CN114909608A - Trenchless pipeline positioning method based on MIMU/mile wheel/photoelectric speed measurement sensor combination - Google Patents

Trenchless pipeline positioning method based on MIMU/mile wheel/photoelectric speed measurement sensor combination Download PDF

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CN114909608A
CN114909608A CN202210593233.1A CN202210593233A CN114909608A CN 114909608 A CN114909608 A CN 114909608A CN 202210593233 A CN202210593233 A CN 202210593233A CN 114909608 A CN114909608 A CN 114909608A
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filter
pipeline
matrix
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information
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CN114909608B (en
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李倩
郭一捷
张强
奔粤阳
赵玉新
吴磊
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Harbin Engineering University
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
    • F17D—PIPE-LINE SYSTEMS; PIPE-LINES
    • F17D5/00—Protection or supervision of installations
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
    • F16L—PIPES; JOINTS OR FITTINGS FOR PIPES; SUPPORTS FOR PIPES, CABLES OR PROTECTIVE TUBING; MEANS FOR THERMAL INSULATION IN GENERAL
    • F16L55/00—Devices or appurtenances for use in, or in connection with, pipes or pipe systems
    • F16L55/26—Pigs or moles, i.e. devices movable in a pipe or conduit with or without self-contained propulsion means
    • F16L55/28—Constructional aspects
    • F16L55/30—Constructional aspects of the propulsion means, e.g. towed by cables
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
    • F16L—PIPES; JOINTS OR FITTINGS FOR PIPES; SUPPORTS FOR PIPES, CABLES OR PROTECTIVE TUBING; MEANS FOR THERMAL INSULATION IN GENERAL
    • F16L55/00—Devices or appurtenances for use in, or in connection with, pipes or pipe systems
    • F16L55/26—Pigs or moles, i.e. devices movable in a pipe or conduit with or without self-contained propulsion means
    • F16L55/48—Indicating the position of the pig or mole in the pipe or conduit
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
    • F16L—PIPES; JOINTS OR FITTINGS FOR PIPES; SUPPORTS FOR PIPES, CABLES OR PROTECTIVE TUBING; MEANS FOR THERMAL INSULATION IN GENERAL
    • F16L2101/00—Uses or applications of pigs or moles
    • F16L2101/30—Inspecting, measuring or testing

Abstract

The invention discloses a non-excavation pipeline positioning method based on an MIMU (micro inertial measurement Unit)/mileage wheel/photoelectric speed measurement sensor combination. The invention utilizes the non-contact speed measurement and non-skid characteristics of the photoelectric speed measurement sensor to combine the photoelectric speed measurement sensor with the odometer to realize the purpose of redundant speed measurement, and utilizes the Federal Kalman filtering algorithm based on the maximum correlation entropy M estimation to realize the effective data fusion of the MIMU, the odometer wheel and the photoelectric speed measurement sensor information on the basis, thereby solving the problem of pipeline positioning performance reduction caused by unstable odometer wheel speed measurement information. The invention effectively reduces the positioning error caused by the slipping of the mileage wheel.

Description

Trenchless pipeline positioning method based on MIMU/mile wheel/photoelectric speed measurement sensor combination
Technical Field
The invention belongs to the field of underground pipeline geographical position information measurement, and particularly relates to a non-excavation pipeline positioning method based on MIMU/mileage wheel/photoelectric speed measurement sensor combination.
Background
The underground pipeline is an important component of urban infrastructure and has important functions of information, energy, water heating resource circulation, waste discharge and the like in cities. Due to the continuous development of urbanization, the number of underground pipelines is increasing, and the distribution of pipelines is more and more complicated. This brings difficulty to the use and maintenance of underground pipelines and the development of urban underground engineering. Therefore, it is necessary to establish an accurate three-dimensional map for the urban underground pipelines to prevent the pipelines from being damaged by excavation, construction and other projects. At present, common nondestructive pipeline positioning technologies, including ground penetrating radar positioning method, multi-frequency electromagnetic positioning method and magnetometer positioning method, have limitations, and positioning performance is limited by various factors such as pipeline materials, contents, pipeline diameter and depth and properties of overlying soil. Different from the method, the pipeline positioning instrument acquires the operation angular velocity and acceleration information by using an Inertial Measurement Unit (IMU) in the operation process of the pipeline, and realizes effective positioning of the pipeline through strapdown Inertial solution. However, because the accumulated principle error exists in the Micro-Electro-Mechanical Systems IMU (MIMU) strapdown solution, the accumulated error is usually corrected by the necessary external measurement information, so as to ensure that the positioning error of the pipeline is kept within the acceptable range. In the process of positioning the pipeline, the inertial calculation error is usually corrected by using the odometer wheel speed measurement information as external measurement information. Besides, the known position points on the pipeline can also update the position of the pipeline positioning instrument, and further inhibit the accumulation of inertial calculation errors.
