CN110243363A - A kind of AGV real-time location method based on inexpensive IMU in conjunction with RFID technique - Google Patents
A kind of AGV real-time location method based on inexpensive IMU in conjunction with RFID technique Download PDFInfo
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- G—PHYSICS
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- 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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- H—ELECTRICITY
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- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
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Abstract
The present invention discloses a kind of AGV real-time location method based on inexpensive IMU in conjunction with RFID technique, compensates sensor error first: carrying out zero acceleration detection and error correction to inexpensive IMU sensor, compensates including the error to acceleration and angle of drift；Then removal acquires data noise: carrying out noise remove to the data that IMU is acquired by Kalman filtering pose more new algorithm, obtains filtered AGV speed and displacement updates, obtain the real time position of AGV；Last RFID carries out position correction: the signal and inertial sensing information acquired by RFID reader is combined positioning, the correction of location information is being carried out with particular reference to node, and then obtain the real-time precise position information of AGV.The present invention is low in cost, can reach actual location requirement, and relatively higher using INS stability and reliability merely, positioning accuracy improves 31%, positions in real time suitable for indoor AGV.
Description
Technical field
The present invention relates to AGV real time positioning technology field, it is specially a kind of based on inexpensive IMU in conjunction with RFID technique
AGV real-time location method.
Background technique
With the proposition of intelligence manufacture, Chinese industrial production will be further towards intelligent, flexibility and Highgrade integration
Direction develop, AGV (Automated Guided Vehicle automated guided vehicle) is as realizing material in intelligent plant
The mobile robot of automatic transportation, carrying out positioning in real time to it is the key that material precisely dispenses.
In recent years, based on the various of the technologies such as infrared ray, ultrasonic wave, bluetooth, less radio-frequency (RFID), ultra wide band (UWB)
Indoor orientation method constantly rises, and is widely used in the fields such as smart home, and positioning accuracy with higher.It is above fixed
Position technology shows that independence is weak, has the problems such as line-of-sight transmission requirement, needs to be assisted by external device device, and lead to
It crosses these hardware devices to carry out data transmission, once external environment is unable to satisfy condition, localization method also will failure.For complexity
Factory process environment, above method is difficult to realize precise positioning, and fixed based on the inertial navigation that self-sensor device is positioned
Position method becomes the key to solve the problem.
Inertial positioning is not depend on external information, without radiation energy, because its independence is good, in face of complicated indoor ring
Border has absolute predominance.But mature inertial navigation device cost is all very high on the market, if can be gone out using inexpensive sensor
The existing collected static acceleration zero bias of data are serious, and acceleration of motion noise is bigger than normal, measures deflection angle under different measuring speeds
Spend the problems such as precision is different and calculating gained position drift is serious；In addition, the position that inertial positioning method resolves AGV is used alone
It sets, obtained error can have cumulative effect at any time, and after a certain period of time, position error will exceed tolerance interval, fixed
Position will also lose meaning.
The main source of error of inertial navigation system (Inertial Navigation System, INS) is inexpensive biography
Sensor itself precision is not high, and systematic error is big, accelerometer and gyroscope is there are the error of zero and calculated result is drifted about at any time
Seriously.
Summary of the invention
In view of the above-mentioned problems, it is an object of the invention to propose a kind of AGV based on inexpensive IMU in conjunction with RFID technique
Real-time location method carries out localization method that RFID technique and inertial positioning are combined by error correction again in spy to IMU first
Determine the correction that reference mode carries out location information, and then obtains the real-time precise position information of AGV.Technical solution is as follows:
A kind of AGV real-time location method based on inexpensive IMU in conjunction with RFID technique, comprising the following steps:
Step 1: compensation sensor error: zero acceleration detection and error correction being carried out to inexpensive IMU sensor, including right
The error of acceleration and angle of drift compensates；
Step 2: removal acquisition data noise: the data that IMU is acquired being carried out by Kalman filtering pose more new algorithm
Noise remove, obtains filtered AGV speed and displacement updates, and obtains the real time position of AGV；
Step 3:RFID carries out position correction: being combined by the signal that RFID reader acquires with inertial sensing information
Positioning is carrying out the correction of location information with particular reference to node, and then is obtaining the real-time precise position information of AGV.
Further, the compensation in the step 1 to acceleration error specifically: to it is static when acceleration information carry out
Acquisition and analysis, obtain the acceleration compensation value in the x direction and the y direction to IMU, compensate accordingly to acceleration value；To boat
The compensation of declination error specifically: the linear motion of AGV is measured, the error information of angular deflection is obtained, AGV is run
Speed as input, angle offset error is trained and is tested using support vector machine method, obtain the AGV speed of service with
The model of angular deviation carries out real-time compensation according to the different speeds of service to the angular deviation of AGV according to model.
