TOUCH: a robotic vision system for underwater object tracking

In this chapter, the receding horizon Kalman filter is applied to underwater navigation systems. The ocean covers about two-thirds of the earth and has a great effect on human beings. However, the ocean is overlooked while we focus our attention on land and atmospheric issues; we have not been able to explore the full depths of the ocean, its abundant livings and non-living resources. For example, only recently we have discovered, by using manned submersibles, that a large amount of methane and carbon dioxide comes from the seafloor and extraordinary groups of organisms live in hydrothermal vent areas. However, a number of complex issues due to the unstructured and hazardous undersea environment make it difficult to survey in the ocean even though today’s technologies have allowed humans to land on the moon and robots to travel to Mars. Unmanned underwater vehicles (UUVs) can help us better understand marine and other environmental issues, protect the ocean resources of the earth from pollution, and efficiently utilize them for human welfare. The UUV is a platform for a variety of sensors: acoustic, magnetic, gravimetric and chemical ones. Most commercial UUVs are tethered and remotely operated, referred to as remotely operated vehicles (ROVs). Extensive use of manned submersibles and ROVs are currently limited to a few applications because of very high operational costs, operator fatigue and safety issues. The demand for advanced underwater vehicle technologies is growing and will eventually lead to fully autonomous and reliable underwater vehicles. Autonomous underwater vehicles (AUVs) were initially developed to perform missions that were not easy for ROVs and manned underwater vehicles. Since the autonomy allows AUVs to be used for risky missions such as a mine countermeasure (MCM) or under-ice operations, AUVs are replacing ROVs towed vehicles as well as manned underwater vehicles (Whitcomb, 2000). For detailed ocean surveys, an AUV acts as a more stable platform for precision sensors than ROVs or towed vehicles because an AUV is not subject to physical disturbances transmitted along the cable to the surface vessel. This absence of physical attachment also allows AUVs to measure ocean characteristics at specific depths and perform bottom-following missions as owing to its autonomy. In short, An AUV provides marine researchers with a new form of access to deeper ocean. For an AUV to successfully complete a typical survey mission, it must follow a path specified by the operator as closely as possible and arrive at a precise location for collecting data. When an AUV is not able to follow the path accurately during the mission, critical Source: Kalman Filter, Book edited by: Vedran Kordic, ISBN 978-953-307-094-0, pp. 390, May 2010, INTECH, Croatia, downloaded from SCIYO.COM

Conventional underwater pipeline inspections typically utilize Remotely Operated Vehicles (ROVs) tethered to Dynamic Positioning Class 2 (DP2) vessels, which entail significant expenses. To reduce these expenses, alternative inspection approaches have been explored. The Robotic Autonomous Underwater Vehicle (AUV) offers a cost-effective alternative by eliminating the need for a DP2 vessel and can instead be deployed using a more economical DP1 vessel, pioneering the underwater pipeline inspection. This game-changing Robotic AUV with Electric Field Gradient Sensor (EFGS) integration using a DP1 vessel can reduce inspection costs by up to 55% compared to using a ROV with a DP2 vessel. Additionally, it can reduce inspection durations from 19 days to just 7 days (a 60% reduction), significantly lowering the cost and carbon footprint due to the shorter execution time. The development of the AUV technology integrated with EFGS was a collaborative effort between PETRONAS and AUV developer, ROVULA. This innovative technology has successfully completed its pilot deployment in November 2024, inspecting a 30″ pipeline over a total length of 155 km offshore Sarawak. The Robotic AUV can achieve superior inspection coverage compared to conventional ROV methods and accomplishes the task in less time. This advantage is due to the untethered operation of the lightweight AUV, which allows it to move twice as fast as the ROV and eliminates the need for physical contact with the pipeline. The EFGS technology performs non-contact cathodic protection (CP) measurements. Unlike the conventional ROV CP stab method, which requires the ROV to frequently stop and stab the CP probe onto the anodes and bare metal (if exposed), the AUV with EFGS moves along the pipeline without stoppage. It accurately measures the field gradient along the entire pipeline without contacting the anodes or pipeline, even if it is buried.

High accuracy and reliable navigation in the underwater environment is very critical for the operations of autonomous underwater vehicles (AUVs). This paper proposes an adaptive federated interacting multiple model (IMM) filter, which combines adaptive federated filter and IMM algorithm for AUV in complex underwater environments. Based on the performance of each local system, the information sharing coefficient of the adaptive federated IMM filter is adaptively determined. Meanwhile, the adaptive federated IMM filter designs different models for each local system. When the external disturbances change, the model of each local system can switch in real-time. Furthermore, an AUV integrated navigation system model is constructed, which includes the dynamic model of the system error and the measurement models of strapdown inertial navigation system/Doppler velocity log (SINS/DVL) and SINS/terrain aided navigation (SINS/TAN). The integrated navigation experiments demonstrate that the proposed filter can dramatically improve the accuracy and reliability of the integrated navigation system. Additionally, it has obvious advantages compared with the federated Kalman filter and the adaptive federated Kalman filter.

