CN110243363B - AGV real-time positioning method based on combination of low-cost IMU and RFID technology - Google Patents

AGV real-time positioning method based on combination of low-cost IMU and RFID technology Download PDF

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CN110243363B
CN110243363B CN201910593352.5A CN201910593352A CN110243363B CN 110243363 B CN110243363 B CN 110243363B CN 201910593352 A CN201910593352 A CN 201910593352A CN 110243363 B CN110243363 B CN 110243363B
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邹益胜
罗焕
王爽
蔡玮
张剑
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Southwest Jiaotong University
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    • 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
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    • H04W—WIRELESS COMMUNICATION NETWORKS
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
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Abstract

The invention discloses an AGV real-time positioning method based on the combination of low-cost IMU and RFID technology, which comprises the following steps of: carrying out zero acceleration detection and error correction on the low-cost IMU sensor, wherein the zero acceleration detection and the error correction comprise the compensation of the errors of the acceleration and the yaw angle; then removing the noise of the collected data: noise removal is carried out on data acquired by the IMU through a Kalman filtering pose updating algorithm, filtered AGV speed and displacement updating are obtained, and the real-time position of the AGV is obtained; and finally, performing position correction on the RFID: and the signals acquired by the RFID reader and the inertial sensing information are combined and positioned, and the position information is corrected at a specific reference node, so that the real-time accurate position information of the AGV is obtained. The method has low cost, can meet the actual positioning requirement, has higher stability and reliability compared with the method of simply using the INS, improves the positioning precision by 31 percent, and is suitable for real-time positioning of the indoor AGV.

Description

AGV real-time positioning method based on combination of low-cost IMU and RFID technology
Technical Field
The invention relates to the technical field of AGV real-time positioning, in particular to an AGV real-time positioning method based on the combination of low-cost IMU and RFID technologies.
Background
With the development of intelligent manufacturing, the industrial production in China will further develop towards the direction of intellectualization, flexibility and high integration, and an AGV (automatic Guided Vehicle) is used as a mobile robot for realizing automatic material transportation in an intelligent factory, and the real-time positioning of the AGV is the key for accurate material distribution.
In recent years, various indoor positioning methods based on technologies such as infrared rays, ultrasonic waves, bluetooth, Radio Frequency Identification (RFID), Ultra Wideband (UWB), and the like have been increasingly used in the fields of smart homes and the like, and have high positioning accuracy. The positioning technologies all show the problems of weak independence, line-of-sight transmission requirements and the like, assistance needs to be carried out by means of external equipment devices, data transmission is carried out through the hardware equipment, and once the external environment cannot meet the conditions, the positioning method also fails. Aiming at complex factory processing environment, the method is difficult to realize accurate positioning, and the inertial navigation positioning method based on self-sensor positioning becomes the key for solving the problem.
The inertial positioning does not depend on external information or radiation energy, and has absolute advantages in terms of good independence and facing to complex indoor environments. However, the cost of mature inertial navigation devices in the market is very high, and if a low-cost sensor is used, the problems that the static acceleration of the acquired data is zero-offset and serious, the motion acceleration is large in noise, the precision of the measured deflection angle is different at different measuring speeds, the calculated displacement is serious and the like can occur; in addition, the inertia positioning method is independently used for calculating the position of the AGV, the obtained error has an accumulative effect along with time, after a certain time, the positioning error exceeds an acceptable range, and the positioning is also meaningless.
The main error sources of an Inertial Navigation System (INS) are that the accuracy of a low-cost sensor is not high, the System error is large, an accelerometer and a gyroscope have zero errors, and the calculation result drifts seriously along with time.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide an AGV real-time positioning method based on a combination of a low-cost IMU and an RFID technology, which performs error correction on the IMU, and then corrects position information at a specific reference node by using a positioning method combining the RFID technology and inertial positioning, so as to obtain real-time accurate position information of the AGV. The technical scheme is as follows:
an AGV real-time positioning method based on the combination of low-cost IMU and RFID technology comprises the following steps:
step 1: and (3) compensating the sensor error: carrying out zero acceleration detection and error correction on the low-cost IMU sensor, wherein the zero acceleration detection and the error correction comprise the compensation of the errors of the acceleration and the yaw angle;
step 2: removing collected data noise: noise removal is carried out on data acquired by the IMU through a Kalman filtering pose updating algorithm, filtered AGV speed and displacement updating are obtained, and the real-time position of the AGV is obtained;
and step 3: and (3) performing position correction by the RFID: and the signals acquired by the RFID reader and the inertial sensing information are combined and positioned, and the position information is corrected at a specific reference node, so that the real-time accurate position information of the AGV is obtained.