Further, the detailed process of the step 2 includes:
Step 21: being coordinately transformed
In location model, using AGV as carrier coordinate system b system, using geographic coordinate system as navigational coordinate system n system, inertia is utilized
The X-direction and Y-direction acceleration information at AGV k moment under carrier coordinate system b system that navigation system measuresWith it is inclined
CornerAcceleration information and angular deflection information of the AGV under navigational coordinate system n system are obtained according to attitude algorithm method:
In formula:For the acceleration information at b system lower k moment,For the acceleration information at n system lower k moment,Exist for AGV
The deflection angle at k moment；
By the way that with up conversion, the acceleration information under b system is transformed under n system；
Step 22: carrying out pose update
In inertial navigation system, the position and speed of AGV under n system is obtained more by the information that the sensor under b system obtains
Newly, model are as follows:
In formula: subscript x and y respectively indicate X-direction and Y-direction；jk-1For the acceleration at k-1 moment, akAnd ak-1Respectively
For the acceleration at n system lower k moment and k-1 moment, vkAnd vk-1It is the speed at k moment and k-1 moment, s respectivelykAnd sk-1Respectively k
The displacement at moment and k-1 moment, △ T are sampling time interval；
UKF state equation:
UKF measurement equation: Zk=HXk+Vk (4)
Wherein, A is state transition matrix, ukEtching system control input, B when for kkMatrix is controlled for k moment state, H is
Observing matrix, VkFor k moment observation noise,Estimate for k-1 moment system state variables,It is for the k moment
System
State variable estimation；ZkFor k moment observational variable；
K moment state variable Xk=[Sk,x Sk,y vk,x vk,y ak,x ak,y]'
Known by pose more new model and state equation, state-transition matrix
Do not consider to control matrix B hereink；Known by measurement equation, observing matrix H=[0 0001 1]；
Using Kalman filtering fundamental equation:
In formula,For k-1 moment system mode prior estimate；For k moment system mode prior estimate；Pk ?When for k
Carve prior estimate error covariance matrix；PkAnd Pk-1K moment and k-1 moment Posterior estimator error co-variance matrix respectively；Q is
Process noise covariance matrix；R is measurement noise covariance matrix；I is unit matrix；KkFor kalman gain or mixing because
Number.It finally obtains filtered AGV speed and displacement updates, obtain the real time position (X, Y) of AGV.
The beneficial effects of the present invention are: the present invention is ground for the combined positioning method of low cost IMU and RFID technique
Study carefully, for the error character of inexpensive IMU measurement data, proposes real-time position error modification method, calculated by Kalman filtering
Method resolves INS position, and is combined with RFID electronic label information, derives the location updating side of integrated positioning
Journey obtains interior AGV posture information；And pass through the correctness of actual measurement experimental verification above method, the results showed that, based on low cost
IMU and the real-time location method of RFID technique can reach actual location requirement, relatively use INS stability and reliability more merely
Height, positioning accuracy improve 31%, position in real time suitable for indoor AGV.
Detailed description of the invention
Fig. 1 is IMU/RFID integrated positioning flow chart.
Fig. 2 is comparison diagram before and after static acceleration compensation: (a) being original acceleration；It (b) is compensated acceleration.
Fig. 3 is the speed of service and angular deviation curve.
Fig. 4 is straight line test result.
Fig. 5 is curve test result.
Fig. 6 is error and displacement curve.
Specific embodiment
The present invention is described in further details in the following with reference to the drawings and specific embodiments.Main contents of the invention include
Two parts: first is that proposing a set of reality based on low cost IMU (Inertial Measurement Unit Inertial Measurement Unit)
When position error modification method expire positioning result the problems such as alleviating inexpensive inertial sensor in test measurement error is big
The requirement of foot industry indoor positioning；Second is that widely applied transponder method in reference orbit traffic, using RFID technique to used
Property navigator fix carry out position correction, the position AGV is resolved by the combinations of two methods data, solves inertial navigation cumulative errors
The problem of.Detailed process is as follows:
Step 1: compensation sensor error: zero acceleration detection and error correction being carried out to inexpensive IMU sensor, including right
The error of acceleration and angle of drift compensates.
There are some problems in inexpensive IMU, in acquisition data forwarding sensor, there is a certain error, including adds in the application
Speed and yaw angle need to pre-process acquisition data.