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper OTC 28312, “Understanding the Global Scientific Value of Industry ROV Data To Quantify Marine Ecology and Guide Offshore Decommissioning Strategies,” by D.L. McLean, University of Western Australia; P. Macreadie, Deakin University; D.J. White, University of Southampton and University of Western Australia; P.G. Thomson, University of Western Australia; A. Fowler, New South Wales Department of Primary Industries; A.R. Gates, National Oceanography Centre, UK; M. Benfield, Louisiana State University; T. Horton, National Oceanography Centre; D. Skropeta, University of Wollongong; T. Bond, University of Western Australia; D.J. Booth, University of Technology, Australia; E. Techera, C. Pattiaratchi, and S.P. Collin, University of Western Australia; D.O.B. Jones, National Oceanography Centre; L. Smith, Woodside Energy; and J.C. Partridge, University of Western Australia, prepared for the 2018 Offshore Technology Conference Asia, 20–23 March, Kuala Lumpur. The paper has not been peer reviewed. Copyright 2018 Offshore Technology Conference. Reproduced by permission. Remotely operated vehicles (ROVs) are used by the offshore oil and gas industry to monitor the integrity of subsea infrastructure and, in doing so, collect terabytes of video and in-situ physical data from inaccessible regions and poorly understood marine environments. The complete paper describes the potential global scientific value of video and other data collected•by ROVs. ROVs and the Offshore Oil and Gas Industry Nearly one-third of global production of oil and gas is sourced offshore. Tens of thousands of offshore wells have been drilled, and more than 900 large-scale offshore oil and gas platforms have been installed. The offshore industry relies on underwater observation, control, and intervention, tasks performed increasingly by ROVs; over 700 were in operation globally at the time the paper was written. The advancement of ROV technology for the industry was prompted in the 1970s by developments extending into water depths beyond the capabilities of divers, and by the desire to reduce reliance on diving operations. Modern ROVs fall into two main classes: work class and observation class. A work-class ROV is car-sized, usually cuboidal, with a buoyant upper part containing power equipment, thrusters, manipulators, and cameras. An umbilical connection to the surface vessel provides electrical and fiber-optic connections that allow control and data transfer. Observation-class ROVs are smaller—sometimes briefcase-sized—with limited power and manipulation capabilities, and are used primarily for inspection purposes. For observation missions that can be conducted without real-time intervention, autonomous underwater vehicles (AUVs) are increasingly popular. These are generally torpedo-shaped, with higher hydrodynamic stability than ROVs, and can offer a similar image-gathering platform. Oil and gas projects and other ocean-based industries such as offshore wind use ROVs at multiple stages in a project’s life. Surveys of the region before installation or construction are conducted using ROVs or AUVs to identify the bathymetry and substrate conditions for hazard assessments and to provide parameters for engineering design. Drilling activities commonly involve ROV monitoring at the seabed level, and, when the production facilities are installed, ROVs are used to perform observation of all subsea activities. During operation of a project, ROVs are used for inspection and intervention. The manipulators operate valves and interchange components, and, if required, can undertake cutting. Inspections include visual surveys as well as checking of anode electropotentials and clearance of marine fouling. Inspections commonly are undertaken annually but can be more or less frequent depending on risk level and maintenance requirements. In total, the ocean around a typical oil and gas development may be visited dozens of times by ROVs during the project’s life.

Velocity plays a crucial role for the reliable operation of autonomous underwater vehicles. This paper investigates velocity interval estimation for autonomous underwater vehicles with parametric uncertainties. The proposed method employs zonotopic reachability analysis to obtain an envelope that contains all possible state values. By incorporating a novel T-N-L structure, the width of the estimated interval is reduced by 25.5% relative to the conventional zonotopic Kalman filter method. Finally, pool experiments are conducted to validate the effectiveness of the proposed method.

Path planning plays an extremely important role in the operation of Autonomous Underwater Vehicles (AUVs) to accomplish the underwater task safely, successfully and efficiently. This paper introduces the Fast Marching method for the path planning of AUVs in 3D large-scale battlefield environments to solve the problem that AUVs require low probability of being found, navigation security and low fuel consumption. The algorithm is able to find the optimal path between two waypoints in the work space and comprehensively takes factors such as the risk of collision with obstacles and mine, detection probability, sailing depth and path length into account. It also considers the maneuverability constraints of the AUV, including the safety depth and turning radius, to obtain the final flyable path. Finally, the proposed algorithm is tested in a large-scale area containing underwater terrain elevation map, sonar and mine density map to evaluate its feasibility, stability and efficiency.