In published articles, for example, in the article "pipe defect positioning technology based on volume kalman smoothing filter", published in journal 4 of "journal of the science of sensing technology" by the Yang theory of Shenyang industry university in 2015, a pipe defect positioning system is formed by using MIMU and a mileage wheel, and the data fusion of the MIMU and the mileage wheel is realized based on a volume kalman filter algorithm; a pipeline combined positioning system is constructed in a text of detection positioning scheme feasibility research in a small-caliber pipeline by MEMS inertial navigation, which is published by a bovine Xiaojie of Wuhan university in 2016 in journal of technical journal of sensing, volume 29, and phase 1, wherein a used data fusion algorithm is an extended Kalman filter; the application of the integrated navigation technology in the oil and gas pipeline surveying and mapping system, published by the research institute of Beijing automated control equipment in 2008, Chaojiang in the journal of China journal of the technical university, volume 16 and phase 6, is mainly researched and utilized to form a long-distance oil and gas pipeline surveying and mapping system by using a fiber-optic gyroscope strapdown inertial navigation system, a mileage wheel and a fixed-distance magnetic marker, and realize the data fusion of the fiber-optic gyroscope strapdown inertial navigation system and the mileage wheel based on an L-D improved Kalman filtering algorithm; in a text of pipeline center line measurement method based on inertial navigation, which is published by the Chinese pipeline petroleum company, lie ui in journal, volume 32, volume 9, of oil and gas storage and transportation in 2013, a laser gyro strapdown inertial navigation system, a mileage wheel and GPS data are effectively fused based on a Kalman filtering algorithm so as to construct a pipeline center line measurement system; in a text entitled "pipeline detection high-precision positioning method based on reverse solution", published in journal of volume 26, 4 of "Chinese journal of inertial technology academic, by Huangfengrong, Hebei Industrial university in 2018, a pipeline positioning method based on a strapdown inertial navigation system, a odometer and position mark points is mainly researched, wherein a filtering algorithm used by both forward solution and reverse solution is a Kalman filter; the Yang governing practice of Shenyang industry university in 2013 mainly researches a forward volume Kalman filtering algorithm taking a mileage wheel speed as an observed quantity and a backward smoothing two-stage filtering algorithm taking a datum point position as a starting point in a mileage correction algorithm for detection in a pipeline geographic coordinate published in journal No. 34 and No. 1 of Instrument and Meter journal, so as to realize optimal estimation on a pipeline defect geographic coordinate. In summary, the conventional pipeline positioning method is mainly based on the combination of a strapdown inertial navigation system and a mileage wheel, and performs effective data fusion on data of two sensors by using a relevant data fusion algorithm, and further corrects a pipeline positioning error by using a GPS or a position mark point on the basis. However, because the mileage wheel belongs to a contact type speed measurement sensor, when the inner wall of the pipeline is uneven or oil stains exist, the mileage wheel is easy to be separated from the pipeline wall so as to cause a slipping phenomenon, and unstable speed measurement information of the mileage wheel directly causes the reduction of the positioning performance of the pipeline. Therefore, the method for positioning the multi-sensor combined pipeline, which can inhibit the influence of the slipping of the mileage wheel on the positioning performance of the pipeline, is innovative.
Disclosure of Invention
The invention aims to provide a trenchless pipeline positioning method based on MIMU/odometer wheel/photoelectric speed measuring sensor combination.
The purpose of the invention is realized by the following technical scheme:
a trenchless pipeline positioning method based on MIMU/mile wheel/photoelectric speed measurement sensor combination specifically comprises the following steps:
step 1: measuring the latitude of the starting point of the pipeline by using a total station
Figure BDA0003666468380000021
Longitude λ 0 And height h 0 ；
Step 2: placing the pipeline positioning instrument provided with the MIMU, the mileage wheel and the photoelectric speed measuring sensor at the inlet of the pipeline to start, and placing the latitude obtained in the step 1
Figure BDA0003666468380000022
Longitude λ 0 And height h 0 Manually binding the target object into a data processor of a pipeline locator and finishing initial alignment;
and step 3: pulling the pipeline locator to pass through the pipeline to reach the pipeline end point by using a pull rope and pulling back the pipeline locator reversely to obtain sensor data of the pipeline locator in the forward running process and sensor data in the reverse running process, wherein the sensor data comprises angular speed output of an MIMU gyroscope
Figure BDA0003666468380000023
MIMU accelerometer specific force output f b Mileage wheel mileage increment Δ S anddisplacement increment delta X output by the photoelectric speed measuring sensor;
and 4, step 4: utilizing the angular velocity information obtained in the process that the pipeline locator in the step 3 runs along the pipeline
Figure BDA0003666468380000024
Sum specific force information f b Performing strapdown inertial solution to obtain a strapdown attitude matrix
Figure BDA0003666468380000025
Speed of strapdown solution
Figure BDA0003666468380000026
And strapdown resolving the location
Figure BDA0003666468380000027
And 5: calculating to obtain corresponding odometer speed information by using the odometer wheel mileage increment delta S obtained in each sampling period in the process of running the pipeline locator along the pipeline in the step 3 and the output displacement increment delta X of the photoelectric speed measuring sensor
Figure BDA0003666468380000031
Speed information of photoelectric speed measuring sensor
Figure BDA0003666468380000032
The formula is as follows:
Figure BDA0003666468380000033
wherein, T s Is a sampling period;
step 6: using MIMU as reference system, and based on the obtained odometer speed information in step 5
Figure BDA0003666468380000034
And the strapdown resolving speed information obtained in the step 4
Figure BDA0003666468380000035
With strapdown attitude matrix
Figure BDA0003666468380000036
Establishing measurement information in the federal filter structure subfilter 1, wherein the formula is as follows:
Figure BDA0003666468380000037
wherein, V DE ,V DN ,V DU Projecting the speed information of the mileage wheel in a navigation coordinate system;
and 7: taking the MIMU as a reference system, and based on the speed information of the photoelectric speed measuring sensor obtained in the step 5
Figure BDA0003666468380000038
And the strapdown resolving speed information obtained in the step 4
Figure BDA0003666468380000039
With strapdown attitude matrix
Figure BDA00036664683800000310
Establishing measurement information in the federal filter structure sub-filter 2, wherein the formula is as follows:
Figure BDA00036664683800000311
wherein, V LE ,V LN ,V LU Projecting speed information of the photoelectric speed measurement sensor in a navigation coordinate system;
and step 8: establishing a state equation and a measurement equation of a sub-filter 1 and a sub-filter 2 in a federal filter structure;
and step 9: initializing a sub-filter 1 and a sub-filter 2 in a federated filtering structure;
