reinforcement learning active disturbance rejection controller. The linear active disturbance rejection controller has achieved satisfactory results in the field of underwater robot control. However, fixed-parameter controllers cannot achieve optimal control performance for the controlled object. Therefore, further exploration of the adaptive capability of control parameters based on the linear active disturbance rejection controller was conducted. The deep deterministic policy gradient (DDPG) algorithm was used to optimize the linear extended state observer (LESO). This strategy employs deep neural networks to adjust the LESO parameters online based on measured states, allowing for more accurate estimation of model uncertainties and environmental disturbances, and compensating the total disturbance into the control input online, resulting in better disturbance estimation and control performance. Simulation results show that the proposed control scheme, compared to PID and fixed parameter LADRC, as well as the double closed-loop sliding mode control method based on nonlinear observers (NESO-DSMC), significantly improves the disturbance estimation accuracy of the linear active disturbance rejection controller, leading to higher control precision and stronger robustness, thus demonstrating the effectiveness of the proposed control strategy. Full article

Autonomous underwater vehicles (AUVs) tasked with seafloor coverage require a robust integration of path planning and control strategies to operate in adverse real-world environments including obstacles, disturbances, and physical constraints. In this work, we present a fully integrated framework that combines an optimal coverage path planning approach with a robust constrained control algorithm. The path planner leverages a priori information of the target area to achieve maximal coverage, minimize path turns, and ensure obstacle avoidance. On the control side, we employ a reference modification technique that guarantees prescribed waypoint tracking performance under input constraints. The resulting integrated solution is validated in a high-fidelity simulation environment employing ROS, Gazebo, and ArduSub Software-in-the-Loop (SITL) on a BlueROV2 platform. The simulation results demonstrate the synergy between path planning and control, illustrating the framework’s effectiveness and readiness for practical seafloor operations such as underwater debris detection. Full article

Improving the autonomy of underwater interventions by remotely operated vehicles (ROVs) can help mitigate the impact of communication delays on operational efficiency. Currently, underwater interventions for ROVs usually rely on real-time teleoperation or preprogramming by operators, which is not only time-consuming and increases the cognitive burden on operators but also requires extensive specialized programming. Instead, this paper uses the intuitive learning from demonstrations (LfD) approach that uses operator demonstrations as inputs and models the trajectory characteristics of the task through the dynamic movement primitive (DMP) approach for task reproduction as well as the generalization of knowledge to new environments. Unlike existing applications of DMP-based robot trajectory learning methods, we propose the underwater DMP (UDMP) method to address the problem that the complexity and stochasticity of underwater operational environments (e.g., current perturbations and floating operations) diminish the representativeness of the demonstrated trajectories. First, the Gaussian mixture model (GMM) and Gaussian mixture regression (GMR) are used for feature extraction of multiple demonstration trajectories to obtain typical trajectories as inputs to the DMP method. The UDMP method is more suitable for the LfD of underwater interventions than the method that directly learns the nonlinear terms of the DMP. In addition, we improve the commonly used homomorphic-based teleoperation mode to heteromorphic mode, which allows the operator to focus more on the end-operation task. Finally, the effectiveness of the developed method is verified by simulation experiments. Full article

This paper presents a novel integration of Systems Engineering (SE) methodologies and Industry 4.0 (I4.0) technologies in the design of robotic systems, focusing on enhancing underwater robotic missions. Using the conceptual design of an underwater exploration vehicle as a case study, we demonstrate how SE can systematically incorporate I4.0 tools to improve mission performance and meet stakeholder expectations. The study begins with an overview of the SE approach, emphasizing the conceptual design stage and aligning it with the application and case study of design theories. We then explore various I4.0 technologies, highlighting their functional benefits rather than technical specifics and addressing design methods for I4.0. Remotely Operated Vehicles (ROVs) are examined in terms of classification, components, and tasks, showcasing their evolution driven by technological advancements, thus tackling the complexity and design of complex systems. The core of our study involves defining stakeholder expectations, using quality function deployment for requirements definition, and performing a functional and logical decomposition of the ROV system. To deal with design fixation within the design team, we developed a tool to help integrate new technologies by also empathizing with their functional capabilities rather than the technology itself. Our approach underscores the importance of understanding and incorporating new technologies functionally, aligning with the transition towards Industry/Society 5.0. This work not only illustrates the synergy between SE and I4.0, but also offers a structured methodology for advancing the design and functionality of complex systems, setting a blueprint for future developments in this field. Full article