Further, the compensation of the acceleration error in step 1 specifically includes: acquiring and analyzing acceleration data in a static state to obtain acceleration compensation values of the IMU in the X direction and the Y direction, and compensating the acceleration values according to the acceleration compensation values; the compensation of the yaw angle error specifically comprises the following steps: the method comprises the steps of measuring linear motion of the AGV to obtain error data of angle deflection, taking the running speed of the AGV as input, training and testing the angle deflection error by using a support vector machine method to obtain a model of the running speed and the angle deviation of the AGV, and compensating the angle deviation of the AGV in real time according to different running speeds according to the model.
Further, the specific process of step 2 includes:
step 21: performing coordinate transformation
In the positioning model, AGV is taken as a carrier coordinate system b system, a geographic coordinate system is taken as a navigation coordinate system n system, and acceleration information in the X direction and the Y direction at the k moment of the AGV under the carrier coordinate system b system is measured by an inertial navigation system
Figure BDA0002116806450000021
And deflection angle
Figure BDA0002116806450000022
Obtaining acceleration information and angle deflection information of the AGV under a navigation coordinate system n system according to an attitude calculation method:
Figure BDA0002116806450000023
in the formula:
Figure BDA0002116806450000024
b is the acceleration information at the time k,
Figure BDA0002116806450000025
n is the acceleration information at the time k,
Figure BDA0002116806450000026
the deflection angle of the AGV at the moment k is obtained;
converting the acceleration information under the b system into the n system through the transformation;
step 22: performing pose update
In an inertial navigation system, the position and speed of an AGV under an n series are updated according to information obtained by a sensor under a b series, and the model is as follows:
Figure BDA0002116806450000027
in the formula: subscripts X and Y denote the X and Y directions, respectively; j is a function ofk-1Jerk at time k-1, akAnd ak-1Acceleration at time k and time k-1, v, respectively, under nkAnd vk-1The velocities at time k and k-1, s, respectivelykAnd sk-1Displacement at time k and time k-1, respectively, and △ T is a sampling time interval;
UKF equation of state:
Figure BDA0002116806450000031
UKF measurement equation: zk＝HXk+Vk(4)
Where A is a state transition matrix, ukFor the system control input at time k, BkIs a state control matrix at time k, H is
The observation matrix is used to observe the matrix,Vkfor the observation of the noise at the time k,
Figure BDA0002116806450000032
for the system state variable estimation at time k-1,
Figure BDA0002116806450000033
is a k-time system
Estimating a state variable; zkAn observed variable at time k;
state variable X at time kk＝[Sk,xSk,yvk,xvk,yak,xak,y]'
The state transition matrix is known by a pose updating model and a state equation
Figure BDA0002116806450000034
The control matrix B is not considered herek(ii) a From the measurement equation, the observation matrix H ═ 000011]；
Applying a Kalman filtering elementary equation:
Figure BDA0002116806450000035
in the formula (I), the compound is shown in the specification,
Figure BDA0002116806450000036
carrying out prior estimation on the system state at the k-1 moment;
Figure BDA0002116806450000037
estimating the state prior of the system at the moment k; pk －Estimating an error covariance matrix for the k moment prior; pkAnd Pk-1Respectively estimating an error covariance matrix at the k moment and the k-1 moment; q is a process noise covariance matrix; r is a measurement noise covariance matrix; i is an identity matrix; kkKalman gain or blending factor. And finally, updating the filtered AGV speed and displacement to obtain the real-time position (X, Y) of the AGV.