Compensation to acceleration error specifically: to it is static when acceleration information be acquired and analyze, obtain to IMU
Acceleration compensation value in the x direction and the y direction, accordingly compensates acceleration value.
The present embodiment is acquired data of sensor when static using 200Hz as sample frequency, and carries out to its result
The compensation of error.For the initial offset for solving the problems, such as acceleration, to it is static when acceleration information analyze, learn needs pair
The acceleration compensation value of IMU is X-direction -0.0253, Y-direction 0.0137.Acceleration comparison diagram such as Fig. 2 institute before and after error compensation
Show, the data that experiment discovery does not compensate processing seriously deviate, and acceleration value fluctuates near 0.025, compensated result
Such as figure (b), it is more conform with actual conditions.
Compensation to course deviation angle error specifically: the linear motion of AGV is measured, the margin of error of angular deflection is obtained
According to, using the AGV speed of service as input, angle offset error is trained and is tested using support vector machine method, is obtained
The model of the AGV speed of service and angular deviation mends the angular deviation of AGV according to the different speeds of service according to model in real time
It repays.
The purpose of zero acceleration detection is to carry out Fuzzy Processing to the data of acquisition, (is added for collecting data in AGV is moved
Speed and deflection angle), if the data variation amount acquired twice is less than specified threshold, then it is assumed that its acceleration is zero or angle is inclined
Switch to zero.By being analyzed and summarized to experimental data, it is believed that think when acceleration change amount is less than 0.002 its there is no
Variation.Angular deflection error change is more complicated, is described below.
It was found that, angular deflection error of the IMU under the different speeds of service is different, done for this problem
A series of experiments simultaneously proposes improved method.It tests and the linear motion that the AGV speed of service is 0~6000mm/min is measured,
The error information of 60 groups of angular deflection is obtained, using the AGV speed of service as input, utilizes support vector machines (Support
Vector Machine, SVM) method is trained and tests to angle offset error, obtain the AGV speed of service and angular deviation
Model, according to model can angular deviation to AGV according to the different speeds of service carry out real-time compensation.Fig. 3 gives operation speed
The relation curve of degree and angular deflection error.
Therefore, in location algorithm, the AGV of the different speeds of service is needed dynamically to take not according to angular deviation curve
Same angle compensation value, makes location algorithm be more in line with reality, while improving location algorithm precision.
Step 2: removal acquisition data noise: the data that IMU is acquired being carried out by Kalman filtering pose more new algorithm
Noise remove, obtains filtered AGV speed and displacement updates, and obtains the real time position of AGV.
Step 21: being coordinately transformed
In location model, using AGV as carrier coordinate system b system, using geographic coordinate system as navigational coordinate system n system, inertia is utilized
The X-direction and Y-direction acceleration information at AGV k moment under carrier coordinate system b system that navigation system measuresAnd deflection
AngleAcceleration information and angular deflection information of the AGV under navigational coordinate system n system are obtained according to attitude algorithm method:
In formula:For the acceleration information at b system lower k moment,For the acceleration information at n system lower k moment,Exist for AGV
The deflection angle at k moment.
By the way that with up conversion, the acceleration information under b system is transformed under n system.
Step 22: pose update is carried out based on Kalman filtering
The collected data of IMU adulterate noise, to the real-time positioning requirements algorithm of AGV can in real time to the data of generation into
Row processing, it is common to use filtering algorithm real-time not can guarantee, Kalman filtering algorithm (Unscented Kalman
Filter, UKF) a kind of algorithm for estimating very good solution this problem as pure time domain.
Pose more new model: in INS positioning system, AGV under n system is obtained by the information that the sensor under b system obtains
Position and speed updates, model are as follows:
In formula: subscript x and y respectively indicate X-direction and Y-direction；jk-1For the acceleration at k-1 moment, akAnd ak-1Respectively
For the acceleration at n system lower k moment and k-1 moment, vkAnd vk-1It is the speed at k moment and k-1 moment, s respectivelykAnd sk-1Respectively k
The displacement at moment and k-1 moment, △ T are sampling time interval.
UKF state equation:
UKF measurement equation: Zk=HXk+Vk (4)
Wherein, A is state transition matrix, ukEtching system control input, B when for kkMatrix is controlled for k moment state, H is
Observing matrix, VkFor k moment observation noise,Estimate for k-1 moment system state variables,It is for the k moment
System
State variable estimation；ZkFor k moment observational variable；
K moment state variable Xk=[Sk,x Sk,y vk,x vk,y ak,x ak,y]'
Known by pose more new model and state equation, state-transition matrix
Do not consider to control matrix B hereink；Known by measurement equation, observing matrix H=[0 0001 1]；
Using Kalman filtering fundamental equation:
In formula,For k-1 moment system mode prior estimate；For k moment system mode prior estimate；Pk ?When for k
Carve prior estimate error covariance matrix；PkAnd Pk-1K moment and k-1 moment Posterior estimator error co-variance matrix respectively；Q is
Process noise covariance matrix；R is measurement noise covariance matrix；I is unit matrix；KkFor kalman gain or mixing because
Number.
