CN107462173A - Micromotion platform displacement measurement method and system based on micro-vision - Google Patents

Abstract

The invention discloses a method and system for measuring the displacement of a micro-moving platform based on micro-vision. The steps include: image sequence acquisition: a group of image sequences are collected through a micro-vision system; coarse matching position acquisition: using an improved particle swarm optimization algorithm to rapidly search in the entire search domain Search to obtain the rough matching position of the image block; obtain the best matching position: use the full search block matching algorithm to search in a small neighborhood with the rough matching position as the center; obtain the best matching position; calculate the displacement of the micro-motion platform: according to the The visual system imaging model establishes the image Jacobian matrix, and converts the displacement corresponding to the best matching position in the image space into the actual displacement of the micro-motion platform. The invention combines the improved particle swarm algorithm and the whole area search algorithm, reduces the consumption of computing resources, and realizes fast matching and displacement measurement; compared with the existing micro-displacement measurement technology, the method has low cost of measurement equipment, high precision, and available A micro-motion system with two degrees of freedom in the measurement plane.

Description

Method and system for measuring displacement of micro-motion platform based on microscopic vision

technical field

The invention relates to a method and system for measuring the displacement of a micro-motion platform based on microscopic vision.

Background technique

Modern science and technology are rapidly developing towards the small and ultra-precision fields. The rise of micron and nanotechnology has triggered major changes in the fields of manufacturing, information, materials, biology, medical treatment and national defense. At the same time, the development of micro-nano technology also puts forward higher requirements for ultra-precision measurement technology. Due to the advantages of high measurement accuracy, fast response speed, and many measurement degrees of freedom, non-contact optical measurement methods have been widely used in the micro-nano field. At present, research at home and abroad mainly focuses on computer micro-vision measurement, stroboscopic micro-measurement, laser Doppler measurement, micro-interferometry, etc. Among them, the computer micro-vision measurement uses the micro-vision system to judge the displacement of the moving part in the micro-motion system through the analysis of the motion vector in the micro-vision image. However, image processing in microscopic vision often uses the ordinary image block matching method or image feature matching to estimate the displacement of the micro-motion platform, so the efficiency is low, and it is difficult to meet the rapid response requirements of the measurement system.

Contents of the invention

In view of the above-mentioned prior art and existing problems, the purpose of the present invention is to provide a method and system for measuring the displacement of a micro-motion platform based on microscopic vision, which can be directly applied to the measurement of a micro-motion platform, and solve the technical problems in the current micro-nano control platform measurement .

The present invention is achieved through the following technical solutions:

The method of measuring the displacement of the micro-motion platform based on microscopic vision, the steps are as follows:

Step (1): Image sequence acquisition: collect a set of image sequences through the micro vision system;

Step (2): Acquisition of rough matching position: use the improved particle swarm optimization algorithm to quickly search in the entire search domain to obtain the rough matching position of the image block;

Step (3): Obtaining the best matching position: centering on the rough matching position, use the whole-area search matching algorithm to search in a small neighborhood to obtain the best matching position;

Step (4): Displacement calculation of the micro-motion platform: The image Jacobian matrix is established according to the imaging model of the micro-vision system, and the displacement corresponding to the best matching position in the image space is converted into the actual displacement of the micro-motion platform.

In the step (1), the micro-vision system includes: a microscope, a CCD camera is installed on the top of the microscope, the CCD camera is connected to a computer terminal, and the XY two-degree-of-freedom micro-motion platform is fixed on the microscope stage, The XY two-degree-of-freedom micro-motion platform is driven by PZT control. The upper surface of the XY two-degree-of-freedom micro-motion platform is marked with a smooth surface. For target plane imaging, the optical axis of the microscope is perpendicular to the upper surface of the marker. At the same time, the optical axis of the microscope is also perpendicular to the target plane of the CCD camera, and the CCD target plane is parallel to the upper surface of the marker.

In the step (2), the coarse matching position acquisition includes:

Step (2-1): Determine the ROI area of the image, and use the ROI area as the solution space for image block matching. The ROI area is determined according to the bit stroke x _R of the XY two-degree-of-freedom micro-motion platform, the microscope magnification k, and the camera pixel size p And the size of the image block [X, Y], the minimum size of the selected ROI area is:

Step (2-2): Select the summed absolute difference (summed absolute difference, SAD) as the matching criterion, which is also the objective function of the particle swarm optimization algorithm. For each pixel in each image [x+u,y+ v] ^T , the corresponding fitness value fit is:

Wherein, x is the abscissa of the image pixel, y is the ordinate of the image pixel, u is the motion amount of the image block in the horizontal axis direction, and v is the motion amount in the vertical axis direction.

