Search Results (21)

Underwater sensor networks (UWSNs) play a crucial role in subsea operations like marine exploration and environmental monitoring. A major challenge for UWSNs is achieving effective and energy-efficient data collection, particularly in deep-sea mining, where energy limitations and long-term deployment are key concerns. This study introduces a Channel-Aware AUV-Aided Data Collection Scheme (CADC) that utilizes deep reinforcement learning (DRL) to improve data collection efficiency. It features an innovative underwater node traversal algorithm that accounts for unique underwater signal propagation characteristics, along with a DRL-based path planning approach to mitigate propagation losses and enhance data energy efficiency. CADC achieves a 71.2% increase in energy efficiency compared to existing clustering methods and shows a 0.08% improvement over the Deep Deterministic Policy Gradient (DDPG), with a 2.3% faster convergence than the Twin Delayed DDPG (TD3), and reduces energy cost to only 22.2% of that required by the TSP-based baseline. By combining a channel-aware traversal with adaptive DRL navigation, CADC effectively optimizes data collection and energy consumption in underwater environments. Full article

Cooperative operations of Unmanned Underwater Vehicles (UUVs) have extensive applications in fields such as marine exploration, ecological observation, and subsea security. Path planning, as a key technology for UUV autonomous navigation, is crucial for enhancing the adaptability and mission execution efficiency of UUVs in complicated marine environments. However, existing methods still have significant room for improvement in handling obstacles, multi-task coordination, and other complex problems. In order to overcome these issues, we put forward a task allocation and path planning method for UUVs. First, we introduce a task allocation mechanism based on an Improved Grey Wolf Algorithm (IGWA). This mechanism comprehensively considers factors such as target value, distance, and UUV capability constraints to achieve efficient and reasonable task allocation among UUVs. To enhance the search efficiency and accuracy of task allocation, a Circle chaotic mapping strategy is incorporated into the traditional GWA to improve population diversity. Additionally, a differential evolution mechanism is integrated to enhance local search capabilities, effectively mitigating premature convergence issues. Second, an improved RRT* algorithm termed GR-RRT* is employed for UUV path planning. By designing a guidance strategy, the sampling probability near target points follows a two-dimensional Gaussian distribution, ensuring obstacle avoidance safety while reducing redundant sampling and improving planning efficiency. Experimental results demonstrate that the proposed task allocation mechanism and improved path planning algorithm exhibit significant advantages in task completion rate and path optimization efficiency. Full article

The remotely operated vehicle (ROV), a vital deep-sea platform, offers key advantages, including operational duration via continuous umbilical power, high task adaptability, and zero human risk. It has become indispensable for deep-sea scientific research and marine engineering. To enhance surveys of cobalt-rich crusts (CRCs) on complex seamount terrains, the 4500-m-class Haima ROV integrates advanced payloads, such as underwater positioning systems, multi-angle cameras, multi-functional manipulators, subsea shallow drilling systems, sediment samplers, and acoustic crust thickness gauges. Coordinated control between deck monitoring and subsea units enables stable multi-task execution within single dives, significantly improving operational efficiency. Survey results from Caiwei Guyot reveal the following: (1) ROV-collected data were highly reliable, with high-definition video mapping CRCs distribution across varied terrains. Captured crust-bearing rocks weighed up to 78 kg, drilled cores reached 110 cm, and acoustic thickness measurements had a 1–2 cm margin of error compared to in situ cores; (2) Video and cores analysis showed summit platforms (3–5° slopes) dominated by tabular crusts with gravel-type counterparts, summit margins (5–10° slopes) hosting gravel crusts partially covered by sediment, and steep slopes (12–15° slopes) exhibiting mixed crust types under sediment coverage. Thicker crusts clustered at summit margins (14 and 15 cm, respectively) compared to thinner crusts on platforms and slopes (10 and 7 cm, respectively). The Haima ROV successfully investigated CRC resources in complex terrains, laying the groundwork for seamount crust resource evaluations. Future advancements will focus on high-precision navigation and control, high-resolution crust thickness measurement, optical imaging optimization, and AI-enhanced image recognition. Full article

