CN109341529A - Linear motor rotor location measurement method based on machine vision - Google Patents
Linear motor rotor location measurement method based on machine vision Download PDFInfo
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- CN109341529A CN109341529A CN201811238077.7A CN201811238077A CN109341529A CN 109341529 A CN109341529 A CN 109341529A CN 201811238077 A CN201811238077 A CN 201811238077A CN 109341529 A CN109341529 A CN 109341529A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
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Abstract
The invention discloses a kind of linear motor rotor location measurement method based on machine vision, include: preparation process: aperiodic fence image being carved on linear motor stator electric along the linear motor rotor direction of motion as target image, high speed camera is mounted on linear motor rotor；Acquisition step: the fence image of linear motor rotor is acquired in real time by high speed camera；It calculates step: calculating the phase correlation function of adjacent fence image using phase related algorithm；Fit procedure: according to the peak Distribution characteristic of phase relevant frequency spectrum, the sub-pixel displacement value of adjacent fence image is obtained using bilinear fit function；Position acquisition step: in conjunction with system calibrating coefficient, the exact position of linear motor rotor is calculated.The present invention carries out multimodal fitting to relevant peaks and surrounding peak value by analysis, obtains the horizontal pixel displacement value Δ x between adjacent fence image, pixel displacement value and mover actual displacement value are demarcated, and then realizes the precise displacement of linear motor rotor position.
Description
Technical field
The present invention relates to linear motor rotor field of measuring technique more particularly to a kind of linear motors based on machine vision
Rotor position measurement method.
Background technique
Linear servo system has the characteristics that high-precision, high acceleration, big stroke obtain it in super hot investment casting industry
It is more and more widely used.The detection accuracy of linear motor rotor position directly affects the control precision of motor, and then determines
The accuracy of manufacture of product.Therefore, studying a kind of high-precision linear motor rotor location measurement method has Precision Machining
Important directive significance.
Traditional linear motor rotor position measurement mainly uses some sensors, such as magnetic grid, grating, laser interferometer
Deng being used for position measurement, example is more and technology maturation, but respectively has shortcoming.Vision measuring method is to develop in recent years
Come a kind of method for detecting position, have many advantages, such as in high precision, it is non-contact and affected by environment small, this method is introduced into directly
In the measurement of line electric mover position, a kind of new thinking is provided for rotor position detection.The essence of vision measuring method is image
Matching, common matching algorithm mainly divide two classes: (1) airspace measurement method: such as pixel recursive method (pixels-recursive
Algorithm, PRA), easily meet requirement of real-time, but to noise-sensitive, and calculating error is larger when offset is big；(2) frequency
Domain measurement method: as based on Fourier-Mellin and phase related algorithm (phase correlation algorithm,
PCA method), they are all based on the frequency-domain correlation of Fourier transformation, are detected using frequency domain information, to image
The variation of the light of grey scale change and environment is insensitive, has strong anti-interference ability, and detection range is larger, but measurement accuracy
It is other that whole Pixel-level can only be reached, measurement other to sub-pixel has certain error.Therefore, a kind of matching of real-time high-precision
Algorithm measures linear motor rotor position most important.
Summary of the invention
The technical problem to be solved by the embodiment of the invention is that providing a kind of linear motor rotor based on machine vision
Location measurement method, so as to improve the measurement accuracy of mover.
In order to solve the above-mentioned technical problem, the embodiment of the present invention proposes a kind of linear motor rotor based on machine vision
Location measurement method, comprising:
Preparation process: using aperiodic fence image as target image, aperiodic fence image is motor-driven along straight-line electric
The sub- direction of motion is carved on linear motor stator electric, and high speed camera is mounted on linear motor rotor；
Acquisition step: the fence image f before and after the displacement of linear motor rotor is acquired in real time by high speed camera1And f2；
It calculates step: calculating the fence image f of displacement front and back using phase related algorithm1And f2Two-dimentional unit pulse letter
Number, obtains f by the offset of the main peak of unit impulse function1And f2Between whole pixel displacement value；
Fit procedure: according to the peak Distribution characteristic of phase relevant frequency spectrum, adjacent fence diagrams are obtained using bilinear fit function
The sub-pixel displacement value of picture；
Position acquisition step: according to whole pixel displacement value and sub-pixel displacement value and system calibrating coefficient is combined, is calculated
To the exact position of linear motor rotor.
The embodiment of the present invention is by proposing a kind of linear motor rotor location measurement method based on machine vision, by dividing
Analysis carries out multimodal fitting to relevant peaks and surrounding peak value, the horizontal pixel displacement value Δ x between adjacent fence image is obtained, by picture
Plain shift value is demarcated with mover actual displacement value, and then realizes the precise displacement of linear motor rotor position.