step 10: performing iterative estimation in the sub-filters 1 and 2 based on the maximum correlation entropy M estimation robust Kalman filtering to obtain state vector estimation values and estimation values of the sub-filters 1 and 2An error covariance matrix; wherein, the state vector estimated by the k-th sub-filter 1 is estimated to be X 1,k The covariance matrix of the estimation error is P 1,k The state vector estimated by the k-th sub-filter 2 is estimated as X 2,k The covariance matrix of the estimation error is P 2,k ；
Step 11: transmitting the state vector estimated value and the estimated error covariance matrix obtained in the step 10 to a main filter, and performing sub-filter data fusion in the main filter by adopting a fusion reset mode so as to obtain a state vector estimated value and an estimated error covariance matrix of the main filter; wherein, the k-th main filter state vector estimation value X g,k And estimation error covariance matrix P g,k The formula is as follows:
Figure BDA0003666468380000041
step 12: using the k-th state vector estimate X of the main filter obtained in step 11 g,k And estimation error covariance matrix P g,k Resetting state vector estimate X in step k in sub-filter 1 1,k Estimating an error covariance matrix P 1,k And the system noise variance matrix Q 1,k The formula is as follows:
Figure BDA0003666468380000042
at the same time, the k-th state vector estimation X of the main filter obtained in step 11 is used g,k And estimation error covariance matrix P g,k Resetting the state vector estimate X of step k in sub-filter 2 2,k Estimating an error covariance matrix P 2,k And the system noise variance matrix Q 2,k The formula is as follows:
Figure BDA0003666468380000043
wherein Q is 0 As a main filter system noise variance matrix, can be empirically derivedTaking values; beta is a 1,k ,β 2,k The information of the k-th step sub-filter 1 and sub-filter 2 is assigned with factors, respectively, as follows:
Figure BDA0003666468380000044
wherein, T 1 ,T 2 The failure alarm threshold values of the sub-filter 1 and the sub-filter 2 are respectively selected according to experience; lambda 1,k ,Λ 2,k The k-th fault detection function values of the sub-filter 1 and the sub-filter 2 are respectively obtained;
step 13: repeating the steps 10 to 12 until all the data obtained in the process of the pipeline positioning instrument running along the pipeline in the forward direction are processed, and utilizing all the main filter state vector estimated values X obtained in the step 11 g Middle 1, 2, 3-dimensional element, namely strapdown inertial solution position error deltaP E ,δP N ,δP U And (3) feeding back the MIMU strapdown resolving position information obtained in the correction step 4, wherein the formula is as follows:
Figure BDA0003666468380000051
the corrected position information P E ,P N ,P U Outputting as the positioning information of the forward running pipeline;
step 14: utilizing the angular velocity information obtained in the process that the pipeline locator in the step 3 runs along the pipeline in the reverse direction
Figure BDA0003666468380000052
Specific force information f b And obtaining mileage increment delta S of the mileage wheel and displacement increment delta X output by the photoelectric speed measuring sensor in each sampling period, and repeating the steps from 4 to 13 to obtain positioning information P 'of the reverse running pipeline' E ,P′ N ,P′ U And on the basis of the above-mentioned information P for positioning pipeline E ,P N ,P U Carrying out weighting fusion to obtain pipeline positioning information, wherein the formula is as follows:
Figure BDA0003666468380000053
wherein L is the length of the pipeline; l is k The fusion position point is away from the pipeline end point length;
Figure BDA0003666468380000054
is a distance starting point L-L k Forward running pipe location information for length;
Figure BDA0003666468380000055
is a distance L from the end point of the pipeline k Length of the counter-running pipe location information.
Further, the state equation of the federal filter structure subfilter 1 established in step 8 is as follows:
Figure BDA0003666468380000056
wherein the state vector X is solved for the position error deltaP by strapdown E ,δP N ,δP U Strapdown resolving speed error delta V E ,δV N ,δV U Strapdown solution misalignment angle phi E ,φ N ,φ U Gyro drift epsilon x ,ε y ,ε z And accelerometer zero offset
Figure BDA0003666468380000057
Composition, the formula is as follows:
Figure BDA0003666468380000058
f (t) is a system state transition matrix, g (t) is a system process noise input matrix, w (t) is a system noise vector, and the specific form is as follows:
Figure BDA0003666468380000059
W(t)＝[w gx w gy w gz w ax w ay w az ] T
in matrix I 3×3 Is a 3 × 3 dimensional unit array, 0 a×b Is a zero matrix with a dimension of a multiplied by b,
Figure BDA00036664683800000510
for the strapdown attitude matrix, w, obtained in step 4 gx ,w gy ,w gz ,w ax ,w ay ,w az Is the noise of the gyroscope and the accelerometer,
Figure BDA0003666468380000061
for specific force output f of accelerometer b Projecting in a navigational coordinate system
Figure BDA0003666468380000062
An antisymmetric array of
Figure BDA0003666468380000063
The strapdown attitude matrix obtained in step 4 can be utilized
Figure BDA0003666468380000064
Outputting the specific force of the accelerometer obtained in the step 3
Figure BDA0003666468380000065
Projected to the navigational coordinate system, the formula is as follows:
Figure BDA0003666468380000066
the state equation of the sub-filter 2 in step 8 is the same as the state equation of the sub-filter 1. In step 8, the measurement equation of the neutron filter 1 is as follows:
Z 1 (t)＝H 1 (t)X(t)+V 1 (t)
wherein, the system measurement information Z 1 (t) obtained from step 6, V 1 (t) Mileage wheel measurement noise, H 1 (t) is a specific form of the measurement matrix:
Figure BDA0003666468380000067
in step 8, the measurement equation of the neutron filter 2 is as follows:
Z 2 (t)＝H 2 (t)X(t)+V 2 (t)
wherein, the system measurement information Z 2 (t) obtained from step 7, V 2 (t) is the measurement noise of the photoelectric speed measuring sensor, H 2 (t) is a measurement matrix, and the specific form is as follows:
Figure BDA0003666468380000068
further, the maximum correlation entropy M estimation robust kalman filter in step 10 is designed as follows:
respectively discretizing the sub-filter state equation and the measurement equation obtained in the step 8 to obtain:
Figure BDA0003666468380000069
wherein phi k|k-1 ,Γ k-1 ,H k Respectively a discretized system state transition matrix, a discretized system process noise input matrix and a discretized measurement matrix; x k ,Z k Respectively a system state vector and a measurement vector in the k step; w k Is a system noise vector, satisfies Gaussian distribution, has a mean of zero and a covariance matrix of Q k ；V k For measuring the noise vector, a Gaussian distribution is also satisfied, the mean value is zero, and the covariance matrix is R k ；
The k filtering iterative computation step comprises the following steps:
X k|k-1 ＝Φ k|k-1 X k-1
Figure BDA0003666468380000071
Figure BDA0003666468380000072
Ψ k ＝diag[G σ (e k,j )]
Figure BDA0003666468380000073
Figure BDA0003666468380000074
X k ＝X k|k-1 -K k (Z k -H k X k|k-1 )
P k ＝(I-K k H k )P k|k-1
wherein, X k|k-1 A state one-step prediction value; p k|k-1 Predicting an error covariance matrix for the state one step; e.g. of a cylinder k To normalize residual errors, e k,j To normalize residual e k The jth element of (a); Ψ k Is a weight matrix;
Figure BDA0003666468380000075
the measured noise covariance matrix is corrected by using the weight matrix; k k Is a filter gain matrix; p k Estimating an error covariance matrix for the state; kernel function G σ (e k,j ) Is selected as a Gaussian function, and the formula is as follows:
Figure BDA0003666468380000076
where σ is the kernel bandwidth.