To date, the development of unmanned technologies using autonomous underwater vehicles (AUVs) has become an urgent demand for solving the problem of inspecting industrial subsea structures. A key issue here is the precise localization of AUVs relative to underwater objects. However, the impossibility of using GPS and the presence of various interferences associated with the dynamics of the underwater environment do not allow high-precision navigation based solely on a standard suite of AUV navigation tools (sonars, etc.). An alternative technology involves the processing of optical images that, at short distances, can provide higher accuracy of AUV navigation compared to the technology of acoustic measurement processing. Although there have been results in this direction, further development of methods for extracting spatial information about objects from images recorded by a camera is necessary in the task of calculating the exact mutual position of the AUV and the object. In this study, in the context of the problem of subsea production system inspection, we propose a technology to recognize underwater objects and provide coordinate references to the AUV based on stereo-image processing. Its distinctive features are the use of a non-standard technique to generate a geometric model of an object from its views (foreshortening) taken from positions of a pre-made overview trajectory, the use of various characteristic geometric elements when recognizing objects, and the original algorithms for comparing visual data of the inspection trajectory with an a priori model of the object. The results of experiments on virtual scenes and with real data showed the effectiveness of the proposed technology. Full article

Due to the precarious underwater environment, many tasks are impossible to carry out with manned vehicles. It is due to the development of autonomous underwater vehicles (AUVs) that exploration, surveillance, and research have been carried out like never before. AUVs are now extensively used for lengthy missions but face challenges in maintaining an accurate position over a larger operational area due to water currents and oceanic complexities. Hence, more sophisticated navigation systems are required for AUVs. As global positioning systems (GPS) and radio signals are attenuated under water, it becomes difficult to navigate subsea areas. In this research work, we propose the accurate position estimation of an AUV by a Kalman filter using an inertial navigation system (INS). In the MATLAB environment, a sample of the INS accelerometer’s data is simulated to determine position from acceleration using the Kalman filter, and the double integration method of determining position is contrasted. Our experimental results imply that our suggested method produces better outcomes. Full article

As subsea shield tunnels are becoming increasingly popular, especially in coastal or river cities, the complicated construction environment poses multiple challenges that need to be addressed to ensure their safety and reliable operation. This study presents the results of centrifuge model tests that aimed to examine the impacts of navigable channel excavation and seawall construction on the deformation and forces acting on a subsea shield tunnel. The symmetry of the tunnel structure, as well as the loading and unloading effects from channel excavation and seawall construction in this engineering project, allow for the simplification of the problem. The centrifuge test model included a novel device to simulate the unloading action of channel excavation and the loading impact from seawall construction. The structural response of the tunnel was monitored using an innovative solution, and various parameters such as vertical displacement, opening of the circumferential joint, circumferential bending moment, and longitudinal stress were analyzed. The results reveal that both channel excavation and seawall construction have significant effects on the stress and deformation of the pre-existing tunnel. While the excavation of the navigable channel reduces the load on the tunnel from the overlying strata, resulting in uplifts in the tunnel structure around the excavation area, and the construction of the seawall causes settlement of the tunnel near the loading zone. The unloading effect of channel excavation leads to the opening tendency of the tunnel circumferential joints, while the loading effect of seawall construction has the opposite effect on the tunnel circumferential joints. The excavation of the channel induces tensile stresses on the tunnel crown around the loading zone, while the seawall construction causes significant compressive stresses on the tunnel crown around the loading zone. It is crucial to prioritize safety and ensure the tunnel’s load-bearing capacity through careful design and construction considerations in practical engineering. The study can guide the design and construction of future projects and help minimize the risk of damage to pre-existing structures. Full article

The observation and detection of the subsea environment urgently require large-scale and long-term observation platforms. The design and development of subsea AUVs involve three key points: the subsea-adapted main body structure, agile motion performance that adapts to complex underwater environments, and underwater acoustic communication and positioning technology. This paper discusses the development and evolution of subsea AUVs before proposing solutions to underwater acoustic communication and positioning navigation schemes. It also studies key technologies for the agile motion of subsea AUVs and finally gives an example of a solution for implementing underwater AUVs, i.e., the disk-shaped autonomous underwater helicopter (AUH). This paper will provide guidance for the design of subsea AUVs and the development of corresponding observation and detection technologies. Full article

Industrial subsea infrastructure inspections using autonomous underwater vehicles (AUV) require high accuracy of AUV navigation relative to the objects being examined. In addition to traditional navigation tools with inertial navigation systems and acoustic navigation equipment, technologies with video information processing are also actively developed today. The visual odometry-based techniques can provide higher navigation accuracy for local maneuvering at short distances to objects. However, in the case of long-distance AUV movements, such techniques typically accumulate errors when calculating the AUV movement trajectory. In this regard, the present article considers a navigation technique that allows for increasing the accuracy of AUV movements in the coordinate space of the object inspected by using a virtual coordinate reference network. Another aspect of the method proposed is to minimize computational costs for AUV moving along the inspection trajectory by referencing the AUV coordinates to the object pre-calculated using the object recognition algorithm. Thus, the use of a network of virtual points for referencing the AUV to subsea objects is aimed to maintain the required accuracy of AUV coordination during a long-distance movement along the inspection trajectory, while minimizing computational costs. Full article