Although the ocean is rich in energy and covers a vast portion of the planet, the present results of underwater target identification are not sufficient because of the complexity of the underwater environment. An enhanced technique based on YOLOv8 is proposed to solve the problems of low identification accuracy and low picture quality in the target detection of current underwater robots. Firstly, considering the issue of model parameters, only the convolution of the ninth layer is modified, and the deformable convolution is designed to be adaptive. Certain parts of the original convolution are replaced with DCN v3, in order to address the issue of the deformation of underwater photos with fewer parameters and more effectively capture the deformation and fine details of underwater objects. Second, the ability to recognize multi-scale targets is improved by employing SPPFCSPC, and the ability to express features is improved by combining high-level semantic features with low-level shallow features. Lastly, using WIoU loss v3 instead of the CIoU loss function improves the overall performance of the model. The enhanced algorithm mAP achieves 86.5%, an increase of 2.1% over the YOLOv8s model, according to the results of the testing of the underwater robot grasping. This meets the real-time detection needs of underwater robots and significantly enhances the performance of the object detection model. Full article

ROV operators often encounter challenges with orientation awareness while operating underwater, primarily due to relying solely on 2D camera feeds to manually control the ROV robot arm. This limitation in underwater visibility and orientation awareness, as observed among Malaysian ROV operators, can compromise the accuracy of arm placement, and pose a risk of tool damage if not handle with care. To address this, a 3D orientation monitoring system for ROVs has been developed, leveraging measurement sensors with nine degrees of freedom (DOF). These sensors capture crucial parameters such as roll, pitch, yaw, and heading, providing real-time data on the ROV’s position along the X, Y, and Z axes to ensure precise orientation. These data are then utilized to generate and process 3D imaging and develop a corresponding 3D model of the operational ROV underwater, accurately reflecting its orientation in a visual representation by using an open-source platform. Due to constraints set by an agreement with the working class ROV operators, only short-term tests (up to 1 min) could be performed at the dockyard. A video demonstration of a working class ROV replica moving and reflecting in a 3D simulation in real-time was also presented. Despite these limitations, our findings demonstrate the feasibility and potential of a cost-effective 3D orientation visualization system for working class ROVs. With mean absolute error (MAE) error less than 2%, the results align with the performance expectations of the actual working ROV. Full article

Net inspection in fish-farm cages is a daily task for divers. This task represents a high cost for fish farms and is a high-risk activity for human operators. The total inspection surface can be more than 1500 m${}^2$, which means that this activity is time-consuming. Taking into account the severe restrictions for human operators in such hostile underwater conditions, this activity represents a significant area for improvement. A platform for net inspection is proposed in this work. This platform includes a surface vehicle, a ground control station, and an underwater vehicle (BlueROV2 heavy) which incorporates artificial intelligence, trajectory control procedures, and the necessary communications. In this platform, computer vision was integrated, involving a convolutional neural network trained to predict the distance between the net and the robot. Additionally, an object detection algorithm was developed to recognize holes in the net. Furthermore, a simulation environment was established to evaluate the inspection trajectory algorithms. Tests were also conducted to evaluate how underwater wireless communications perform in this underwater scenario. Experimental results about the hole detection, net distance estimation, and the inspection trajectories demonstrated robustness, usability, and viability of the proposed methodology. The experimental validation took place in the CIRTESU tank, which has dimensions of 12 × 8 × 5 m, at Universitat Jaume I. Full article

Underwater robots have been widely used in ocean exploration, deep-sea observation, seabed operations, marine scientific research, and other fields. Underwater low-latency, efficient, and safe communication modes are key to realizing the application of an underwater robot data transmission system. This paper mainly studies the optical communication between underwater mobile robots, including the large-dispersion-angle light-emitting diode (LED) design, large field of view receiving technology, weak light detector technology, etc. By designing a 120° large divergence angle underwater optical communication system in this study, the receiving field-of-view angle of the receiving end can reach 60°, which is suitable for the optical communication system of an underwater mobile platform. The high-power LED driver circuit is designed to drive the high-power LED and adopt weak light detection technology to ensure its stability and reliability. The experimental results show that, in the case of incomplete alignment between the transmitter and receiver, stable communication of underwater robots in motion is achieved through the design of a large divergence angle and a receiving field-of-view angle and the use of an underwater weak light detection technology. The communication distance is 30 m, and the communication rate remains above 10 Mbps. The information transmission content can include network data transmission, real-time video, high-definition video, high-definition images, and other data types. This equipment provides a solution for cableless data transmission of remotely operated vehicles (ROVs) and substantially enhances the application field of ROVs. Full article