Further, the fault detection function Λ in step 12 is provided 1,k ,Λ 2,k The specific calculation formula is as follows:
Figure BDA0003666468380000077
Figure BDA0003666468380000078
wherein Z is 1,k ,H 1,k ,X 1,k|k-1 ,R 1,k ,P 1,k|k-1 Respectively measuring a vector, a measuring matrix, a state one-step prediction value, a measuring noise covariance matrix and a state one-step prediction error covariance matrix in the kth step in the sub-filter 1; in the same way, Z 2,k ,H 2,k ,X 2,k|k-1 ,R 2,k ,P 2,k|k-1 The k-th step measurement vector, the measurement matrix, the state one-step prediction value, the measurement noise covariance matrix and the state one-step prediction error covariance matrix in the sub-filter 2 are respectively.
The invention has the beneficial effects that:
the invention provides a method for realizing redundant speed measurement by combining a photoelectric speed measurement sensor and a mileage wheel aiming at the problems of the slippage of the mileage wheel, the failure of measurement information and the reduction of the pipeline positioning performance caused by the accumulation of inertia resolving errors in the positioning process of a pipeline positioning instrument, and realizes the effective data fusion of the MIMU, the mileage wheel and the information of the photoelectric speed measurement sensor by using a Federal Kalman filtering algorithm based on the maximum correlation entropy M estimation on the basis of the characteristics that the photoelectric speed measurement sensor carries out non-contact speed measurement and does not have the slippage, thereby solving the problem of the reduction of the pipeline positioning performance caused by the instability of the speed measurement information of the mileage wheel.
In order to verify the beneficial effects of the method, the simulation comparison is carried out on the MIMU/odometer wheel/photoelectric speed measurement sensor combination-based trenchless pipeline positioning method and the MIMU/odometer wheel combination positioning method based on the conventional Kalman Filtering (KF) algorithm. In the simulation process, straight pipeline, bent pipeline and deep pipeline tracks existing in the urban underground pipeline are simulated, the length of the simulated underground pipeline track is 140m, a pipeline locator is used for drawing the pipeline at a constant speed of 0.4m/s to acquire data, and the sampling frequency is 100 HZ. Setting MIMU gyroscope zero bias to 12DEG/h, white noise of gyroscope
Figure BDA0003666468380000081
The accelerometer has zero bias of 0.075mg and white noise of the accelerometer
Figure BDA0003666468380000082
The scale factor of the mileage wheel is 0.995, and the scale factor of the photoelectric speed measuring sensor is 0.98. In order to simulate the slipping phenomenon of the odometer wheel in the working process of the pipeline positioning instrument, the output of the odometer wheel is set to zero for 5s at 100s and 200s of simulation respectively.
As can be seen from FIG. 2, when the odometer wheel slips, the MIMU/odometer wheel combination positioning error based on the conventional KF algorithm is obviously increased, the mean square error in the east direction reaches 1.2053m, the mean square error in the north direction reaches 2.4018m, and the mean square error in the sky direction is 0.1066 m. According to the combined positioning method of the MIMU/mileage wheel/photoelectric speed measurement sensor, when the mileage wheel slips, the photoelectric speed measurement sensor can still output correct speed measurement information to correct the inertial navigation system, so that the positioning error caused by the slipping of the mileage wheel can be effectively reduced, the mean square error of the east direction is 0.0919m, the mean square error of the north direction is 0.0560m, and the mean square error of the sky direction is 0.0566 m. Simulation results show that the combined positioning method of the MIMU/odometer wheel/photoelectric speed measuring sensor can effectively reduce positioning errors caused by slipping of the odometer wheel, and compared with the combined positioning method of the MIMU/odometer wheel, the east positioning accuracy of the pipeline is improved by 92.3%, the north positioning accuracy is improved by 97.6%, and the day positioning accuracy is improved by 46.9%.
