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Inertial Navigation System of Pipeline Inspection Gauge

机译:管道检测计的惯性导航系统

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摘要

In this brief, a high-accuracy inertial navigation system (INS) for a pipeline inspection gauge (PIG) is proposed. Two mechanization approaches are investigated. First, a full INS dynamic model is used, and second, a 3-D reduced inertial sensor system (RISS) is utilized. The INS uses the full inertial measurement unit (IMU) data to calculate the navigation solution, whereas the RISS uses an encoder, one single-axis gyroscope, and two accelerometers. The RISS model is proposed in this brief for its better accuracy and less computational complexity. Due to the accumulated error in the INS or RISS solution, an extended Kalman filter is proposed to fuse the IMU data with PIG measurements. For the full INS model, these measurements are the encoder's derived velocity constraint and the detected pipe length measurement. On the other hand, for the RISS model, it only refers to the detected pipe length measurement. An experimental setup, with a prototype of the in-pipe robot, is designed and built to test and validate our algorithms in a real pipe environment. Subsequently, the accuracy of the proposed algorithms is verified experimentally.
机译:在此简述中,提出了一种用于管道检查量表(猪)的高精度惯性导航系统(INS)。调查了两种机械化方法。首先,使用完整的INS动态模型,并且第二,使用3-D减小的惯性传感器系统(RIS)。 INS使用完整的惯性测量单元(IMU)数据来计算导航解决方案,而RISS使用编码器,一个单轴陀螺仪和两个加速度计。 RIS模型是提出的,以提高其更好的准确性和更少的计算复杂性。由于INS或RISS解决方案中的累积误差,提出了一个扩展的卡尔曼滤波器,以使IMU数据融合猪测量。对于完整的INS模型,这些测量是编码器的推导速度约束和检测到的管道长度测量。另一方面,对于RISS模型,它只是指检测到的管道长度测量。使用管道机器人的原型进行实验设置,以测试和构建以测试和验证我们在真正的管道环境中的算法。随后,通过实验验证所提出的算法的准确性。

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