The invention has the beneficial effects that: the method is researched aiming at a combined positioning method of a low-cost IMU and an RFID technology, and a real-time positioning error correction method is provided aiming at the error characteristics of the measurement data of the low-cost IMU, the INS position is solved through a Kalman filtering algorithm, and is combined with RFID electronic tag information to deduce a position updating equation of combined positioning, so that the indoor AGV pose information is obtained; the correctness of the method is verified through an actual measurement experiment, and the result shows that the real-time positioning method based on the low-cost IMU and the RFID technology can meet the actual positioning requirement, the stability and the reliability are higher than those of the method only using the INS, the positioning precision is improved by 31%, and the method is suitable for indoor AGV real-time positioning.
Drawings
FIG. 1 is a flow chart of an IMU/RFID combined location.
FIG. 2 is a comparison graph before and after static acceleration compensation: (a) is the original acceleration; (b) to compensate for acceleration.
Fig. 3 is a graph of operating speed versus angular deviation.
Fig. 4 is a straight line test result.
Fig. 5 is a graph of test results.
FIG. 6 is a graph of error versus displacement.
Detailed Description
The invention is described in further detail below with reference to the figures and specific embodiments. The main content of the invention comprises two parts: firstly, a set of real-time positioning error correction method based on a low-cost IMU (Inertial Measurement Unit) is provided, the problems of large Measurement error and the like of a low-cost Inertial sensor in a test are solved, and a positioning result meets the requirement of industrial indoor positioning; and secondly, referring to a transponder method widely applied to rail transit, the RFID technology is utilized to carry out position correction on inertial navigation positioning, and the position of the AGV is calculated through the combination of data of the two methods, so that the problem of inertial navigation accumulated error is solved. The specific process is as follows:
step 1: and (3) compensating the sensor error: zero acceleration detection and error correction are performed on low cost IMU sensors, including compensation for acceleration and yaw error.
The low cost IMU has some problems in application, and the sensor has certain errors including acceleration and yaw angle before data acquisition, and the acquired data needs to be preprocessed.
The compensation for the acceleration error is specifically: and acquiring and analyzing the acceleration data in the static state to obtain acceleration compensation values of the IMU in the X direction and the Y direction, and compensating the acceleration values accordingly.
In the embodiment, data of the sensor at rest is collected by taking 200Hz as a sampling frequency, and error compensation is carried out on the result. In order to solve the problem of initial deviation of the acceleration, the acceleration data in the static state is analyzed, and the acceleration compensation value required for the IMU is-0.0253 in the X direction and 0.0137 in the Y direction. The acceleration comparison graph before and after error compensation is shown in fig. 2, experiments show that the data which is not subjected to compensation processing is seriously deviated, the acceleration value fluctuates around 0.025, and the compensated result is shown in a graph (b) and is relatively in accordance with the actual situation.
The compensation of the yaw angle error specifically comprises the following steps: the method comprises the steps of measuring linear motion of the AGV to obtain error data of angle deflection, taking the running speed of the AGV as input, training and testing the angle deflection error by using a support vector machine method to obtain a model of the running speed and the angle deviation of the AGV, and compensating the angle deviation of the AGV in real time according to different running speeds according to the model.
The zero acceleration detection aims at carrying out fuzzy processing on the collected data, and regarding the data (acceleration and deflection angle) collected in the AGV movement, if the variation of the data collected twice is smaller than a specified threshold value, the acceleration is considered to be zero or the angle is deflected to be zero. By analyzing and summarizing the experimental data, it is considered that no change occurs when the acceleration change amount is less than 0.002. The angular deflection error variation is relatively complex and will be explained below.
Experiments find that the IMU has different angular deflection errors at different operating speeds, and a series of experiments are carried out to solve the problem and provide an improved method. The method comprises the steps of measuring linear motion with the AGV running speed of 0-6000 mm/min through an experiment to obtain 60 groups of error data of angular deflection, taking the AGV running speed as input, training and testing the angular deflection error by using a Support Vector Machine (SVM) method to obtain a model of the AGV running speed and the angular deviation, and compensating the angular deviation of the AGV in real time according to different running speeds according to the model. Figure 3 shows the operating speed versus angular deflection error.
