Research On Attitude Measurement Method Of Horizontal Directional Drilling Tools

With the acceleration of urbanization,horizontal directional drilling technology has been paid more and more attention and application.In horizontal directional drilling construction,the guidance monitoring and pipeline detection of the guide instrument are the key to adjust the drill tool’s forward track.Mastering the underground moving state of the drill tool is convenient for the construction personnel to timely adjust the advance direction of the drill bit and make the guide hole drilling according to the predetermined track.In order to obtain the drill tool state information in directional drilling and improve the horizontal directional construction quality,this paper will carry on the thorough research around the drill tool attitude measurement method.Firstly,the paper briefly introduces the development and application advantages of horizontal directional drilling technology at home and abroad,and studies the theory and method of drilling tool attitude measurement.Then,designing the drill tool attitude information measurement system,and analyzing sources of the system.The calibration method is used to compensate the deterministic error.In order to compensate the random noise,the multi-sensor fusion algorithm is used to improve the general Unscented Kalman filter(UKF)algorithm,and establishing an eight-state parametric system model based on quaternions.Finally,the system is tested statically and dynamically with the help of the upper computer tool.The paper focuses on the following contents: 1.For the design of the drill tool attitude measurement system,in order to meet the requirements of small size,low power consumption and excellent cost,STM32103 + MARG(Magnetometer Accelerometer and Rate Gyro)combined attitude measurement program is developed,and the device layout is designed to reduce the system power consumption through working mode identification and conversion.2.Aiming at the deterministic errors of the system components,modeling and error calibration experiments were carried out for the accelerometer,gyroscope and magnetometer respectively,and the parameters in the model were determined.3.In view of the random noise generated by devices,the data fusion method of multiple sensors was studied.The quaternion in the updated state equation was compensated by the measurement of accelerometer and magnetometer.In order to reduce no trace of kalman filtering algorithm calculation cost,solve the problem of its divergent,fusion algorithm and fading factor,Carlson design improvements of no trace adaptive kalman filter algorithm,at the same time joining the symmetry judgement and reset to ensure the calculation of matrix is qualitative and symmetry to reduce dependence on the accuracy of the system equations of parameters and improve the stability of the system.