Drawings
FIG. 1 is a flow chart of an embodiment of a trenchless pipe positioning method based on the combination of MIMU/odometer wheel/photoelectric speed measuring sensor according to the present invention;
FIG. 2 is a comparison graph of the MIMU/odometer wheel combined positioning effect based on the conventional KF algorithm and the combined positioning result based on the MIMU/odometer wheel/photoelectric speed measuring sensor in the invention;
fig. 3 is a comparison result of the MIMU/odometer wheel combined positioning error based on the conventional KF algorithm and the combined positioning error based on the MIMU/odometer wheel/photoelectric speed measuring sensor in the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Step 1: measuring the latitude of the starting point of the pipeline by using a total station
Figure BDA0003666468380000091
Longitude lambda 0 And height h 0 ；
Step 2: the pipe locator provided with the MIMU, the mileage wheel and the photoelectric speed measuring sensor is arranged at the inlet of the pipe to be started, and the latitude obtained in the step 1 is
Figure BDA0003666468380000092
Longitude λ 0 And height h 0 Manually binding the target object into a data processor of a pipeline locator and finishing initial alignment;
and step 3: pulling the pipeline locator to pass through the pipeline to reach the pipeline end point by using a pull rope and pulling back the pipeline locator reversely to obtain sensor data of the pipeline locator in the forward running process and sensor data in the reverse running process, wherein the sensor data comprises angular speed output of an MIMU gyroscope
Figure BDA0003666468380000093
MIMU accelerometer specific force output f b Mileage increment delta S of the mileage wheel and displacement increment delta X output by the photoelectric speed measuring sensor;
and 4, step 4: utilizing the angular velocity information obtained in the process that the pipeline locator in the step 3 runs along the pipeline
Figure BDA0003666468380000094
Sum specific force information f b Performing strapdown inertial solution to obtain a strapdown attitude matrix
Figure BDA0003666468380000095
Speed of strapdown solution
Figure BDA0003666468380000096
And strapdown resolving the location
Figure BDA0003666468380000097
And 5: calculating to obtain corresponding odometer speed information by using the odometer wheel mileage increment delta S obtained in each sampling period in the process of running the pipeline locator along the pipeline in the step 3 and the output displacement increment delta X of the photoelectric speed measuring sensor
Figure BDA0003666468380000098
Speed information of photoelectric speed measuring sensor
Figure BDA0003666468380000099
Namely, it is
Figure BDA00036664683800000910
In the formula: t is s Is a sampling period;
step 6: using MIMU as reference system, and obtaining the speed information of the mileage wheel based on the step 5
Figure BDA00036664683800000911
And the strapdown resolving speed information obtained in the step 4
Figure BDA00036664683800000912
With strapdown attitude matrix
Figure BDA00036664683800000913
Establishing measurement information in the Federal Filter Structure sub-Filter 1, i.e.
Figure BDA00036664683800000914
In the formula: v DE ,V DN ,V DU Projecting the speed information of the mileage wheel in a navigation coordinate system;
and 7: taking the MIMU as a reference system, and based on the speed information of the photoelectric speed measuring sensor obtained in the step 5
Figure BDA0003666468380000101
And the strapdown resolving speed information obtained in the step 4
Figure BDA0003666468380000102
With strapdown attitude matrix
Figure BDA0003666468380000103
Establishing measurement information in the Federal Filter Structure sub-Filter 2, i.e.
Figure BDA0003666468380000104
In the formula: v LE ,V LN ,V LU Projecting speed information of the photoelectric speed measurement sensor in a navigation coordinate system;
and 8: establishing a state equation of a sub-filter 1 in a federal filtering structure, wherein the specific form is as follows:
Figure BDA0003666468380000105
in the formula: solving the state vector X for the position error deltaP by strapdown E ,δP N ,δP U Strapdown resolving speed error delta V E ,δV N ,δV U Strapdown solution misalignment angle phi E ,φ N ,φ U Gyro drift epsilon x ,ε y ,ε z And accelerometer zero offset
Figure BDA0003666468380000106
Is composed of, i.e.
Figure BDA0003666468380000107
F (t) is a system state transition matrix, G (t) is a system process noise input matrix, W (t) is a system noise vector, and the specific form is as follows:
Figure BDA0003666468380000108
W(t)＝[w gx w gy w gz w ax w ay w az ] T
in matrix I 3×3 Is a 3 × 3 dimensional unit array, 0 a×b Is a zero matrix with a dimension of a multiplied by b,
Figure BDA0003666468380000109
for the strapdown attitude matrix, w, obtained in step 4 gx ,w gy ,w gz ,w ax ,w ay ,w az Is the noise of the gyroscope and the accelerometer,
Figure BDA00036664683800001010
for specific force output f of accelerometer b Projecting in a navigational coordinate system
Figure BDA00036664683800001011
An antisymmetric array of
Figure BDA00036664683800001012
The strapdown attitude matrix obtained in step 4 can be utilized
Figure BDA00036664683800001013
Outputting the specific force of the accelerometer obtained in the step 3
Figure BDA00036664683800001014
Projected onto a navigational coordinate system, i.e.