Therefore, in the positioning algorithm, different angle compensation values are dynamically adopted for AGVs with different running speeds according to the angle deviation curve, so that the positioning algorithm is more practical, and meanwhile, the positioning algorithm precision is improved.
Step 2: removing collected data noise: and carrying out noise removal on the data acquired by the IMU through a Kalman filtering pose updating algorithm to obtain the filtered AGV speed and displacement update, and obtaining the real-time position of the AGV.
Step 21: performing coordinate transformation
In the positioning model, AGV is taken as a carrier coordinate system b system, a geographic coordinate system is taken as a navigation coordinate system n system, and acceleration information in the X direction and the Y direction at the k moment of the AGV under the carrier coordinate system b system is measured by an inertial navigation system
Figure BDA0002116806450000051
And deflection angle
Figure BDA0002116806450000052
Obtaining acceleration information and angle deflection information of the AGV under a navigation coordinate system n system according to an attitude calculation method:
Figure BDA0002116806450000053
in the formula:
Figure BDA0002116806450000054
b is the acceleration information at the time k,
Figure BDA0002116806450000055
n is the acceleration information at the time k,
Figure BDA0002116806450000056
the deflection angle of the AGV at time k.
By the above conversion, the acceleration information in the b system is converted into the n system.
Step 22: pose updating based on Kalman filtering
Noise is doped in data acquired by an IMU, a real-time positioning requirement algorithm of an AGV can process generated data in real time, a commonly used filtering algorithm cannot guarantee real-time performance, and a Kalman filtering algorithm (UKF) serving as a pure time domain estimation algorithm well solves the problem.
And (3) updating the model of the pose: in an INS positioning system, the position and speed update of an AGV under n series is obtained by using information obtained by a sensor under b series, and the model is as follows:
Figure BDA0002116806450000061
in the formula: subscripts X and Y denote the X and Y directions, respectively; j is a function ofk-1Jerk at time k-1, akAnd ak-1Acceleration at time k and time k-1, v, respectively, under nkAnd vk-1The velocities at time k and k-1, s, respectivelykAnd sk-1Displacement at time k and time k-1, respectively, and △ T is the sampling interval.
UKF equation of state:
Figure BDA0002116806450000062
UKF measurement equation: zk＝HXk+Vk(4)
Where A is a state transition matrix, ukFor the system control input at time k, BkIs a state control matrix at time k, H is
Observation matrix, VkFor the observation of the noise at the time k,
Figure BDA0002116806450000063
is a system state variable at the moment of k-1It is estimated that the position of the target,
Figure BDA0002116806450000064
is a k-time system
Estimating a state variable; zkAn observed variable at time k;
state variable X at time kk＝[Sk,xSk,yvk,xvk,yak,xak,y]'
The state transition matrix is known by a pose updating model and a state equation
Figure BDA0002116806450000065
The control matrix B is not considered herek(ii) a From the measurement equation, the observation matrix H ═ 000011]；
Applying a Kalman filtering elementary equation:
Figure BDA0002116806450000066
in the formula (I), the compound is shown in the specification,
Figure BDA0002116806450000071
carrying out prior estimation on the system state at the k-1 moment;
Figure BDA0002116806450000072
estimating the state prior of the system at the moment k; pk －Estimating an error covariance matrix for the k moment prior; pkAnd Pk-1Respectively estimating an error covariance matrix at the k moment and the k-1 moment; q is a process noise covariance matrix; r is a measurement noise covariance matrix; i is an identity matrix; kkKalman gain or blending factor.
The partial calculation is realized by MAT L AB, and finally the filtered AGV speed and displacement are updated to obtain the real-time position (X, Y) of the AGV.
And step 3: and (3) performing position correction by the RFID: and the signals acquired by the RFID reader and the inertial sensing information are combined and positioned, and the position information is corrected at a specific reference node, so that the real-time accurate position information of the AGV is obtained.
