Pipeline Segmentation using Level-Set Method

Autonomous underwater vehicles (AUVs) have become main tools for underwater survey in scientific, military, and commercial applications. A significant amount of onboard power is needed to execute long-range missions; however, energy storage is limited in AUV. This paper presents a novel landing AUV that can land on the sea floor to perform long-term monitoring and measuring tasks and save onboard electric energy. The AUV has following characteristics. Firstly, a triangular structure of the AUV which is composed of three cabins is used to achieve stable bottom sitting. Secondly, the releasing mechanism is designed to eject and recover the AUV. Bottom sitting and recovering is verified by ocean trials. This paper provides guidance for design of similar AUVs.

Autonomous underwater vehicles (AUVs) allow us to survey and explore globally and freely in vast deep sea. We aim to develop long cruising range AUVs (LCAUVs) for researches of the global change, ocean-trench earthquake, and biodiversity and for exploring ocean resources. We have developed the first prototype of LCAUV, Urashima since 1998. The vehicle powered by a PEFC system marked the world record of cruising distance of 317 kilometers in 2005. The vehicle, 10 m long and 10 tons in weight, has the specifications: maximum depth ratings; 3500 m, maximum cruising speed; 3.2 knots, and endurance; 60 hours. This large vehicle has large user payload of a few hundreds kilograms. In 2007, research and development of the elemental technologies which will be utilizes for development of the second generation LCAUV started to expand cruising range to 3000 kilometers. For long range cruising, technology improvement of power sources, navigations, communications, and vehicle controlling are mainly important. Since endurance of the LCAUV becomes over 600 hours, during this cruising period the vehicle controller must autonomously deal with problems which may occur in any devices. If the vehicle controller does not recover a fatal problem, the controller appropriately leads status of the vehicle to quit an executing mission and ascend itself. We have improved the vehicle controller and plan sea-trials using the controller in January 2009. On the other hand, The AUV, MR-X1, 2.4 m long and 800 kg in weight, has been developed for scientific research and the test bed of the LCAUVs since 2000. This working AUV is also needed for installing or recovering observation equipment, tracking and sampling benthos or planktons, and doing simple repetitive works. The first sea-trial of MR-X1 was carried out in 2003. After that, the MR-X1 system is only used as a test bed of the developed devices in the LCAUV project. In this year, we have developed new items, a device installer on the sea-floor and mud core sampler for the MR-X1. In the technical report, a redundant controlling system for the LCAUV installed on the MR-X1 is described. And new items and their test results will be reported.

Autonomous Underwater vehicles (AUV) with reconfigurable payloads are rapidly becoming common. Their dynamic characteristics are affected when payloads change. Typically, retuning of the controller is required to maintain good control performance. To address this situation, we develop a technique to enable rapid identification of AUV dynamics online. We demonstrate the technique with a fin-controlled single-thruster torpedo-shaped AUV. By decoupling the system according to planar and horizontal motion, mathematical models for yaw and pitch dynamics are developed. This results in a second-order transfer function with auxiliary steady state fin deflection. Identification of continuous-time model was performed to preserve the physical meaning of the parameters. Identification in continuous-time requires time-derivative terms which are reconstructed using the state variable filter (SVF). Then, recursive least-square (RLS) algorithm is used to identify the unknown parameters. The proposed identification method was validated through field deployments of our AUVs. The online estimates compare favorably with results obtained from offline identification methods requiring numerical optimization. We demonstrate how turning radius of the AUV can be estimated accurately from the identified parameters. We also show how a gain-scheduled controller, with better control performance than a constant-gain controller, can be designed using the estimated parameters.

Autonomous underwater vehicles (AUVs) have great advantages for activities in deep oceans, and are expected as the attractive tool for near future underwater development or investigation. However, AUVs have various problems which should be solved for motion control, acquisition of sensors' information, behavioral decision, navigation without collision, self-localization and so on. This paper proposes an adaptive biologically inspired neural controller for trajectory tracking of AUVs in nonstationary environment. The kinematic adaptive neuro-controller is an unsupervised neural network, which is termed Self-Organization Direction Mapping Network (SODMN). The network uses an associative learning system to generate transformations between spatial coordinates and coordinates of propellers' velocity. The neurobiological inspired control architecture requires no knowledge of the geometry of the robot or of the quality, number, or configuration of the robot's sensors. The SODMN proposed in this paper represents a simplified way to understand in part the mechanisms that allow the brain to collect sensory input to control adaptive behaviours of autonomous navigation of the animals. The efficiency of the proposed neurobiological inspired controller for autonomous intelligent navigation was implemented on an underwater vehicle capable of operating during large periods of time for observation and monitoring tasks.

