Browsing by Author "Ben Moallem, Issam"

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemEmbargo
    Localization for Autonomous Underwater Vehicles inside Harsh and GPS-Denied Environments
    (2023-12-01) Ben Moallem, Issam; Ghorbel, Fathi H.
    The localization of Autonomous Underwater Vehicles (AUVs) deployed for integrity inspection of liquid storage facilities, to prevent failure of the process, is a critical and challenging task. This is primarily due to the harsh and GPS-denied work environment, as well as to the high degree of accuracy required by such confined-space activities to ensure accurate motion control and navigation, and generate rigorous inspection data associated with their true physical locations. Conventionally, an AUV performing general surveying operations is equipped with an Inertial Navigation System (INS) and/or a Doppler Velocity Log (DVL) for real-time state estimation and positioning, with respect to some inertial reference frame, while navigating its local environment. Due to inherently accumulating measurement errors over time, an INS/DVL device usually relies on the GPS for periodic recalibrations, which requires surfacing of the submersible robot. In deep waters, this strategy is energy resource and time inefficient, hence costly. Furthermore, for covered and underwater environments such as storage tanks, GPS signals are not even accessible at the liquid surface. Moreover, neither the INS/DVL-GPS system nor the traditional baseline acoustic positioning systems, based on trilateration techniques, provide a satisfactory solution accuracy as demanded by precision tasks such as pinpointing defects in steel storage and underwater structures. To overcome the shortcomings of the conventional underwater localization techniques, and achieve high-fidelity mapping between inspection data and real physical locations, we propose in this thesis a novel, accurate, and robust method to solve the robot localization problem inside confined, harsh, and GPS-denied environments. This method uses affordable sensors and fast algorithms to develop new techniques that provide accurate positions of the mobile agent. Given the geometry of the asset under investigation, a map representation for the robot's workspace is constructed based on range measurements over its boundaries. Then, the robot's position and orientation are accurately estimated relative to some defined reference landmarks (features) extracted from the map. In the event that the robot fails to recognize any landmark, a point-set registration technique is employed. In this case, the robot recursively matches map observations while in motion, which yields a relative position with respect to the most recently determined landmark-based position. The devised localization method will unleash fully autonomous robotic operations in confined, harsh, and GPS-denied environments. It will also facilitate Risk-Based Inspection (RBI) by employing predictive capabilities to optimize maintenance planning. This method can be applied in the oil and gas industry for inspecting liquid storage assets such as Aboveground Storage Tanks (ASTs) and Floating Production Storage and Offloading units (FPSOs).
  • Thumbnail Image
    Item
    Magnetic Flux Leakage System for External Robotic Inspection of Oil and Gas Pipelines
    (2015-04-24) Ben Moallem, Issam; Ghorbel, Fathi Hassan; Dick, Andrew J.; Stanciulescu, Ilinca
    Pipelines transport invaluable energy resources such as crude oil and natural gas over long distances. The integrity of the piping system in terms of safety of the process is then of high importance. However, pipes are prone by time to defects that may degrade their properties and lead to failures. Particularly, wall thinning is a serious anomaly that threatens aging pipelines. Therefore, their inspection plays a critical role to prevent the collapse of the system. Magnetic Flux Leakage (MFL) is by far the most effective technique of nondestructive evaluation for robotic diagnosis of ferromagnetic pipes. This work follows a novel approach to control such problem and assess the condition of the pipe by measuring with a good precision the wall radial thickness based on calibrated curves of reference and using an MFL diagnostic system tool. The proposed technique is generic and can be applied systematically for pipes with different sizes and material properties. It represents an advancement over the current conventional practices which require multiple physical experiments to generate empirical reference curves. Such procedures are cumbersome, time consuming and in consequence costly. The MFL sensing tool will be placed at the end-effector of a mobile robot platform devoted for external pipe inspection in a desert environment. It is based on permanent magnets producing a strong magnetic field that locally magnetizes and saturates the sample in question. At areas where there is metal loss, the magnetic flux flowing in the pipe leaks from the wall, which is detected by a Hall effect sensor and compared to the reference curve to estimate the wall thickness.