Step (2-3): Particle initialization: evenly distribute I initial particles in the solution space, and use formula (2) to calculate the Corresponding fitness value;

Step (2-4): put the i-th particle In the process of n iterations, the smallest fitness value in the fitness value [fit _i,1 ,fit _i,2 ,...fit _i,n ] is used as the local optimal solution pbest[i] of each particle:

pbest[i]=min{fit _i,1 ,fit _i,2 ,...fit _i,n };

Select the particle with the smallest fitness value of all particles during n iterations as the global optimal solution gbest[n], namely:

gbest[n]=min{pbest ₁ ,pbest ₂ ,...pbest _I };

Each particle updates its position and velocity according to formula (3):

In the formula, n represents the nth iteration, C ₁ and C ₂ are learning factors, set to 2, R ₁ and R ₂ are random numbers, and R ₁ and R ₂ ∈ [0,1].

Indicates the velocity of the i-th particle during the n+1 iteration,

Indicates the position of the i-th particle in the n+1 iteration process,

Indicates the velocity of the i-th particle during the n-th iteration,

Indicates the position of the i-th particle during the n-th iteration;

Step (2-5): Calculate the distance r _gbest,i between particle i and the particle of the current global optimal solution after the nth iteration:

Calculate the closest distance r _nearest,i between particle i and the position passed by the entire particle swarm:

Among them, p∈[1,2,...I], q∈[1,2,...n-1];

After each particle updates its own position, the particle updates its own fitness value fit _{i,n according to the set rules, and the particle updates its own fitness value fit i,n according} to the set rules as follows:

a. If r _gbest,i < r ₀ , where r ₀ is the set threshold, the new particle i updates its fitness value according to formula (2);

b. If r _gbest,i ＞r ₀ and r _nearest,i ＞r ₀ , the new particle i updates its fitness value according to formula (2);

c. If r _gbest,i >r ₀ and r _nearest,i <r ₀ , replace the fitness value of the new particle with the fitness value of the point where the new particle i is the closest to the entire particle swarm, fit _i,n = SAD _nearest ;

Step (2-6): Repeat steps (2-4)-(2-5) until the maximum number of iterations is met, and the coordinates corresponding to the calculated global optimal solution are used as the rough matching position

In the step (3), it is assumed that the coarse matching position As the center, select a small search area, the size of the small search area is set to 7×7 pixels, search with the full search block matching algorithm in the small search area, and calculate the fitness value of 49 pixels in the small search area , select the point with the smallest fitness value as the final matching result

In the step (4), according to the description in the step (1), the CCD target plane is parallel to the upper surface of the marker, and the imaging model is simplified to a pinhole model; the Jacobian matrix of the image space and the micro-motion platform is as follows:

In the formula, x _img and y _img are the image space coordinates, x ₀ and y ₀ are the position coordinates of the micro-motion platform, is a Jacobian matrix with constant parameters.

According to the final matching result obtained in step (3) Calculate the position corresponding to the micro-motion platform:

The microscopic vision-based micro-motion platform displacement measurement system includes: a memory, a processor, and computer instructions stored on the memory and run on the processor, and the computer instructions complete the following steps when running on the processor:

Step (1): Image sequence acquisition: collect a set of image sequences through the micro vision system;

Step (2): Acquisition of rough matching position: use the improved particle swarm optimization algorithm to quickly search in the entire search domain to obtain the rough matching position of the image block;

Step (3): Obtaining the best matching position: centering on the rough matching position, use the whole-area search matching algorithm to search in a small neighborhood to obtain the best matching position;

Step (4): Displacement calculation of the micro-motion platform: The image Jacobian matrix is established according to the imaging model of the micro-vision system, and the displacement corresponding to the best matching position in the image space is converted into the actual displacement of the micro-motion platform.

A computer-readable storage medium having stored thereon computer instructions which, when executed on a processor, perform the following steps:

Step (1): Image sequence acquisition: collect a set of image sequences through the micro vision system;

Step (2): Acquisition of rough matching position: use the improved particle swarm optimization algorithm to quickly search in the entire search domain to obtain the rough matching position of the image block;

Step (3): Obtaining the best matching position: centering on the rough matching position, use the whole-area search matching algorithm to search in a small neighborhood to obtain the best matching position;

Step (4): Displacement calculation of the micro-motion platform: The image Jacobian matrix is established according to the imaging model of the micro-vision system, and the displacement corresponding to the best matching position in the image space is converted into the actual displacement of the micro-motion platform.