Autonomous underwater vehicles (AUVs) are becoming commonplace in the study of inshore coastal marine habitats. Combined with shipboard systems, scientists are able to make in-situ measurements of water column and benthic properties. In CSIRO, autonomous gliders are used to collect water column data, while surface vessels are used to collect bathymetry information through the use of swath mapping, bottom grabs, and towed video systems. Although these methods have provided good data coverage for coastal and deep waters beyond 50m, there has been an increasing need for autonomous in-situ sampling in waters less than 50m deep. In addition, the collection of benthic and water column data has been conducted separately, requiring extensive post-processing to combine data streams. As such, a new AUV was developed for in-situ observations of both benthic habitat and water column properties in shallow waters. This paper provides an overview of the Starbug X AUV system, its operational characteristics including vision-based navigation and oceanographic sensor integration.

Autonomous underwater vehicles (AUVs) have many, scientific, military, and commercial applications because of their potential capabilities and significant cost-performance improvements. Typical AUV development requires critical system design and many costly at-sea trials during which systems specifications can be validated. Modeling and simulation provides a cost-effective method for carrying out preliminary component or system (hardware and software) testing and verification. Due to the complexity of most AUV development and their tight schedules, the simulated systems often differ considerably from the actual embedded architecture, thereby reducing the fidelity of the entire simulation. We adopted a hardware-in-the-loop (HIL) simulation approach that includes some level of the system's hardware and software. As expected, an HIL simulation requires all components be run under hard real-time constraint. The paper presents our implementation and considerations on HIL simulation for single and multiple vehicles. A brief mention of sensor and environment modeling is also provided.

Autonomous Underwater Vehicles (AUVs) are widely used for marine survey, in both the coastal and deep sea areas and they are applicable to both civil and defense applications. They are pre-programmed and can operate without human intervention and this makes them attractive to many marine industries. A concern with AUVs is the high energy consumption required by their thrusters for depth control, buoyancy change and manoeuvrability and that adversely affects their performance and endurance. This paper presents the design and development of novel stand-alone variable buoyancy system for AUVs and investigates its performance through numerical and experimental investigations. The design idea is based upon the Pump Driven Variable Buoyancy System (PDVBS) and uses a hydraulic based method to control the buoyancy. The VBS is integrated into a medium sized AUV of 3 m length and the performance of the vehicle in vertical plane is investigated. The results are presented for a buoyancy change requirement of 5 kg and a diaphragm type positive displacement pump, with a buoyancy change rate of 5 kg/min, is utilized. Depth control performance of the AUV and its hovering capabilities, at a desired depth of 60 m using the Linear Quadratic Regulator (LQR) controller, are analysed in detail. Finally, the results indicate that the designed and developed VBS is effective in changing the buoyancy and controlling the heave velocity. These two features are expected to provide higher endurance and better performance in AUVs involved in rescue/attack operations.

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

Autonomous Underwater Vehicles (AUVs) are becoming increasingly popular for ocean exploration, industrial and military application. In particular, AUVs are becoming the attractive option for underwater search and survey operation as they are inexpensive compared to manned vehicles. Most of an AUV power is utilized by its propulsion system. Since the AUV power only depends on the onboard battery, the power consumption becomes a crucial issue in optimizing the AUV performance. In this study, the MSI300 propeller was analyzed in regards to its performance by using computational fluid dynamics (CFD). At first, a 3D scanner was used to capture the surface of the propeller and the geometry was converted into a nonuniform rational B-Spline Curve (NURBS) model. Then, CFD simulation was performed to analyze the propeller in the turbulent and Bollard pull condition. It was found that the thrust and torque coefficients are descending with the increasing value of advance ratio. The propeller efficiency is at 61.05%, which occurred at a 0.8 advance ratio. It shows that the CFD simulation has successfully represented the characteristics of the propeller.

Autonomous underwater vehicles (AUVs) are becoming increasingly ubiquitous due to the growing needs in exploring Ocean resources. One of the most challenging tasks in this domain relates to the energy these vehicles require, given the increase in the number of scientific payloads and on the mission complexity. One way to potentially reduce the amount of energy consumed during vertical motion is to replace or complement the thruster action with a controlled change of the vehicles floatation, using a variable buoyancy system (VBS). This paper presents the development of an electromechanical VBS for shallow depths, up to 100 m, to be included in an existing AUV. A preliminary mechanical design is presented, along with a mathematical model allowing the calculation of the energy spent by this device, based on the components manufacturers’ data. A comparison between the energy consumption using thrusters and the designed VBS is presented.