Underwater Vehicles have become more sophisticated, driven by the off-shore sector and the scientific community’s rapid advancements in underwater operations. Notably, many underwater tasks, including the assessment of subsea infrastructure, are performed with the assistance of Autonomous Underwater Vehicles (AUVs). There have been recent breakthroughs in Artificial Intelligence (AI) and, notably, Deep Learning (DL) models and applications, which have widespread usage in a variety of fields, including aerial unmanned vehicles, autonomous car navigation, and other applications. However, they are not as prevalent in underwater applications due to the difficulty of obtaining underwater datasets for a specific application. In this sense, the current study utilises recent advancements in the area of DL to construct a bespoke dataset generated from photographs of items captured in a laboratory environment. Generative Adversarial Networks (GANs) were utilised to translate the laboratory object dataset into the underwater domain by combining the collected images with photographs containing the underwater environment. The findings demonstrated the feasibility of creating such a dataset, since the resulting images closely resembled the real underwater environment when compared with real-world underwater ship hull images. Therefore, the artificial datasets of the underwater environment can overcome the difficulties arising from the limited access to real-world underwater images and are used to enhance underwater operations through underwater object image classification and detection. Full article

This paper proposed a compact design of the subsea cobalt-rich crust mining vehicle with a general purpose support vessel for subsea resource exploration, sample collection, and research. The necessary functions were considered in the concept design, including walk, crushing/mining, sample collection, cutter head adaptation, vehicle orientation, crust texture measurement, awareness, positioning, and navigation. The prototype was tested in both tank and subsea environment. The sea trials were carried out with the support of a general purpose support vessel. The track design worked well in both the tank and subsea environment and the mining vehicle walked smoothly in the sea trial. The crust was crushed to the size of 2 mm and 10 mm with different cutting parameters and successfully collected by the jet pump, 6 kg in total. The crust texture was measured by the onboard sonar successfully and can be used for cutting parameter selection. The cameras captured the images of the subsea environment, but the actions of crushing and sample collection produced plumes, which blocked the camera vision. In the situation, the front image sonar can be used to keep the vehicle away from big rocks. The mining vehicle is not limited to the mining and sampling of subsea cobalt-rich crust. Most of the subsea solid resources on the seabed can be considered to use the compact mining vehicle for sampling and related research. The only issues to be considered are the crushing ability and sample size required. Full article

This paper presents the technical and economic feasibility analysis of the subsea shuttle tanker (SST). The SST is proposed as an alternative to subsea pipelines and surface tankers with the primary purpose of transporting CO₂ autonomously underwater from onshore facilities to subsea wells for direct injection at marginal subsea fields. In contrast to highly weather-dependent surface tanker operations, the SST can operate in any condition underwater. The technical–economic analysis is performed in two steps. First, the SST’s technical feasibility is evaluated by investigating designs with lower and higher capacities. The purpose is to observe the appearance of technical limits (if present) when the SST is scaled down or up in size. Second, an economic analysis is performed using the well-reviewed cost models from the publicly available Zero Emissions Platform (ZEP) and Maritime Un-manned Navigation through Intelligence in Networks (MUNIN) D9.3 reports. The scenarios considered are CO₂ transport volumes of 1 to 20 million tons per annum (mtpa) with transport distances of 180 km to 1500 km in which the cost per ton of CO₂ is compared between offshore pipelines, crewed/autonomous tanker ships, and SST. The results show that SSTs with cargo capacities 10,569 m³, 23,239 m³, and 40,730 m³ are technically feasible. Furthermore, the SSTs are competitive for short and intermediate distances of 180–750 km and smaller CO₂ volumes of 1–2.5 mtpa. Lastly, it is mentioned that the SST design used the DNVGL Rules for Classification for Naval Vessels, Part 4 Sub-surface ships, Chapter 1 Submarine, DNVGL-RU-NAVAL-Pt4Ch1, which is primarily catered towards military submarine design. It is expected that a dedicated structural design code that is optimized for the SST would reduce the structural weight and corresponding capital expenditure (CAPEX). Full article