The beneficial effects of the present invention are:

The invention uses the combination of the improved particle swarm algorithm and the whole area search algorithm to reduce the consumption of computing resources and realize fast matching and displacement measurement; at the same time, compared with the existing micro-displacement measurement technology, the method has the advantages of low cost of measurement equipment and high precision. High, can be used to measure the micro-motion system with two degrees of freedom (X-Y) in the plane, etc.

Description of drawings

The accompanying drawings constituting a part of the present application are used to provide further understanding of the present application, and the schematic embodiments and descriptions of the present application are used to explain the present application, and do not constitute improper limitations to the present application.

Fig. 1 is a hardware connection diagram of the present invention;

Fig. 2 is a schematic diagram of particle update fitness value rule in the improved particle swarm optimization algorithm;

Figure 3 is based on As the center, select a small search domain, and use the full search block matching algorithm to search for the schematic diagram of the exact solution;

Fig. 4 is a schematic diagram of simplifying the imaging model into a pinhole model;

Fig. 5 is a flow chart of the method of the present invention.

detailed description

It should be pointed out that the following detailed description is exemplary and intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and/or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and/or combinations thereof.

As shown in Figure 5, the microscopic vision-based micro-motion platform displacement measurement method, the steps are as follows:

Step (1): Image sequence acquisition: collect a set of image sequences through a micro-vision system consisting of marked feature points, a micro-motion platform, a stereo microscope, and a CCD camera;

Step (2): Acquisition of rough matching position: use the improved particle swarm optimization algorithm to quickly search in the entire search domain to obtain the rough matching position of the image block; as shown in Figure 2;

Step (3): Obtaining the best matching position: centering on the rough matching position, use the full search block matching algorithm to search in a small neighborhood to obtain the best matching position;

Step (4): Displacement calculation of the micro-motion platform: The image Jacobian matrix is established according to the imaging model of the micro-vision system, and the displacement corresponding to the best matching position in the image space is converted into the actual displacement of the micro-motion platform.

In the step (1), the principle of image sequence acquisition is as follows: the principle of image acquisition is as shown in Figure 1, the execution part of the XY two-degree-of-freedom micro-motion platform is driven by PZT, and markers are attached to the surface, and the surface of the markers is smooth. After the coaxial light is reflected by the marker, it passes through the microscope optical path and forms an image on the CCD target plane. Wherein, the optical axis of the microscope is perpendicular to the upper surface of the marker, and the optical axis of the microscope is also perpendicular to the target plane of the camera.

In the step (2), the coarse matching position acquisition includes:

Step (2-1) Determine the ROI area of the image, and use the entire ROI area as the solution space for image block matching. The entire ROI area can be determined according to the position stroke x _R of the micro-motion platform, the microscope magnification k, the camera pixel size p, and the image block The size [X, Y] is determined, and the minimum selected ROI area is:

Step (2-2) select the summed absolute difference (summed absolute difference, SAD) as the matching criterion, which is also the objective function of the particle swarm optimization algorithm, for each pixel point [x+u,y+v] ^T in each image , and its corresponding fitness value is:

Step (2-3) particle initialization: uniformly distribute I initial particles in the whole solution space, each particle Use formula (2) to calculate its corresponding fitness value.

Step (2-4) the i-th particle particle The smallest value among the fitness values [fit _i,1 ,fit _i,2 ,...fit _i,n ] in the n iteration process is taken as the local optimal solution of each particle, that is, pbest[i]= min{fit _i,1 ,fit _i,2 ,...fit _i,n }. Select the particle with the smallest fitness value of all particles during n iterations as the global optimal solution, that is,

gbest[n]=min{pbest ₁ , pbest ₂ ,... pbest ₁ }.

Each particle updates its position and velocity as follows:

In the formula, n represents the nth iteration, C ₁ and C ₂ are the learning factors, which are set to 2, R ₁ and R ₂ are random numbers, and R ₁ and R ₂ ∈ [0,1].