Figure BDA00036664683800001015
The state equation for building the sub-filter 2 in the federal filter structure is the same as the state equation for the sub-filter 1. Further, a measurement equation of the neutron filter 1 in the federal filter structure is established, and the specific form is as follows:
Z 1 (t)＝H 1 (t)X(t)+V 1 (t)
in the formula: system measurement information Z 1 (t) obtained from step 6, V 1 (t) Mileage wheel measurement noise, H 1 (t) is a measurement matrix, and the specific form is as follows:
Figure BDA0003666468380000111
establishing a measurement equation of the neutron filter 2 in the federal filter structure, wherein the specific form is as follows:
Z 2 (t)＝H 2 (t)X(t)+V 2 (t)
in the formula: system measurement information Z 2 (t) obtained from step 7, V 2 (t) is the measurement noise of the photoelectric speed measurement sensor, H 2 (t) is a measurement matrix, and the specific form is as follows:
Figure BDA0003666468380000112
and step 9: initializing a sub-filter 1 and a sub-filter 2 in a federated filtering structure;
step 10: estimating robust Kalman filtering based on the maximum correlation entropy M in the sub-filters 1 and 2 to carry out iterative estimation so as to obtain state vector estimation values and estimation error covariance matrixes of the sub-filters 1 and 2; wherein, the state vector estimated by the k-th sub-filter 1 is estimated to be X 1,k The covariance matrix of the estimation error is P 1,k The state vector estimated by the k-th sub-filter 2 is estimated as X 2,k The covariance matrix of the estimation error is P 2,k ；
The maximum correlation entropy M estimation robust Kalman filter is designed as follows: respectively discretizing the sub-filter state equation and the measurement equation obtained in the step 8 to obtain:
Figure BDA0003666468380000113
in the formula:Φ k/k-1 ,Γ k-1 ,H k respectively a discretized system state transition matrix, a discretized system process noise input matrix and a discretized measurement matrix; x k ,Z k Respectively a system state vector and a measurement vector in the k step; w k Is a system noise vector, satisfies a Gaussian distribution, has a mean of zero and a covariance matrix of Q k ；V k For measuring the noise vector, a Gaussian distribution is also satisfied, the mean value is zero, and the covariance matrix is R k ；
The k filtering iterative computation step comprises the following steps:
X k|k-1 ＝Φ k|k-1 X k-1
Figure BDA0003666468380000121
Figure BDA0003666468380000122
Ψ k ＝diag[G σ (e k,j )]
Figure BDA0003666468380000123
Figure BDA0003666468380000124
X k ＝X k|k-1 -K k (Z k -H k X k|k-1 )
P k ＝(I-K k H k )P k|k-1
in the formula: x k|k-1 A state one-step prediction value; p k|k-1 Predicting an error covariance matrix for the state one step; e.g. of the type k To normalize residual errors, e k,j To normalize residual e k The jth element of (a); Ψ k Is a weight matrix;
Figure BDA0003666468380000125
the measured noise covariance matrix is corrected by using the weight matrix; k is k Is a filter gain matrix; p k Estimating an error covariance matrix for the state; kernel function G σ (e k,j ) Is chosen as a Gaussian function, i.e.
Figure BDA0003666468380000126
In the formula: sigma is kernel function bandwidth;
step 11: and (3) transmitting the state vector estimated value and the estimation error covariance matrix obtained in the step (10) to a main filter, and performing sub-filter data fusion in the main filter by adopting a fusion reset mode to obtain the state vector estimated value and the estimation error covariance matrix of the main filter. Wherein, the k-th main filter state vector estimation value X g,k And estimation error covariance matrix P g,k The specific calculation formula is as follows:
Figure BDA0003666468380000127
step 12: using the k-th state vector estimate X of the main filter obtained in step 11 g,k And estimation error covariance matrix P g,k Resetting the state vector estimate X in step k in sub-filter 1 1,k Estimating an error covariance matrix P 1,k And the system noise variance matrix Q 1,k I.e. by
Figure BDA0003666468380000131
At the same time, the k-th state vector estimation value X of the main filter obtained in step 11 is used g,k And estimation error covariance matrix P g,k Resetting the state vector estimate X of step k in sub-filter 2 2,k Estimating an error covariance matrix P 2,k And the system noise variance matrix Q 2,k I.e. by
Figure BDA0003666468380000132
In the formula: q 0 The noise variance matrix of the main filter system can be taken according to experience; beta is a 1,k ,β 2,k Respectively distributing factors for the information of the k-th step sub-filter 1 and the sub-filter 2, wherein the specific calculation formula is as follows:
Figure BDA0003666468380000133
in the formula: t is 1 ,T 2 The failure alarm threshold values of the sub-filter 1 and the sub-filter 2 are respectively selected according to experience; lambda 1,k ,Λ 2,k The k-th step fault detection function values of the sub-filter 1 and the sub-filter 2 are respectively, and the specific calculation formulas are respectively as follows:
Figure BDA0003666468380000134
Figure BDA0003666468380000135
in the formula: z 1,k ,H 1,k ,X 1,k|k-1 ,R 1,k ,P 1,k|k-1 Respectively measuring a vector, a measuring matrix, a state one-step prediction value, a measuring noise covariance matrix and a state one-step prediction error covariance matrix in the kth step in the sub-filter 1; in the same way, Z 2,k ,H 2,k ,X 2,k|k-1 ,R 2,k ,P 2,k|k-1 Respectively measuring a vector, a measuring matrix, a state one-step prediction value, a measuring noise covariance matrix and a state one-step prediction error covariance matrix in the kth step in the sub-filter 2;
step 13: repeating the steps 10-12 until all the data obtained in the process of running the pipeline locator along the forward direction of the pipeline are processed, and utilizing all the state vector estimated values X of the main filter obtained in the step 11 g Middle 1, 2, 3Dimensional element, i.e. strapdown inertial solution position error deltaP E ,δP N ,δP U Feedback correction of the MIMU strapdown solution position information obtained in step 4, i.e.
Figure BDA0003666468380000136
The corrected position information P E ,P N ,P U Outputting as the positioning information of the forward running pipeline;
step 14: utilizing the angular velocity information obtained in the process that the pipeline locator in the step 3 runs along the pipeline in the reverse direction
Figure BDA0003666468380000141
Specific force information f b And (4) repeating the steps from 4 to 13 to obtain reverse running pipeline positioning information P 'by using the mileage wheel mileage increment obtained in each sampling period as S and the displacement increment delta X output by the photoelectric speed measuring sensor' E ,P′ N ,P′ U And on the basis of the above-mentioned information P for positioning pipeline E ,P N ,P U Performing weighted fusion to obtain pipe location information, i.e.