Underwater robots play a vital role in the exploration and development of marine resources and the inspection and maintenance of offshore platforms. In this paper, the motion control technology of ROV is studied, the kinematics and dynamics of ROV are analyzed, the kinematics and dynamics models of ROV are established, and the degrees of freedom of the models are decouple according to the control requirements. The fluid damping coefficient of ROV was obtained using Fluent software, and an ROV control system based on sliding mode variable structure was designed. The saturation function was introduced into the sliding mode controller to reduce the adverse effects of buffeting. The classical PID controller, fuzzy PID controller, and sliding mode controller designed in this paper were simulated and analyzed by Simulink. A semi-physical simulation platform based on Unity3D was established. It can be seen from the simulation results and the pool experiment results that the performance of the sliding mode controller designed in this paper is better than the classical PID controller and the fuzzy PID controller. The sliding mode control method is used to control the ROV motion, which has better control effect and precision. Full article

In recent decades, there has been considerable interest in developing underwater remotely operated vehicles (ROVs) due to their vital role in exploring ocean depths to perform missions in various applications, including offshore oil and gas, military and defense, scientific research, and aquaculture. To this end, researchers must consider multiple aspects to develop ROVs, such as general design, power and thrust system, navigation and control, and obstacle avoidance. Accordingly, this paper proposes an integrated framework for designing and implementing an ROV prototype, considering the mechanical, electrical, and software systems. Eventually, image processing was implemented using Python to examine the ROV’s capabilities in performing underwater missions. The proposed design employs six thrusters to provide controllability of the ROV in six-degrees-of-freedom (DOF). We coated the track width of the printed circuit board (PCB) with a composite mixture of tin, silver, and gold to resist corrosion and harsh environments, enhance the circuit performance and solderability, and increase its life span. The PCB was designed to sustain 30 A with 10 cm × 10 cm dimensions. The image processing results revealed that the proposed ROV could successfully identify the benthic species, follow the desired routes, detect cracks, and analyze obstacles. Full article

This study presents a relative localization estimation method for a group of low-cost underwater drones (l-UD), which only uses visual feedback provided by an on-board camera and IMU data. It aims to design a distributed controller for a group of robots to reach a specific shape. This controller is based on a leader–follower architecture. The main contribution is to determine the relative position between the l-UD without using digital communication and sonar positioning methods. In addition, the proposed implementation of the EKF to fuse the vision data and the IMU data improves the prediction capability in cases where the robot is out of view of the camera. This approach allows the study and testing of distributed control algorithms for low-cost underwater drones. Finally, three robot operating system (ROS) platform-based BlueROVs are used in an experiment in a near-realistic environment. The experimental validation of the approach has been obtained by investigating different scenarios. Full article

Underwater robotics is rapidly evolving due to the increasing demand for marine resource exploitation. Compared with rigid robots propelled by propellers, bionic robots are stealthier and more maneuverable, such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs), making them widely used underwater. In order to study the motion state of the umbrella jellyfish bionic robot, the displacement of the jellyfish robot along the same direction and the surrounding fluid pressure distribution caused by the jellyfish motion under different experimental conditions are discussed in this paper. The effect of different environmental factors on driving the jellyfish robot is determined by comparing the displacements at different observation points. The results of the study show that the lower the frequency and the longer the motion period, the greater the displacement produced by the robot within the same motion period. Frequency has a significant effect on the motion state of the jellyfish robot. While the change of amplitude also affects the motion state of the jellyfish robot, the displacement of the relaxation phase of the jellyfish robot is much smaller than that of the contraction phase with a small amplitude. It can be concluded that the effect of frequency on robot displacement is greater than the effect of amplitude on robot displacement. This study qualitatively discusses the changes of the motion state of the bionic jellyfish robot in still water under the excitation of different frequencies and amplitudes, and the results can provide corresponding reference for the future application of the bionic jellyfish robot, such as resource exploration, underwater exploration, and complex environment exploration. Full article

This paper presents a simulation model environment for the popular and low-cost remotely operated vehicle (ROV) BlueROV2 implemented in Simulink™ which has been designed and experimentally validated for benchmark control algorithms for underwater vehicles. The BlueROV2 model is based on Fossen’s equations and includes a kinematic model of the vehicle, the hydrodynamics of vehicle and water interaction, a dynamic model of the thrusters, and, lastly, the gravitational/buoyant forces. The hydrodynamic parameters and thruster model have been validated in a test facility. The benchmark model also includes the ocean current, modeled as constant velocity. The tether connecting the ROV to the top-site facility has been modeled using the lumped mass method and is implemented as a force input to the ROV model. At last, to show the usefulness of the benchmark model, a case study is presented where a BlueROV2 is deployed to inspect an offshore monopile structure. The case study uses a sliding mode controller designed for the BlueROV2. The controller fulfills the design criteria defined for the case study by following the provided trajectory with a low error. It is concluded that the simulator establishes a benchmark for future control schemes for position control and trajectory tracking under the influence of environmental disturbances. Full article