Step (2-5) Calculate the distance between particle i and the particle of the current global optimal solution after the nth iteration Calculate the shortest distance between particle i and the position passed by the entire particle swarm, that is where p∈[1,2,...I],q∈[1,2,...n-1]. After each particle updates its own position, the particle updates its own fitness value fit _i,n according to the following rules, as follows:

ar _gbest,i < r ₀ , where r ₀ is the threshold, and the new particle i updates its fitness value according to formula (2);

br _gbest,i ＞r ₀ and r _nearest,i ＞r ₀ , the new particle i updates its fitness value according to formula (2);

cr _gbest,i >r ₀ and r _nearest,i <r ₀ , replace the fitness value of the new particle with the fitness value of the point where the new particle i is closest to the entire particle swarm, that is, fit _i,n =SAD _nearest .

Among them, r ₀ is the set threshold, which can be determined by empirical formula according to the search window and the number of particles.

Step (2-6) Repeat steps (2-4), (2-5) until the maximum number of iterations is met, and use the coordinates corresponding to the global optimal solution calculated by the above method as the rough matching position

In described step (3), think In the center, select a small search field and set its size to 7×7pixel, as shown in Figure 3. Search with the full search block matching algorithm in the entire area, calculate the fitness value of 49 pixels in the area, and select the point with the smallest fitness value as the final matching result

In the step (4), according to the step (1), the CCD target plane is parallel to the upper surface of the marker, so the imaging model can be simplified to a pinhole model, as shown in FIG. 4 . According to the geometric relationship shown in the figure, the Jacobian matrix of the image space and the micro-motion platform can be derived as follows:

In the formula, x _img and y _img are the image space coordinates, x ₀ and y ₀ are the position coordinates of the micro-motion platform, is a Jacobian matrix with constant parameters. According to the requirement in step (3), the position corresponding to the micro-movement platform can be deduced:

The above descriptions are only preferred embodiments of the present application, and are not intended to limit the present application. For those skilled in the art, there may be various modifications and changes in the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of this application shall be included within the protection scope of this application.

Claims (10)

1. the micromotion platform displacement measurement method based on micro-vision, it is characterized in that, step is as follows：

Step (1)：Image sequence acquisition：One group of image sequence is gathered by micro- vision system；

Step (2)：Thick matched position obtains：Using improved particle swarm optimization algorithm in whole region of search fast search, obtain Obtain the thick matched position of image block；

Step (3)：Best match position obtains：Centered on thick matched position, using area coverage matching algorithm small Searched in neighborhood, obtain best match position；

Step (4)：Micromotion platform displacement calculates：Image Jacobin matrix is established according to micro- vision system imaging model, by image Displacement corresponding to best match position is converted to micromotion platform actual displacement in space.

2. the micromotion platform displacement measurement method based on micro-vision as claimed in claim 1, it is characterized in that, the step (1) in, micro- vision system, including：Microscope, the microscope top installation CCD camera, the CCD camera are whole with computer End connection, the XY two degrees of freedom micromotion platform are fixed on microscope carrier, and the XY two degrees of freedom micromotion platform passes through Label is posted in PZT controller drive controls, the XY two degrees of freedom micromotion platform upper surface, and label surface is smooth；Coaxially Light is incident to label back reflection and is imaged by microscopes optical path in CCD camera target plane, and microscopes optical axis is perpendicular to label Upper surface, meanwhile, microscopes optical axis is also perpendicularly to CCD camera target plane, and CCD target planes are parallel to label upper surface.

3. the micromotion platform displacement measurement method based on micro-vision as claimed in claim 1, it is characterized in that, the step (2) in, thick matched position, which obtains, to be included：

Step (2-1)：Image ROI region is determined, the solution space using ROI region as image Block- matching, ROI region determines basis The position stroke x of XY two degrees of freedom micromotion platforms_R, microscope magnification k, camera pixel dimension p and image block size size [X, Y] is determined, the ROI region size of selection is minimum：

<mrow> <mi>R</mi> <mi>O</mi> <mi>I</mi> <mo>=</mo> <mo>&amp;lsqb;</mo> <mfrac> <mrow> <mn>2</mn> <msub> <mi>x</mi> <mi>R</mi> </msub> <mi>k</mi> </mrow> <mi>p</mi> </mfrac> <mo>+</mo> <mfrac> <mi>X</mi> <mn>2</mn> </mfrac> <mo>+</mo> <mn>1</mn> <mo>,</mo> <mfrac> <mrow> <mn>2</mn> <msub> <mi>x</mi> <mi>R</mi> </msub> <mi>k</mi> </mrow> <mi>p</mi> </mfrac> <mo>+</mo> <mfrac> <mi>Y</mi> <mn>2</mn> </mfrac> <mo>+</mo> <mn>1</mn> <mo>&amp;rsqb;</mo> <mo>;</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>