The part calculates and is realized by MATLAB, finally obtains filtered AGV speed and displacement updates, obtain the reality of AGV
When position (X, Y).
Step 3:RFID carries out position correction: being combined by the signal that RFID reader acquires with inertial sensing information
Positioning is carrying out the correction of location information with particular reference to node, and then is obtaining the real-time precise position information of AGV.
According to combination of the above localization method, low cost IMU is installed on AGV, the present invention selects nine axis attitude measurements sensing
Device.For the acceleration signal of acquisition, is calculated by coordinate conversion and attitude algorithm algorithm obtains the location information of AGV.For
There is accumulated error in conventional inertial positioning method, the signal and inertial sensing information acquired by RFID reader carries out group
Positioning is closed, the correction of location information is being carried out with particular reference to node, and then obtain the real-time precise position information of AGV.
In order to verify the accuracy of error correction localization method proposed by the present invention, actual measurement experiment has been carried out.In this experiment,
IMU need to be fixedly mounted on AGV, and AGV has track when moving, and label known to setting position is as reference node in track
Point, reader go identification label to determine current location.
In an experiment, using JY9001 attitude transducer, voltage 3.5V~5V measures dimension as the dimension of acceleration 3, magnetic field 3
Dimension, angle 3 are tieed up, and stability acceleration is 0.01g, angular speed 0.05 °/s, data output frequencies 0.1Hz~200Hz, and data connect
Mouth is serial ports.
RFID device used in experiment is the passive reader of FY-2422R type, and electronic tag is corresponding passive
Electronic tag.IMU is mounted on AGV, and the transmission of data is carried out by bluetooth and host computer, and signal acquisition frequency is set as 200Hz.
This experiment carries out in the lab in AGV, and AGV sets a set track, communicates using bluetooth.
Experiment and interpretation of result:
The experiment of linear motion and arbitrary trajectory movement has been done respectively.Using conventional quadratic integral calculation method and improvement
Error correcting method afterwards analyzes experimental data.Fig. 4 give straight line test experiments as a result, actual measurement linear motion under,
Using conventional method and the positioning track comparison diagram obtained using amendment location algorithm, conventional method X-direction error 21.5%, Y
Deflection error 5.5%, error correcting method X-direction error 4.1%, Y-direction error 3.4%, therefore error proposed by the present invention
Correction algorithm performance is more excellent.
Fig. 5 gives the actual measurement experimental result of arbitrary trajectory, the experimental results showed that, the linear motion of beginning corrects positioning side
Method and conventional localization method difference are little, and with the accumulation of time, the drift of conventional localization method is more serious, is displaced 2.5m
Afterwards, it has been difficult to reach required precision, but has corrected the with the obvious advantage of location algorithm.In this experiment, conventional method error reaches
40%, localization method position error maximum 9.75% is corrected, precision improves 31% compared with conventional method.
It is learnt by experimental analysis, under the conditions of using JY9001 attitude transducer, between moving displacement and experimental error
There are Fig. 6 relationship, user can select suitable layout type according to the actual location required precision to AGV.With existing AGV ruler
Very little is Consideration, if it is desired to which positioning accuracy is 0.5m, then reference mode is primary every 7.8 meters of arrangements, if precision is 1.0m,
Reference mode is primary every 10m arrangement.System position correction is carried out using above method, guarantees position error always acceptable
In range.
Low expensive or positioning accuracy is that AGV real-time and precise positions generally existing at this stage ask under indoor complex environment
Topic, comprehensively considers, and the present invention is studied for the combined positioning method of low cost IMU and RFID technique.For inexpensive IMU
The error character of measurement data, proposes real-time position error modification method, is solved by Kalman filtering algorithm to INS position
It calculates, and is combined with RFID electronic label information, derive the location updating equation of integrated positioning, obtain interior AGV pose
Information.Pass through the correctness of actual measurement experimental verification above method, the results showed that, it is real-time based on inexpensive IMU and RFID technique
Localization method can reach actual location requirement, and relatively higher using INS stability and reliability merely, positioning accuracy improves
31%, it is positioned in real time suitable for indoor AGV.