Detailed description of the invention
Fig. 1 is the flow chart of the linear motor rotor location measurement method based on machine vision of the embodiment of the present invention.
Fig. 2 is the impulse function spectrogram of the different shift values of the embodiment of the present invention.
Fig. 3 is the phase relevant frequency spectrum figure of the different shift values of the embodiment of the present invention.
Fig. 4 is 4 kinds of position distribution situation maps of the peak value of the relevant peaks of the embodiment of the present invention.
Specific embodiment
It should be noted that in the absence of conflict, the features in the embodiments and the embodiments of the present application can phase
It mutually combines, invention is further described in detail in the following with reference to the drawings and specific embodiments.
If directional instruction (such as up, down, left, right, before and after ...) is only used for explaining at certain in the embodiment of the present invention
Relative positional relationship, motion conditions etc. under one particular pose (as shown in the picture) between each component, if the particular pose is sent out
When raw change, then directionality instruction also correspondingly changes correspondingly.
If in addition, the description for being related to " first ", " second " etc. in the present invention is used for description purposes only, and should not be understood as
Its relative importance of indication or suggestion or the quantity for implicitly indicating indicated technical characteristic.Define as a result, " first ",
The feature of " second " can explicitly or implicitly include at least one of the features.
Fig. 1 is please referred to, the linear motor rotor location measurement method based on machine vision of the embodiment of the present invention includes standard
Standby step~position acquisition step.
Preparation process: using aperiodic fence image as target image (when it is implemented, can be used in the prior art
The building method of middle fence image in linear motor rotor position precision measurement method based on image entropy, filters out anti-interference
Property strong aperiodic fence image as target image), by aperiodic fence image along linear motor rotor direction of motion engraving
On linear motor stator electric, high speed camera is mounted on linear motor rotor.When linear motor movement, if high speed camera is adopted
The displacement RELATED APPLICATIONS image and target image of collection are respectively f1And f2, then there are coordinate conversion relations between them:
f2=f1(w(x,y))；
Wherein, w indicates f1And f2Coordinate transform on two-dimensional space, the essence of detection are exactly to find optimal transformation ginseng
Number, then according to similarity transformation model:
Wherein, (x, y) and (x', y') is respectively f1And f2In pixel coordinate；γ is zoom factor；Δ θ is rotation angle
Degree；(Δ x, Δ y) are displacement.Due to the upper movement only in horizontal direction of ideal line motor, there is no scalings, rotation, therefore
Here γ is 1, and Δ θ is 0, then model simplification are as follows:
Acquisition step: the fence image f before and after the displacement of linear motor rotor is acquired in real time by high speed camera1And f2。
It calculates step: calculating the fence image f of displacement front and back using phase related algorithm1And f2Two-dimentional unit pulse letter
Number, obtains f by the offset of the main peak of unit impulse function1And f2Between whole pixel displacement value.Phase related algorithm detects mesh
Marker displacement is the similarity and spatial domain translation obtained between image using frequency domain fourier power spectrum.
Fit procedure: according to peak Distribution characteristic (the peak Distribution characteristic of phase correlation function: one of phase relevant frequency spectrum
Usually there are some irrelevant peaks to be just distributed in around it very much with zero-mean around a relevant peaks, therefore when displacement is sub-pix,
Displacement appears between the multimodal of phase relevant frequency spectrum), the sub-pix of adjacent fence image is obtained using bilinear fit function
Shift value.
Position acquisition step: according to whole pixel displacement value and sub-pixel displacement value and system calibrating coefficient is combined, is calculated
To the exact position of linear motor rotor.
15000 frames/s high speed camera can be selected in the embodiment of the present invention, is transported using high speed camera acquisition linear motor rotor
The fence striped of dynamic front and back, is calculated linear motor rotor actual displacement.
As an implementation, the calculating step includes:
Pre-process sub-step: the image f using peaceful (Hanning (the flap-top)) window function of the Chinese to acquisition1And f2Added
Window function pretreatment, inhibits spectral leakage when Fourier transformation, improves the precision and anti-interference of measurement.Wherein, Hanning window
The expression formula of function are as follows:
Γ (m, n)=k × 0.5 (1-cos (2 π (m/M))) × 0.5 (1-cos (2 π (n/N)))；
Wherein, M, N are the size of image, and k is structure factor.The embodiment of the present invention is by selecting different structure factors to seek
An optimal window function is looked for, spectrum confusion can be preferably inhibited, is more advantageous to subsequent calculating；When k is 1, flap-top
Window function is exactly Hanning window function, therefore the application range of flap-top window function is wider.