The use of an autonomous underwater vehicle (AUV) to inspect underwater industrial infrastructure requires the precise, coordinated movement of the AUV relative to subsea objects. One significant underwater infrastructure system is the subsea production system (SPS), which includes wells for oil and gas production, located on the seabed. The present paper suggests a method for the accurate navigation of AUVs in a distributed SPS to coordinate space using video information. This method is based on the object recognition and computation of the AUV coordinate references to SPS objects. Stable high accuracy during the continuous movement of the AUV in SPS space is realized through the regular updating of the coordinate references to SPS objects. Stereo images, a predefined geometric SPS model, and measurements of the absolute coordinates of a limited number of feature points of objects are used as initial data. The matrix of AUV coordinate references to the SPS object coordinate system is computed using 3D object points matched with the model. The effectiveness of the proposed method is estimated based on the results of computational experiments with virtual scenes generated in the simulator for AUV, and with real data obtained by the Karmin2 stereo camera (Nerian Vision, Stuttgart, Germany) in laboratory conditions. Full article

To reduce human risk and maintenance costs, Autonomous Underwater Vehicles (AUVs) are involved in subsea inspections and measurements for a wide range of marine industries such as offshore wind farms and other underwater infrastructure. Most of these inspections may require levels of manoeuvrability similar to what can be achieved by tethered vehicles, called Remotely Operated Vehicles (ROVs). To extend AUV intervention time and perform closer inspection in constrained spaces, AUVs need to be more efficient and flexible by being able to undulate around physical constraints. A biomimetic fish-like AUV known as RoboFish has been designed to mimic propulsion techniques observed in nature to provide high thrust efficiency and agility to navigate its way autonomously around complex underwater structures. Building upon advances in acoustic communications, computer vision, electronics and autonomy technologies, RoboFish aims to provide a solution to such critical inspections. This paper introduces the first RoboFish prototype that comprises cost-effective 3D printed modules joined together with innovative magnetic coupling joints and a modular software framework. Initial testing shows that the preliminary working prototype is functional in terms of water-tightness, propulsion, body control and communication using acoustics, with visual localisation and mapping capability. Full article

Since the development of the first autonomous underwater vehicles, the demanded tasks for subsea operations have become more and more challenging as, for instance, intervention, maintenance and repair of seabed installations, in addition to surveys. As a result, the development of autonomous underwater reconfigurable vehicles (AURVs) with the capability of interacting with the surrounding environment and autonomously changing the configuration, according to the task at hand, can represent a real breakthrough in underwater system technologies. Driven by these considerations, an innovative AURV has been designed by the Department of Industrial Engineering of the University of Florence (named as UNIFI DIEF AURV), capable of efficiently reconfiguring its shape according to the task at hand. In particular, the UNIFI DIEF AURV has been provided with two extreme configurations: a slender (“survey”) configuration for long navigation tasks, and a stocky (“hovering”) configuration designed for challenging goals as intervention operations. In order to observe the several dynamic features for the two different configurations, a novel formulation for the dynamic maneuverability analysis (DMA) of an AURV, adapting Yoshikawa’s well-known manipulability theory for robotic arms, is proposed in this work. More specifically, we introduce a novel analysis which relates the vehicle body-fixed accelerations with the rotational speed of each thruster, taking into account also the AURV dynamic model for each configuration and the propulsion system. Full article

This paper proposes a 3D object recognition method for non-coloured point clouds using point features. The method is intended for application scenarios such as Inspection, Maintenance and Repair (IMR) of industrial sub-sea structures composed of pipes and connecting objects (such as valves, elbows and R-Tee connectors). The recognition algorithm uses a database of partial views of the objects, stored as point clouds, which is available a priori. The recognition pipeline has 5 stages: (1) Plane segmentation, (2) Pipe detection, (3) Semantic Object-segmentation and detection, (4) Feature based Object Recognition and (5) Bayesian estimation. To apply the Bayesian estimation, an object tracking method based on a new Interdistance Joint Compatibility Branch and Bound (IJCBB) algorithm is proposed. The paper studies the recognition performance depending on: (1) the point feature descriptor used, (2) the use (or not) of Bayesian estimation and (3) the inclusion of semantic information about the objects connections. The methods are tested using an experimental dataset containing laser scans and Autonomous Underwater Vehicle (AUV) navigation data. The best results are obtained using the Clustered Viewpoint Feature Histogram (CVFH) descriptor, achieving recognition rates of $51.2\%$, $68.6\%$ and $90\%$, respectively, clearly showing the advantages of using the Bayesian estimation ($18\%$ increase) and the inclusion of semantic information ($21\%$ further increase). Full article