# The Development of the Pipe Jacking Guidance Technology
## 5 Inertial Guidance Method
### Research Status  
Theoretically, the pipe jacking machine posture can be measured without human intervention by using only the inertial navigation theory. However, due to the integration  
Figure 13: Schematic diagram of Dead Reckoning.  
process in the inertial guidance algorithm, there is a non-integrable error term. This error term can be ignored under a limited degree of time accumulation, but as the error accumulates to a certain limit, it will seriously affect the navigation accuracy. Consequently, the implementation of inertial guidance technology necessitates the suppression of errors to ensure compliance with the stipulated requirements. There are three primary categories of error suppression for inertial sensors. The first category is compensating correction of inertial sensors by multiple sensor measurement parameters. The second category is the design of error compensation algorithms [22]. The third category is the use of redundant devices to increase the number of inertial sensors [23]. The fourth category is self-compensating errors by rotational modulation techniques [24]. Presently, research endeavors are directed towards enhancing the precision of inertial sensors in pipe jacking. This objective is pursued by augmenting the number of sensor devices and refining optimization algorithms to ensure enhanced accuracy.  
The TMG-32B inertial guidance system from TOKYO KEIKI Co. Ltd. in Tokyo, Japan, contains gyroscopes and inclinometers internally. The dual-axis inclinometer measures pitch and roll angles, thereby improving the accuracy of attitude measurements through redundant gyroscope angle data information. The PN-200 system incorporates an odometer that facilitates the calculation of the pipe jacking distance. The azimuth of the pipe jacking machine is determined by the north-pointing function of the gyroscope. The position of the pipe jacking machine is calculated by integrating the odometer readings with the calculated waypoints and utilizing the attitude updating algorithm. Presently, the company's TMG-32F and PN-S1 guidance systems are considered to be in a relatively mature state. The RMS-D (Curve Type) automatic guidance system of Lixin Measurement (Shanghai, China) Co., Ltd. is predicated on an in-house developed elevation measuring instrument, the purpose of which is to provide elevation information. An odometer is placed at the starting shaft to detect the jacking distance of the pipe sheet. The odometer's readings are then combined with gyroscope data to calculate the pipe jacking machine's position information. The principle of this system is analogous to that of the products of the Japanese company.  
Based on the idea of multi-source data fusion, that is, fusing data information from different sensors to improve accuracy, [PERSON] [25] proposed a pipe jacking guidance scheme for a fiber optic gyro inertial navigation system. The Fiber Optic Gyro Inertial Measurement Unit (FOG-IMU) is situated in close proximity to the pipe jacking machine, with a static leveling configured above it. An odometer is positioned at the wall of the starting shaft to quantify the length of pipe jacking. A multi-sensor data fusion algorithm based on fiber optic gyro/odometer/static leveling is proposed, and a 15-dimensional Kalman filter is established. A horizontal position projection model is constructed based on odometry and a fiber optic gyro-inertial measurement unit (FOG-IMU). An elevation measurement model is also constructed based on static leveling. These models are used to obtain external gauging information for combined navigation. Finally, the guiding parameters for pipe jacking construction are derived, and the feasibility of the program is proved through experiments. [PERSON] [26] proposed a zero-speed detection algorithm to address the problem that the zero-speed update error compensation algorithm cannot accurately distinguish between motion and stationary states. Experimental results show that the zero-speed detection accuracy reaches 99.18%.