As an implementation, the phase related algorithm includes:
If reference picture f1Target image f is obtained after (x, y) displacement (△ x, △ y)2(x, y), then:
f2(x, y)=f1(x+Δx,y+Δy)；
According to the translation feature of Fourier transformation, Fourier transformation is carried out to above formula and is obtained:
F2(u, v)=F1(u,v)exp[j2π(uΔx+vΔy)]；
The normalization crosspower spectrum of two width fence images of translation front and back is calculated according to the following formula:
Wherein, F1 *(u, v) is F1The complex conjugate of (u, v)；F2(u, v) and F1(u, v) is respectively f2(x, y) and f1(x,y)
Fourier transformation；△ x and △ y respectively indicate the pixel displacement value on both horizontally and vertically；Above formula is inverse by Fourier
Transformation can obtain:
Q (x, y)=F-1(exp [2 π j (u Δ x+v Δ y)])=δ (x+ Δ x, y+ Δ y)；
Two-dimentional unit impulse function δ (x+ Δ x, y+ Δ y), the then offset of impulse function δ of translation are calculated by above formula
Position is whole pixel displacement value.The deviation post of impulse function δ is the displacement (△ x, △ y) between image, ideally
The peak value of impulse function δ should be 1, but since noise, light and the image sampling etc. in actual environment interfere, often make pulse
The peak value of function is less than 1, and the size of peak value reflects the degree of relevancy of two width fence images, at the same time as anti-interference
Characterization；When (△ x, △ y) is integer, then 2-D impulse function δ will form an apparent peak value, and peak coordinate indicates figure
Pixel displacement value as between；When (△ x, △ y) is not integer, then peak energy q can be spread to adjacent pixel around, in main peak
Surrounding forms many small burrs, reduces the precision of translation parameters estimation.Fig. 2 is the two images that will have different displacements
It carries out phase related algorithm and calculates the impulse function spectrogram obtained.From figure 2, it is seen that when translational movement (△ x, △ y) is integer
When, (for the peak value of x+ Δ x, y+ Δ y) closer to 1, displacement detecting result is more accurate by impulse function δ；When translational movement (△ x, △ y) is
When sub-pix, the actual measurement peak value of impulse function δ is only nearest solution, and there are many irrelevant peaks, estimation essences around main peak
Degree reduces.Therefore, the embodiment of the present invention can measure whole pixel displacement amount.
The phase related algorithm principle of the embodiment of the present invention is derived from the translation feature of Fourier transformation, to noise and environment
Light variation has stronger patience, is a kind of image measurement algorithm of strong interference immunity.
As an implementation, sub-pixel displacement value (△ x, △ y) is calculated using following formula in the fit procedure:
Wherein, C1For the peak value of main peak in highest 4 relevant peaks of phase relevant frequency spectrum, C2、C3、C4Respectively highest
4 relevant peaks in 3 secondary peaks peak value, C1、C2、C3、C4Corresponding position is respectively (x1,y1)、(x2,y2)、(x3,y3)、
(x4,y4)；xi=x 'i-x1(i=2,3,4)；y′i=yi-y1(i=2,3,4)；w1And w2It represents the 4th relevant peaks and acts on water
The weight of offset, calculation formula are as follows in gentle vertical direction:
When the embodiment of the present invention carries out airspace displacement detecting using correlation function, the Two-dimensional Pulsed function δ of generation usually exists
Offset corresponds to an apparent relevant peaks, and surrounding will appear with the irrelevant peaks of zero-mean normal distribution.Pass through what will be obtained
The peak value of impulse function spectrogram carries out partial enlargement, as shown in figure 3, impulse function δ is in unimodal when displacement is whole pixel
Shape；When displacement is sub-pix, impulse function δ shows as subsidiary several secondary peaks around multimodal shape or tomography shape or main peak；
It can determine that actual pixel displacement value centainly not on any one peak coordinate, but is located at several highest peak values simultaneously
Between.According to phase correlation peak characteristic distributions, when sub-pix occurs in displacement, shift value falls in the more of phase relevant frequency spectrum
Between peak.If highest 4 peaks are relevant peaks, wherein top is main peak, peak C1；The peak value of other 3 secondary peaks is respectively
C2, C3, C4, positional relationship is according to the difference of sub-pixel displacement value, and there are 4 kinds of situations as shown in Figure 4, actual shift values
(△ x, △ y) is located between 4 peak values.The two dimension that the embodiment of the present invention obtains the two images of displacement front and back by calculating step
Unit impulse function passes through the main peak C of unit impulse function1Offset obtain whole pixel displacement value (dx, dy), then basis
The position distribution of 4 relevant peaks is calculated the sub-pix offset (△ x, △ y) between image, therefore finally calculates adjacent
Shift value between image is (dx+ △ x, dy+ △ y).
As an implementation, system calibrating coefficient ε=1.248 in the position acquisition step, are calculated by following formula
To linear motor rotor shift value s:
S=ε (dx+ △ x)；
Wherein, dx is the whole pixel displacement value in horizontal direction, and △ x is the sub-pix offset in horizontal direction.