According to the combined positioning method, the low-cost IMU is installed on the AGV, and the invention selects nine-axis attitude measurement sensors. And for the collected acceleration signals, obtaining the position information of the AGV through coordinate conversion calculation and an attitude calculation algorithm. Aiming at the problem of accumulated errors in the conventional inertial positioning method, signals acquired by an RFID reader and inertial sensing information are combined and positioned, and position information is corrected at a specific reference node, so that real-time accurate position information of the AGV is obtained.
In order to verify the accuracy of the error correction positioning method provided by the invention, an actual measurement experiment is carried out. In the experiment, the IMU needs to be fixedly installed on the AGV, the AGV has a track when moving, a label with a known set position in the track is used as a reference node, and a reader identifies the label to determine the current position.
In the experiment, a JY9001 attitude sensor is adopted, the voltage is 3.5V-5V, the measurement dimension is 3 dimensions of acceleration, the magnetic field is 3 dimensions, the angle is 3 dimensions, the stability acceleration is 0.01g, the angular speed is 0.05 DEG/s, the data output frequency is 0.1 Hz-200 Hz, and the data interface is a serial port.
The RFID equipment used in the experiment is an FY-2422R type passive reader-writer, and the electronic tag is a passive electronic tag corresponding to the electronic tag. IMU installs on the AGV, carries out the transmission of data through bluetooth and host computer, and signal acquisition frequency sets up to 200 Hz. This experiment is gone on in AGV place laboratory, and AGV sets for an established orbit, and what the communication adopted is the bluetooth.
Experiment and result analysis:
experiments of linear motion and arbitrary track motion are respectively carried out. And analyzing the experimental data by adopting a conventional quadratic integral calculation method and an improved error correction method. Fig. 4 shows a straight line test experiment result, and a positioning track comparison graph obtained by a conventional method and a positioning track obtained by a correction positioning algorithm is adopted under actual measurement of straight line motion, wherein the error in the X direction is 21.5% and the error in the Y direction is 5.5% in the conventional method, the error in the X direction is 4.1% in the error correction method, and the error in the Y direction is 3.4%, so that the error correction algorithm provided by the invention has more excellent performance.
Fig. 5 shows the actual measurement experiment result of any trajectory, and the experiment result shows that the initial linear motion correction positioning method is not much different from the conventional positioning method, the drift of the conventional positioning method is more serious along with the accumulation of time, the accuracy requirement is difficult to achieve after the displacement is 2.5m, but the advantage of the correction positioning algorithm is obvious. In the experiment, the error of the conventional method reaches 40%, the maximum positioning error of the correction positioning method is 9.75%, and the precision is improved by 31% compared with that of the conventional method.
Through experimental analysis, the relationship of FIG. 6 exists between the motion displacement and the experimental error under the condition of using the JY9001 attitude sensor, and a user can select a proper layout mode according to the actual positioning precision requirement on the AGV. Taking the existing AGV size as a consideration factor, if the required positioning accuracy is 0.5m, the reference nodes are arranged at intervals of 7.8 m, and if the accuracy is 1.0m, the reference nodes are arranged at intervals of 10 m. The method is used for correcting the position of the system, and the positioning error is ensured to be always within an acceptable range.
The high cost or the low positioning precision is a common problem in the current stage of AGV real-time accurate positioning in an indoor complex environment, and the combined positioning method of the low-cost IMU and the RFID technology is researched comprehensively. Aiming at the error characteristics of low-cost IMU measurement data, a real-time positioning error correction method is provided, the INS position is solved through a Kalman filtering algorithm, the INS position is combined with RFID electronic tag information, a position updating equation of combined positioning is deduced, and indoor AGV pose information is obtained. The correctness of the method is verified through an actual measurement experiment, and the result shows that the real-time positioning method based on the low-cost IMU and the RFID technology can meet the actual positioning requirement, the stability and the reliability are higher than those of the method using the INS only, the positioning precision is improved by 31%, and the method is suitable for indoor AGV real-time positioning.