Autonomous Underwater Vehicles (AUV) capable of adapting their mission planning in response to sensor data have applicability in a variety of applications. For example, to find the source of a chemical in a fluid environment, an AUV could search a region until it detects the chemical plume, then steer the vehicle based on real-time chemical detections and flow information to maneuver the vehicle toward the odor source. Behavior based planning (BBP) is a reactive mission planning approach that is ideally suited for such applications. This article reviews behavior based planning, describes an example BBP designed to locate the deepest location within a test area, and includes results from in-water experiments on a REMUS AUV. This experiment was a test exercise in preparation for the use of BBP on a REMUS class vehicle to trace chemical plumes to their source.

Autonomous Underwater Vehicles (AUVs) have found their application in exploration and surveillance. This paper proposes a cooperative distributed search algorithm based on a minimum entropy approach for AUVs with capabilities like multi agent exploration. The work is motivated by the project “Thesaurus”, whose among its own purposes has the survey of marine areas of archaeological interest in the Tuscan Archipelago. The cooperative algorithm is developed to move each vehicle on the exploration area taking into account communication constraints and a prior prediction probability map, which is updated on line during the mission. The prior probability map over the exploration area is built through Gaussian kernel approximation, on the basis of previous findings and historical archival data.

Autonomous Underwater Vehicles (AUVs) are becoming indispensable for the maritime industry and defense applications. The nonlinear, time-varying, and highly coupled dynamics of AUVs, along with the parametric uncertainties and unmodeled dynamics, make the design of efficient controllers a hard task. This article explores a robust control strategy that aims at providing better tracking accuracy by reducing the switching gain in order to reduce chattering and the control error bandwidth. The performance of the proposed controller is demonstrated through rigorous simulation on an experimentally validated AUV, and superior path tracking performance is noted against sliding mode and time delay control methodologies under various uncertain conditions.

Autonomous Underwater Vehicles (AUVs) are powerful tools for exploring, investigating and managing our ocean resources. As the capabilities of these platforms continue to expand and they continue to mature as operational assets, navigation remains a fundamental technological component. This chapter presents a road map for the vehicle designer to aid in integrating the latest navigation methods into new platforms for science, industry and military platforms. Along the way, we point to emerging needs where new research can lead directly to an expansion of the operational abilities of these powerful tools. To accomplish this we start by describing the problem, explaining the needs of vehicle users and the challenges of autonomous localization. Next we explain the state of practice, how operational assets currently solve this difficult problem. To expand this explanation we present new research targeted at helping AUV builders to make the complex tradeoffs in creating a platform with the appropriate navigation solution. We conclude with an overview of the latest research and how these advances might soon become available for AUV operations in new environments such as the littoral zone, at the poles and under-ice. Throughout this chapter we attempt to reach across the disciplinary boundaries that separate the researcher from the operator.

Sonar image object detection plays a crucial role in obstacle detection, target recognition, and environmental perception in autonomous underwater vehicles (AUVs). However, the complex underwater acoustic environment introduces various interferences, such as noise, scattering, and echo, which hinder the effectiveness of existing object detection methods in achieving satisfactory accuracy and robustness. To address these challenges in forward-looking sonar (FLS) images, we propose a novel multi-level feature aggregation network (MLFANet). Specifically, to mitigate the impact of seabed reverberation noise, we designed a low-level feature aggregation module (LFAM), which enhances key low-level image features, such as texture, edges, and contours in the object regions. Given the common presence of shadow interference in sonar images, we introduce the discriminative feature extraction module (DFEM) to suppress redundant features in the shadow regions and emphasize the object region features. To tackle the issue of object scale variation, we designed a multi-scale feature refinement module (MFRM) to improve both classification accuracy and positional precision by refining the feature representations of objects at different scales. Additionally, the CIoU-DFL loss optimization function was constructed to address the class imbalance in sonar data and reduce model computational complexity. Extensive experimental results demonstrate that our method outperforms state-of-the-art detectors on the Underwater Acoustic Target Detection (UATD) dataset. Specifically, our approach achieves a mean average precision (mAP) of 81.86%, an improvement of 7.85% compared to the best-performing existing model. These results highlight the superior performance of our method in marine environments.