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Reconstruction of Riser Profiles by an Underwater Robot Using Inertial Navigation

机译:利用惯性导航,水下机器人重建提升管概况

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摘要

This paper proposes a kinematic model and an inertial localization system architecturefor a riser inspecting robot. The robot scrolls outside the catenary riser, used for underwaterpetroleum exploration, and is designed to perform several nondestructive tests. Itcan also be used to reconstruct the riser profile. Here, a realistic simulation model of robotkinematics and its environment is proposed, using different sources of data: oil platformcharacteristics, riser static configuration, sea currents and waves, vortex-induced vibrations,and instrumentation model. A dynamic finite element model of the riser generatesa nominal riser profile. When the robot kinematic model virtually scrolls the simulatedriser profile, a robot kinematic pattern is calculated. This pattern feeds error models of astrapdown inertial measurement unit (IMU) and of a depth sensor. A Kalman filter fusesthe simulated accelerometers data with simulated external measurements. Along the riservertical part, the estimated localization error between the simulated nominal and Kalmanfilter reconstructed robot paths was about 2 m. When the robot model approaches theseabed it assumes a more horizontal trajectory and the localization error increases significantly.
机译:本文提出了一种运动模型和惯性本地化系统建筑,即检测机器人。机器人滚动到围网提升板之外,用于疏水勘探,旨在执行几种非破坏性测试。 ITCAN还可用于重建提升管概况。这里,提出了一种逼真的仿真模型及其环境,使用不同的数据来源:石油平稳性,提升机静态配置,海流和波,涡旋诱导的振动和仪表模型。立管生成标称立管谱的动态有限元模型。当机器人运动学模型几乎滚动SIMUTIOLSSER配置文件时,计算机器人运动模式。该模式将Astrapdown惯性测量单元(IMU)和深度传感器的误差模型提供给误差模型。卡尔曼滤波器Fusesthe模拟加速度计具有模拟外部测量的数据。沿着立体型部分,模拟标称和KalmanFilter重建机器人路径之间的估计定位误差约为2米。当机器人模型接近后,它假定更横向轨迹,并且本地化误差显着增加。

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