The paper is devoted to solving the problem of developing a synthesis method for combined position-force control systems (CS) for electric drives (ED) of multi-link underwater manipulators (MUM) mounted on autonomous underwater vehicles (AUV) operating in the mode of landing on the ground or on work sites, followed by rigid fixation of these AUV using special devices. To solve this problem, a comprehensive method is proposed in the first part of the paper. First, according to this method, a self-adjusting regulators are synthesized for the ED of each degree of freedom of the MUM, which makes it possible to stabilize its variable dynamic parameters at a given nominal level. This stabilization compensates for the influence of Coulomb and viscous friction, as well as part of the external torque caused by the mutual influences between the MUM links and their interaction with the viscous medium, on the quality of control. Then, to ensure the operation of the self-adjusting regulators and the ED CS, based on the initial nonlinear models of the ED, observers with a variable structure are synthesized to obtain information about the current values of external moments, as well as the rotation speeds and accelerations of all the ED output shafts. Using this information and measurements of only angle sensors of the specified output shafts, it was possible to synthesize position-force regulators for ED, minimizing the selected quadratic cost function, ensuring the specified movements of the output shafts of the item instance while creating the required moments. In the second part of the paper a synthesis of a combined position-force control system with all MUM elements was performed using these position-force regulators. As the results of computer modeling showed, this control system is capable of providing accurate movements of the MUM work tool along specified spatial trajectories and simultaneous creation of the required force effects on the work objects in the presence of the interaction of all its links with a viscous medium with variable parameters (viscous friction and added masses) while performing various complex technological operations.

The operation of Autonomous Underwater Vehicles (AUVs) within underwater sensor network fields provides an opportunity to reuse the network infrastructure for long baseline localisation of the AUV. Computationally efficient localisation can be accomplished using off-the-shelf hardware that is comparatively inexpensive and which could already be deployed in the environment for monitoring purposes. This paper describes the development of a particle filter based localisation system which is implemented onboard an AUV in real-time using ranging information obtained from an ad-hoc underwater sensor network. An experimental demonstration of this approach was conducted in a lake with results presented illustrating network communication and localisation performance.KeywordsSensor NodeCommunication RangeInertial Measurement UnitRound Trip TimeAutonomous Underwater VehicleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Monitoring and M A Record-Breakin Geophysical Mon Copyright 2011 b 10.1029/2011GM Following destruction of the Deepwater Horizon drilling rig, while unmitigated blowout from the Macondo well was ongoing, NOAA scientific response cruise GU-10-02 (27 May to 4 June 2010) employed coordinated ship and autonomous underwater vehicle (AUV) operations to locate and study deep hydrocarbon plumes. The ship hydrocast survey localized maximum optical signals of a deep plume, centered at ~1150 m depth, approximately 13 km southwest of the blowout. Deployed at this location, the AUV conducted a high-resolution survey of plume structure, which indicated small-scale topographic influences on plume transport. Maximum plume intensity was observed along the western slope of Biloxi Dome. The orientation of gradients in plume intensity relative to isobaths indicated flow from the dome slope onto the dome top. In terms of the relative proportions of major hydrocarbon groups, all plume samples southwest of the blowout exhibited similar composition. The chemical composition of the plume southwest of the blowout was significantly different from the composition of a weaker deep plume observed southeast of the blowout. Variation in optical signal from a colored dissolved organic matter (CDOM) fluorometer (FCDOM) explained up to 97% (median 88%) of the variance in the concentrations of individual hydrocarbon compounds. AUV data also showed that FCDOM was highly correlated with three other optical measurements (r > 0.97) and oxygen measurements (r = 0.95). The results provide unique perspective on small-scale dynamics of a deep plume and illustrate the potential for studying subsurface plumes of dispersed oil using AUVs with off-the-shelf sensors.