Figure BDA0003666468380000142
In the formula: l is the length of the pipeline; l is k The fusion position point is away from the pipeline end point length;
Figure BDA0003666468380000143
is a distance starting point L-L k Length of forward running pipe positioning information;
Figure BDA0003666468380000144
is a distance L from the end point of the pipeline k Length of the reverse run pipe location information.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (4)

1. A trenchless pipeline positioning method based on MIMU/mile wheel/photoelectric speed measurement sensor combination is characterized in that: the method specifically comprises the following steps:
step 1: measuring the latitude of the starting point of the pipeline by using a total station
Figure FDA0003666468370000011
Longitude λ 0 And height h 0 ；
Step 2: placing the pipeline positioning instrument provided with the MIMU, the mileage wheel and the photoelectric speed measuring sensor at the inlet of the pipeline to start, and placing the latitude obtained in the step 1
Figure FDA0003666468370000012
Longitude λ 0 And height h 0 Manually binding the target object into a data processor of a pipeline locator and finishing initial alignment;
and step 3: pulling the pipeline locator to pass through the pipeline by using a traction rope to reach the pipeline end point and pulling back the pipeline to obtain sensor data in the forward running process and the sensor data in the reverse running process of the pipeline locator, wherein the sensor data comprises angular speed output of the MIMU gyroscope
Figure FDA0003666468370000013
MIMU accelerometer specific force output f b Mileage increment delta S of the mileage wheel and displacement increment delta X output by the photoelectric speed measuring sensor;
and 4, step 4: utilizing the angular velocity information obtained in the process that the pipeline locator in the step 3 runs along the pipeline
Figure FDA0003666468370000014
Sum specific force information f b Performing strapdown inertial solution to obtain a strapdown attitude matrix
Figure FDA0003666468370000015
Speed of strapdown solution
Figure FDA0003666468370000016
And strapdown resolving the location
Figure FDA0003666468370000017
And 5: calculating to obtain corresponding odometer speed information by using the odometer wheel mileage increment delta S obtained in each sampling period in the process of running the pipeline locator along the pipeline in the step 3 and the output displacement increment delta X of the photoelectric speed measuring sensor
Figure FDA0003666468370000018
Speed information of photoelectric speed measuring sensor
Figure FDA0003666468370000019
The formula is as follows:
Figure FDA00036664683700000110
wherein, T s Is a sampling period;
and 6: using MIMU as reference system, and obtaining the speed information of the mileage wheel based on the step 5
Figure FDA00036664683700000111
And the strapdown resolving speed information obtained in the step 4
Figure FDA00036664683700000112
With strapdown attitude matrix
Figure FDA00036664683700000113
Establishing measurement information in the federal filter structure sub-filter 1, wherein the formula is as follows:
Figure FDA00036664683700000114
wherein, V DE ，V DN ，V DU Projecting the speed information of the mileage wheel in a navigation coordinate system;
and 7: taking the MIMU as a reference system, and based on the speed information of the photoelectric speed measuring sensor obtained in the step 5
Figure FDA0003666468370000021
And the strapdown resolving speed information obtained in the step 4
Figure FDA0003666468370000022
With strapdown attitude matrix
Figure FDA0003666468370000023
Establishing measurement information in the federal filter structure sub-filter 2, wherein the formula is as follows:
Figure FDA0003666468370000024
wherein, V LE ，V LN ，V LU Projecting speed information of the photoelectric speed measurement sensor in a navigation coordinate system;
and step 8: establishing a state equation and a measurement equation of a sub-filter 1 and a sub-filter 2 in a federal filter structure;
and step 9: initializing a sub-filter 1 and a sub-filter 2 in a federated filtering structure;
step 10: estimating robust Kalman filtering based on the maximum correlation entropy M in the sub-filters 1 and 2 to carry out iterative estimation so as to obtain state vector estimation values and estimation error covariance matrixes of the sub-filters 1 and 2; wherein, the state vector estimated by the k-th sub-filter 1 is estimated to be X 1，k The covariance matrix of the estimation error is P 1，k The state vector estimated by the k-th sub-filter 2 is estimated as X 2，k The covariance matrix of the estimation error is P 2，k ；
Step 11: transmitting the state vector estimated value and the estimation error covariance matrix obtained in the step 10 to a main filter, and performing sub-filter data fusion in the main filter by adopting a fusion reset mode so as to obtain a state vector estimated value and an estimation error covariance matrix of the main filter; wherein, the k-th main filter state vector estimation value X g，k And estimation error covariance matrix P g，k The formula is as follows:
Figure FDA0003666468370000025
step 12: using the k-th state vector estimate X of the main filter obtained in step 11 g，k And estimation error covariance matrix P g，k Resetting state vector estimate X in step k in sub-filter 1 1，k Estimating an error covariance matrix P 1，k And the system noise variance matrix Q 1，k The formula is as follows:
Figure FDA0003666468370000026
at the same time, the k-th state vector estimation value X of the main filter obtained in step 11 is used g，k And estimation error covariance matrix P g，k Resetting the state vector estimate X of step k in sub-filter 2 2，k Estimating an error covariance matrix P 2，k And the system noise variance matrix Q 2，k The formula is as follows:
Figure FDA0003666468370000031
wherein Q is 0 The noise variance matrix of the main filter system can be taken according to experience; beta is a 1，k ，β 2，k The information of the k-th step sub-filter 1 and sub-filter 2 is assigned with factors, respectively, as follows:
Figure FDA0003666468370000032
wherein, T 1 ，T 2 The failure alarm threshold values of the sub-filter 1 and the sub-filter 2 are respectively selected according to experience; lambda 1，k ，Λ 2，k The k-th fault detection function values of the sub-filter 1 and the sub-filter 2 are respectively obtained;
step 13: repeating the steps 10 to 12 until all data obtained in the process of running the pipeline locator along the forward direction of the pipeline are processed, and utilizing all the main filter state vector estimated values X obtained in the step 11 g Middle 1, 2, 3 dimensional element, namely strapdown inertial solution position error deltaP E ，δP N ，δP U And (3) feeding back the MIMU strapdown resolving position information obtained in the correction step 4, wherein the formula is as follows:
Figure FDA0003666468370000033
the corrected position information P E ，P N ，P U Outputting as the positioning information of the forward running pipeline;
step 14: utilizing the angular velocity information obtained in the process that the pipeline locator in the step 3 runs along the pipeline in the reverse direction
Figure FDA0003666468370000034
Specific force information f b And obtaining mileage increment delta S of the mileage wheel and displacement increment delta X output by the photoelectric speed measuring sensor in each sampling period, and repeating the steps from 4 to 13 to obtain positioning information P 'of the reverse running pipeline' E ，P′ N ，P′ U And on the basis of the above-mentioned information P for positioning pipeline E ，P N ，P U Carrying out weighting fusion to obtain pipeline positioning information, wherein the formula is as follows:
Figure FDA0003666468370000035
wherein L is the length of the pipeline; l is k The fusion position point is away from the pipeline end point length;
Figure FDA0003666468370000036
is a distance starting point L-L k Length of forward running pipe positioning information;
Figure FDA0003666468370000041
is a distance L from the end point of the pipeline k Length of the counter-running pipe location information.