Claims (1)

1. An AGV real-time positioning method based on the combination of low-cost IMU and RFID technology is characterized by comprising the following steps:
step 1: and (3) compensating the sensor error: carrying out zero acceleration detection and error correction on the low-cost IMU sensor, wherein the zero acceleration detection and the error correction comprise the compensation of the errors of the acceleration and the yaw angle;
step 2: removing collected data noise: noise removal is carried out on data acquired by the IMU through a Kalman filtering pose updating algorithm, filtered AGV speed and displacement updating are obtained, and the real-time position of the AGV is obtained;
and step 3: and (3) performing position correction by the RFID: the signals acquired by the RFID reader and the inertial sensing information are combined and positioned, and the position information is corrected at a specific reference node, so that the real-time accurate position information of the AGV is obtained;
the compensation of the acceleration error in the step 1 specifically comprises: acquiring and analyzing acceleration data in a static state to obtain acceleration compensation values of the IMU in the X direction and the Y direction, and compensating the acceleration values according to the acceleration compensation values; the compensation of the yaw angle error specifically comprises the following steps: measuring the linear motion of the AGV to obtain error data of angle deflection, taking the running speed of the AGV as input, training and testing the angle deflection error by using a support vector machine method to obtain a model of the running speed and the angle deviation of the AGV, and compensating the angle deviation of the AGV in real time according to different running speeds according to the model;
the specific process of the step 2 comprises the following steps:
step 21: performing coordinate transformation
In the positioning model, AGV is taken as a carrier coordinate system b system, a geographic coordinate system is taken as a navigation coordinate system n system, and acceleration information in the X direction and the Y direction at the k moment of the AGV under the carrier coordinate system b system is measured by an inertial navigation system
Figure FDA0002486009900000011
And deflection angle
Figure FDA0002486009900000012
Obtaining acceleration information and angle deflection information of the AGV under a navigation coordinate system n system according to an attitude calculation method:
Figure FDA0002486009900000013
in the formula:
Figure FDA0002486009900000014
b is the acceleration information at the time k,
Figure FDA0002486009900000015
n is the acceleration information at the time k,
Figure FDA0002486009900000016
the deflection angle of the AGV at the moment k is obtained;
converting the acceleration information under the b system into the n system through the transformation;
step 22: performing pose update
In an inertial navigation system, the position and speed of an AGV under an n series are updated according to information obtained by a sensor under a b series, and the model is as follows:
Figure FDA0002486009900000021
in the formula: subscripts X and Y denote the X and Y directions, respectively; j is a function ofk-1Jerk at time k-1, akAnd ak-1Acceleration at time k and time k-1, v, respectively, under nkAnd vk-1The velocities at time k and k-1, S, respectivelykAnd Sk-1Respectively displacement at the time k and the time k-1, and delta T is a sampling time interval;
UKF equation of state:
Figure FDA0002486009900000022
UKF measurement equation: zk＝HXk+Vk(4)
Where A is a state transition matrix, ukFor the system control input at time k, BkFor the state control matrix at time k, H for the observation matrix, VkFor the observation of the noise at the time k,
Figure FDA0002486009900000023
for the system state variable estimation at time k-1,
Figure FDA0002486009900000024
estimating a system state variable at the moment k; zkAn observed variable at time k;
state variable X at time kk＝[Sk,xSk,yvk,xvk,yak,xak,y]'
The state transition matrix is known by a pose updating model and a state equation
Figure FDA0002486009900000025
The control matrix B is not considered herek(ii) a From the measurement equation, the observation matrix H ═ 000011]；
Applying a Kalman filtering elementary equation:
Figure FDA0002486009900000026
in the formula (I), the compound is shown in the specification,
Figure FDA0002486009900000031
carrying out prior estimation on the system state at the k-1 moment;
Figure FDA0002486009900000032
estimating the state prior of the system at the moment k; pk －Estimating an error covariance matrix for the k moment prior; pkAnd Pk-1Posterior estimation error covariance matrixes at the k moment and the k-1 moment respectively;
q is a process noise covariance matrix; r is a measurement noise covariance matrix; i is an identity matrix; kkIs a kalman gain or a blending factor;
and finally, updating the filtered AGV speed and displacement to obtain the real-time position (X, Y) of the AGV.
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