Autonomous underwater vehicles (AUVs) are used to execute an increasingly challenging set of missions in commercial, environmental and defense industries. The resources available to the AUV in service of these missions are typically a limited power supply and onboard sensing of its local environment. Optimal path planning is needed to maximize the chances that these AUVs will successfully complete long-endurance missions within their power budget. A time-optimal path planner has been recently developed to minimize AUV mission time required to traverse a dynamic ocean environment at a specified speed through the water. For many missions, time minimization is appropriate because the AUVs operate at a fixed propeller speed. However, the ultimate limiting constraint on AUV operations is often the onboard power supply, rather than mission time. While an empirical or theoretical relationship between mission time and power could be applied to estimate power usage in the path planner, the real power usage and availability on an AUV varies mission-to-mission, as a result of multiple factors, including vehicle buoyancy, battery charge cycle, fin configuration, and water type or quality. In this work, we use data collected from two mid-size AUVs operating in various conditions to learn the mission-to-mission variability in the power budget so that it could be incorporated into the mission planner.

Optimal Sensor Placement for Multiple Underwater Target Localization with Acoustic Range Measurements

Underwater object tracking holds considerable significance in the field of ocean engineering. Additionally, it serves as a crucial component in the operations of autonomous underwater vehicles (AUVs), particularly during tasks associated with capturing marine organisms. However, the attenuation and scattering of light result in shortcomings such as poor contrast in underwater images. Additionally, the motion deformation of marine organisms poses a significant challenge. Therefore, existing tracking algorithms face difficulty in direct application to underwater object tracking. To overcome this challenge, we propose a novel tracking architecture for the marine organism capturing of AUVs called ULOTrack. ULOTrack is based on a performance discrimination and re-detection framework and constitutes three modules: (1) an object tracker, which can extract multi-feature information of the underwater target; (2) a multi-layer tracking performance discriminator, which serves the purpose of evaluating the stability of the current tracking state, thereby reducing potential model drift; and (3) lightweight detection, which can predict the candidate boxes to relocate the lost tracked underwater object. We conduct comprehensive experiments to validate the efficacy of the designed modules. Finally, the results of the experimentation demonstrate that ULOTrack significantly outperforms existing approaches. In the future, we aim to carefully scrutinize and select more suitable features to enhance tracking accuracy and speed.

Considering that few or no human operators are directly involved in the operation of Autonomous Marine Systems (AMS), an online risk model is necessary to enhance the intelligence of the AMS, its situation awareness, and decision-making. The current study identifies the criteria for an online risk model for AMS, which can be used to assess its validity and effectiveness.Taking an under-ice Autonomous Underwater Vehicle (AUV) operation as an example, the current work investigates how different risk analysis methods, namely the Preliminary Hazard Analysis (PHA), the Systems Theoretic Process Analysis (STPA), and Procedural Hazard and Operability Analysis (HAZOP), contribute to fulfilling the different criteria for online risk modeling of AMS. The analysis results show that STPA can be considered a good basis for developing an online risk model due to its relatively good coverage of the identified evaluation criteria, especially its ability to handle the interaction between system and software failure. In addition, considering some shortcomings of using STPA and the changing role of human operators in the AMS operation, PHA and Procedural HAZOP can be used as complementary tools. It is expected that the analysis results and conclusions can be adapted to other AMS as well.

Oil and gas companies are increasingly being forced to conduct their sub-sea pipeline inspections in increasingly more hostile and deeper waters. Typically, Remotely Operated Vehicles (ROV) are used for sub-sea pipeline inspections and these are controlled by experienced ROV operators from a surface support vessel, which tend to make ROV operations costly. Conversely, an Autonomous Underwater Vehicle (AUV) can perform sub-sea pipeline inspections without the need for a human operator. This paper sets-out to describe the development of a novel Hybrid AUV (HAUV) system, composed of two unmanned vehicles and designed specifically for sub-sea pipeline inspection. The first vehicle is a compact Unmanned Surface Vehicle (USV), which tracks a sub-sea pipeline from the sea surface. The second vehicle is a HAUV, which is connected to the USV via a fibre optic tether. Both vehicles operate together to automatically inspect sub-sea pipelines. The complete system is controlled by a remote control station on the surface. At this early stage of development, its capability is limited due to its size and battery power source but lab-scale units have been developed and have undergone field testing. Our team study is ongoing and endeavours to refine the HAUV system-capabilities enabling us to offer a cost-effective approach for sub-sea pipeline inspection in the near future.

Bathymetric navigation enables the long-term operation of autonomous underwater vehicles by reducing navigation drift errors with no need for GPS position fixes. In the case that a bathymetric map is not available, the simultaneous localization and mapping (SLAM) algorithm is required, but this increases computational complexity and memory requirement. Panel-based bathymetric SLAM could considerably reduce the computational burden. However, it may suffers from incorrect update when the vehicle does not belong to the updated panel. This study proposes a new update method, called weighted grid partitioning, which considers the probability distribution of a vehicle's location, and is more effective in terms of the map accuracy, computational burden, and memory usage compared to standard update methods. The feasibility of the proposed algorithm is verified through simulations.