2. The method of claim 1 for positioning a trenchless pipe based on the combination of MIMU/odometer wheel/photoelectric speed sensor, wherein the method comprises the following steps: the state equation of the federal filter structure subfilter 1 established in the step 8 is as follows:
Figure FDA0003666468370000042
wherein the state vector X is solved for the position error deltaP by strapdown E ，δP N ，δP U Strapdown resolving speed error delta V E ，δV N ，δV U Strapdown solution misalignment angle phi E ，φ N ，φ U Gyro drift epsilon x ，ε y ，ε z And accelerometer zero offset
Figure FDA0003666468370000043
Composition, the formula is as follows:
Figure FDA0003666468370000044
f (t) is a system state transition matrix, G (t) is a system process noise input matrix, W (t) is a system noise vector, and the specific form is as follows:
Figure FDA0003666468370000045
W(t)＝[w gx w gy w gz w ax w ay w az ] T
in matrix I 3×3 Is a 3 × 3 dimensional unit array, 0 a×b Is a zero matrix with a dimension of a multiplied by b,
Figure FDA0003666468370000046
for the strapdown attitude matrix, w, obtained in step 4 gx ，w gy ，w gz ，w ax ，w ay ，w az Is the noise of the gyroscope and the accelerometer,
Figure FDA0003666468370000047
for specific force output f of accelerometer b Projecting in a navigational coordinate system
Figure FDA0003666468370000048
An antisymmetric array of
Figure FDA0003666468370000049
The strapdown attitude matrix obtained in step 4 can be utilized
Figure FDA00036664683700000410
Outputting the specific force of the accelerometer obtained in the step 3
Figure FDA00036664683700000411
Projected to the navigational coordinate system, the formula is as follows:
Figure FDA00036664683700000412
the state equation of the sub-filter 2 in step 8 is the same as the state equation of the sub-filter 1. In step 8, the measurement equation of the neutron filter 1 is as follows:
Z 1 (t)＝H 1 (t)X(t)+V 1 (t)
wherein, the system measurement information Z 1 (t) obtained from step 6, V 1 (t) Mileage wheel measurement noise, H 1 (t) is a specific form of the measurement matrix:
Figure FDA0003666468370000051
in step 8, the measurement equation of the sub-filter 2 is as follows:
Z 2 (t)＝H 2 (t)X(t)+V 2 (t)
wherein, the system measurement information Z 2 (t) obtained from step 7, V 2 (t) is the measurement noise of the photoelectric speed measuring sensor, H 2 (t) is a measurement matrix, and the specific form is as follows:
Figure FDA0003666468370000052
3. the method of claim 1 for positioning a trenchless pipe based on the combination of MIMU/odometer wheel/photoelectric speed sensor, wherein the method comprises the following steps: the maximum correlation entropy M estimation robust kalman filter in step 10 is designed as follows:
respectively discretizing the sub-filter state equation and the measurement equation obtained in the step 8 to obtain:
Figure FDA0003666468370000053
wherein phi k|k-1 ，Γ k-1 ，H k Respectively a discretized system state transition matrix, a discretized system process noise input matrix and a discretized measurement matrix; x k ，Z k Respectively a system state vector and a measurement vector in the k step; w k To be aThe system noise vector satisfies the Gaussian distribution, the mean value is zero, and the covariance matrix is Q k ；V k For measuring the noise vector, the Gaussian distribution is also satisfied, the mean is zero, and the covariance matrix is R k ；
The k filtering iterative computation step comprises the following steps:
X k|k-1 ＝Φ k|k-1 X k-1
Figure FDA0003666468370000054
Figure FDA0003666468370000055
Ψ k ＝diag[G σ (e k，j )]
Figure FDA0003666468370000056
Figure FDA0003666468370000057
X k ＝X k|k-1 -K k (Z k -H k X k|k-1 )
P k ＝(I-K k H k )P k|k-1
wherein, X k|k-1 A state one-step prediction value; p k|k-1 Predicting an error covariance matrix for the state one step; e.g. of the type k To normalize residual errors, e k，j To normalize residual e k The jth element of (a); Ψ k Is a weight matrix;
Figure FDA0003666468370000061
the measured noise covariance matrix is corrected by using the weight matrix; k k Is a filter gain matrix; p k Is shaped likeA state estimation error covariance matrix; kernel function G σ (e k，j ) Is selected as a Gaussian function, and the formula is as follows:
Figure FDA0003666468370000062
where σ is the kernel bandwidth.
4. The method of claim 1 for positioning a trenchless pipe based on the combination of MIMU/odometer wheel/photoelectric speed sensor, wherein the method comprises the following steps: wherein the fault detection function Λ in step 12 1，k ，Λ 2，k The specific calculation formula is as follows:
Figure FDA0003666468370000063
Figure FDA0003666468370000064
wherein Z is 1，k ，H 1，k ，X 1，k|k-1 ，R 1，k ，P 1，k|k-1 Respectively measuring a vector, a measuring matrix, a state one-step prediction value, a measuring noise covariance matrix and a state one-step prediction error covariance matrix in the kth step in the sub-filter 1; in the same way, Z 2，k ，H 2，k ，X 2，k|k-1 ，R 2，k ，P 2，k|k-1 The k-th step measurement vector, the measurement matrix, the state one-step prediction value, the measurement noise covariance matrix and the state one-step prediction error covariance matrix in the sub-filter 2 are respectively.
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