Integrated Positioning System of Autonomous Underwater Robot and Its Application in High Latitudes of Arctic Zone

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AUV Application for Inspection of Underwater Communications

Regular inspection of underwater communications (pipelines and cables) is actual problem of modern oil and gas industry. Specially equipped vessels, towed underwater devices and remote operated vehicles /ROV/ are applied for these purposes as usually, but quality of acquired data does not always allow revealing emergencies at the proper time. Spot inspections by ROVs give difficultly comparable data (Baker, 1991; Murray, 1991). The perspective solution of the problem is autonomous underwater vehicles /AUV/ application as the intellectual carrier of research equipment (Evans et al., 2003; Kojima et al., 1997). According (Ageev, 2005) the main goals of pipeline and cables inspection are: 1. more accurate position determination (searching and tracking); 2. pipe sagging and freespan detection and measurement; 3. terrain survey on each side of communication by means of high frequency side scan sonar /HF SSS/ and detection of extraneous objects; 4. detection of damages; 5. leakage detection of transported substances (for pipelines). The pipeline and cable inspection by means of AUV includes two stages: preliminary (communication search and detection) and the main (motion along the communication with carrying out of necessary measurements, i.e. tracking). Exact mutual orientation of AUV and inspected object is required in real time during the tracking stage. To solve inspection tasks AUV should be equipped with reliable detection systems for inspected object recognition. Video, electromagnetic and echo-sounder data can be used for these purposes. Each of these devices demonstrates optimal results for certain classes of objects in appropriate conditions. For example, metal pipelines have the significant sizes and can be detected by all listed above devices. While underwater cables have a small diameter, because of this applicability of acoustic methods is limited (Petillot et al., 2002). Process of communications search and detection is complicated, as a rule, with a poor visibility of the given objects (strewed with a ground, silted or covered by underwater flora and fauna). Experiments with the use of AUV for inspection of underwater communications have been carried out for a long time. Usually only one instrument, which AUV is equipped with, is used for object detection. O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg Document type: Part of book or chapter of boo

A Technique to Navigate Autonomous Underwater Vehicles Using a Virtual Coordinate Reference Network during Inspection of Industrial Subsea Structures

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

A Technique to Navigate Autonomous Underwater Vehicles Using a Virtual Coordinate Reference Network during Inspection of Industrial Subsea Structures

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

Method for the Coordination of Referencing of Autonomous Underwater Vehicles to Man-Made Objects Using Stereo Images

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