The result that the displacement that the embodiment of the present invention detects linear motor rotor is more stable accurately, error is smaller, measures is more
Close to true value, and there is stronger robustness, solves the problems, such as that linear motor rotor location drawing picture measurement accuracy is poor.
It although an embodiment of the present invention has been shown and described, for the ordinary skill in the art, can be with
A variety of variations, modification, replacement can be carried out to these embodiments without departing from the principles and spirit of the present invention by understanding
And modification, the scope of the present invention are defined by the appended claims and their equivalents.
Claims (5)
1. a kind of linear motor rotor location measurement method based on machine vision characterized by comprising
Preparation process: using aperiodic fence image as target image, aperiodic fence image is transported along linear motor rotor
Dynamic direction is carved on linear motor stator electric, and high speed camera is mounted on linear motor rotor；
Acquisition step: the fence image f before and after the displacement of linear motor rotor is acquired in real time by high speed camera1And f2；
It calculates step: calculating the fence image f of displacement front and back using phase related algorithm1And f2Two-dimentional unit impulse function, lead to
The offset for crossing the main peak of unit impulse function obtains f1And f2Between whole pixel displacement value；
Fit procedure: according to the peak Distribution characteristic of phase relevant frequency spectrum, adjacent fence image is obtained using bilinear fit function
Sub-pixel displacement value；
Position acquisition step: according to whole pixel displacement value and sub-pixel displacement value and system calibrating coefficient is combined, is calculated straight
The exact position of line electric mover.
2. the linear motor rotor location measurement method based on machine vision as described in claim 1, which is characterized in that described
Calculating step includes:
Pre-process sub-step: using Hanning window function to the image f of acquisition1And f2It is pre-processed, inhibits spectral leakage, it is described
The expression formula of window function are as follows:
Γ (m, n)=k × 0.5 (1-cos (2 π (m/M))) × 0.5 (1-cos (2 π (n/N)))；
Wherein, M, N are the size of image, and k is structure factor.
3. the linear motor rotor location measurement method based on machine vision as described in claim 1, which is characterized in that described
Phase related algorithm includes:
If reference picture f1Target image f is obtained after (x, y) displacement (△ x, △ y)2(x, y), then:
f2(x, y)=f1(x+Δx,y+Δy)；
According to the translation feature of Fourier transformation, Fourier transformation is carried out to above formula and is obtained:
F2(u, v)=F1(u,v)exp[j2π(uΔx+vΔy)]；
The normalization crosspower spectrum of two width fence images of translation front and back is calculated according to the following formula:
Wherein, F1 *(u, v) is F1The complex conjugate of (u, v)；F2(u, v) and F1(u, v) is respectively f2(x, y) and f1Fu of (x, y)
In leaf transformation；△ x and △ y respectively indicate the pixel displacement value on both horizontally and vertically；Above formula is passed through into inverse Fourier transform
It can obtain:
Q (x, y)=F-1(exp [2 π j (u Δ x+v Δ y)])=δ (x+ Δ x, y+ Δ y)；
Two-dimentional unit impulse function δ (x+ Δ x, y+ Δ y), the then deviation post of impulse function δ of translation are calculated by above formula
For whole pixel displacement value.
4. the linear motor rotor location measurement method based on machine vision as described in claim 1, which is characterized in that described
Sub-pixel displacement value (△ x, △ y) is calculated using following formula in fit procedure:
Wherein, C1For the peak value of main peak in highest 4 relevant peaks of phase relevant frequency spectrum, C2、C3、C4It is 4 respectively highest
The peak value of 3 secondary peaks, C in relevant peaks1、C2、C3、C4Corresponding position is respectively (x1,y1)、(x2,y2)、(x3,y3)、(x4,y4)；
x′i=xi-x1(i=2,3,4)；y′i=yi-y1(i=2,3,4)；w1And w2Represent the 4th relevant peaks act on it is horizontal and vertical
The weight of offset, calculation formula are as follows on direction:
5. the linear motor rotor location measurement method based on machine vision as described in claim 1, which is characterized in that described
Linear motor rotor shift value s is calculated by following formula in system calibrating coefficient ε=1.248 in position acquisition step:
S=ε (dx+ △ x)；
Wherein, dx is the whole pixel displacement value in horizontal direction, and △ x is the sub-pix offset in horizontal direction.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113658260A (en) * | 2021-07-12 | 2021-11-16 | 南方科技大学 | Robot pose calculation method and system, robot and storage medium |
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| CN207923058U (en) * | 2018-03-27 | 2018-09-28 | 安徽大学 | A kind of linear motor rotor position detection platform based on machine vision |
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