Claims (3)
1. a kind of AGV real-time location method based on inexpensive IMU in conjunction with RFID technique, which is characterized in that including following step
It is rapid:
Step 1: compensation sensor error: zero acceleration detection and error correction being carried out to inexpensive IMU sensor, including to acceleration
The error of degree and angle of drift compensates；
Step 2: noise removal acquisition data noise: being carried out to the data that IMU is acquired by Kalman filtering pose more new algorithm
Removal, obtains filtered AGV speed and displacement updates, and obtains the real time position of AGV；
Step 3:RFID carries out position correction: it is fixed that the signal and inertial sensing information acquired by RFID reader is combined
Position is carrying out the correction of location information with particular reference to node, and then is obtaining the real-time precise position information of AGV.
2. the AGV real-time location method according to claim 1 based on inexpensive IMU in conjunction with RFID technique, feature
Be, to the compensation of acceleration error in the step 1 specifically: to it is static when acceleration information be acquired and analyze,
The acceleration compensation value in the x direction and the y direction to IMU is obtained, acceleration value is compensated accordingly；To the benefit of course deviation angle error
It repays specifically: the linear motion of AGV is measured, the error information of angular deflection is obtained, using the AGV speed of service as defeated
Enter, angle offset error is trained and is tested using support vector machine method, obtains the AGV speed of service and angular deviation
Model carries out real-time compensation according to the different speeds of service to the angular deviation of AGV according to model.
3. the AGV real-time location method according to claim 1 based on inexpensive IMU in conjunction with RFID technique, feature
It is, the detailed process of the step 2 includes:
Step 21: being coordinately transformed
In location model, using AGV as carrier coordinate system b system, using geographic coordinate system as navigational coordinate system n system, inertial navigation is utilized
The X-direction and Y-direction acceleration information at the AGV of system measurements k moment under carrier coordinate system b systemAnd deflection angleAcceleration information and angular deflection information of the AGV under navigational coordinate system n system are obtained according to attitude algorithm method:
In formula:For the acceleration information at b system lower k moment,For the acceleration information at n system lower k moment,It is AGV in k
The deflection angle at quarter；
By the way that with up conversion, the acceleration information under b system is transformed under n system；
Step 22: carrying out pose update
In inertial navigation system, updated by the position and speed that the information that the sensor under b system obtains obtains AGV under n system,
Model are as follows:
In formula: subscript x and y respectively indicate X-direction and Y-direction；jk-1For the acceleration at k-1 moment, akAnd ak-1Respectively n system
The acceleration at lower k moment and k-1 moment, vkAnd vk-1It is the speed at k moment and k-1 moment, s respectivelykAnd sk-1The respectively k moment
With the displacement at k-1 moment, △ T is sampling time interval；
UKF state equation:
UKF measurement equation: Zk=HXk+Vk (4)
Wherein, A is state transition matrix, ukEtching system control input, B when for kkMatrix is controlled for k moment state, H is observation square
Battle array, VkFor k moment observation noise,Estimate for k-1 moment system state variables,For the estimation of k moment system state variables；
ZkFor k moment observational variable；
K moment state variable Xk=[Sk,x Sk,y vk,x vk,y ak,x ak,y]'
Known by pose more new model and state equation, state-transition matrix
Do not consider to control matrix B hereink；Known by measurement equation, observing matrix H=[0 0001 1]；
Using Kalman filtering fundamental equation:
In formula,For k-1 moment system mode prior estimate；For k moment system mode prior estimate；Pk ?For k moment elder generation
Test evaluated error covariance matrix；PkAnd Pk-1Respectively k moment and k-1 moment Posterior estimator error co-variance matrix；Q was
Journey noise covariance matrix；R is measurement noise covariance matrix；I is unit matrix；KkFor kalman gain or mixing factor；
It finally obtains filtered AGV speed and displacement updates, obtain the real time position (X, Y) of AGV.
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| CN112068099A (en) * | 2020-07-31 | 2020-12-11 | 西安电子科技大学 | Multi-radiation source rapid positioning speed measurement method and device based on error compensation |
| CN112362052A (en) * | 2020-10-27 | 2021-02-12 | 中国科学院计算技术研究所 | Fusion positioning method and system |
| CN114608572A (en) * | 2022-03-25 | 2022-06-10 | 高斯机器人(深圳)有限公司 | Method for realizing AGV indoor positioning by combining UWB and IMU |
| CN114608572B (en) * | 2022-03-25 | 2024-07-05 | 高斯机器人(深圳)有限公司 | Method for realizing AGV indoor positioning by combining UWB and IMU |
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