Step (2-2)：For the accumulative absolute error of selection as matching criterior, accumulative absolute error is also the mesh of particle swarm optimization algorithm Scalar functions, to each pixel [x+u, y+v] in each image^T, corresponding adaptive value fit is：

<mrow> <mi>f</mi> <mi>i</mi> <mi>t</mi> <mo>=</mo> <mi>S</mi> <mi>A</mi> <mi>D</mi> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>x</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>X</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>y</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>Y</mi> </munderover> <mo>|</mo> <msub> <mi>f</mi> <mi>j</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>f</mi> <mrow> <mi>j</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>+</mo> <mi>n</mi> <mo>,</mo> <mi>y</mi> <mo>+</mo> <mi>v</mi> <mo>)</mo> </mrow> <mo>|</mo> <mo>;</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow>

Wherein, x is image slices vegetarian refreshments abscissa, and y is image slices vegetarian refreshments ordinate, and u is image block X direction amount of exercise, and v is Y direction amount of exercise；

Step (2-3)：Particle initializes：I primary is evenly distributed on solution space, each particle is calculated using formula (2)Corresponding adaptive value；

Step (2-4)：By i-th of particleAdaptive value [fit is obtained in n iterative process_i,1,fit_i,2, ...fit_i,n] locally optimal solution pbest [i] of the minimum adaptive value in the inside as each particle：

Pbest [i]=min { fit_i,1,fit_i,2,...fit_i,n}；

The minimum particle of all particles adaptive value in n iterative process is selected as globally optimal solution gbest [n], i.e.,：

Gbest [n]=min { pbest₁,pbest₂,...pbest_I}；

Each particle updates position and the speed of oneself with formula (3)：

<mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mover> <mi>v</mi> <mo>&amp;RightArrow;</mo> </mover> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>n</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mover> <mi>v</mi> <mo>&amp;RightArrow;</mo> </mover> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>C</mi> <mn>1</mn> </msub> <mo>&amp;times;</mo> <msub> <mi>R</mi> <mn>1</mn> </msub> <mo>&amp;times;</mo> <mrow> <mo>(</mo> <mi>p</mi> <mi>b</mi> <mi>e</mi> <mi>s</mi> <mi>t</mi> <mo>&amp;lsqb;</mo> <mi>i</mi> <mo>&amp;rsqb;</mo> <mo>-</mo> <msub> <mover> <mi>x</mi> <mo>&amp;RightArrow;</mo> </mover> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>C</mi> <mn>2</mn> </msub> <mo>&amp;times;</mo> <msub> <mi>R</mi> <mn>2</mn> </msub> <mo>&amp;times;</mo> <mrow> <mo>(</mo> <mi>p</mi> <mi>b</mi> <mi>e</mi> <mi>s</mi> <mi>t</mi> <mo>&amp;lsqb;</mo> <mi>i</mi> <mo>&amp;rsqb;</mo> <mo>-</mo> <msub> <mover> <mi>x</mi> <mo>&amp;RightArrow;</mo> </mover> <mi>i</mi> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mover> <mi>x</mi> <mo>&amp;RightArrow;</mo> </mover> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>n</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mover> <mi>x</mi> <mo>&amp;RightArrow;</mo> </mover> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mover> <mi>v</mi> <mo>&amp;RightArrow;</mo> </mover> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>n</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> <mo>;</mo> </mrow> 1

In formula, n represents nth iteration, C₁,C₂For Studying factors, 2, R are set to₁,R₂For random number, R₁,R₂∈[0,1]；

Speed of i-th of particle in (n+1)th iterative process is represented,

Position of i-th of particle in (n+1)th iterative process is represented,

Speed of i-th of particle during nth iteration is represented,

Represent position of i-th of particle during nth iteration；

Step (2-5)：After calculating nth iteration, the distance between particle of particle i and current globally optimal solution r_gbest,i：

<mrow> <msub> <mi>r</mi> <mrow> <mi>g</mi> <mi>b</mi> <mi>e</mi> <mi>s</mi> <mi>t</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> <mo>=</mo> <mo>|</mo> <mo>|</mo> <msub> <mover> <mi>x</mi> <mo>&amp;RightArrow;</mo> </mover> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>-</mo> <mi>g</mi> <mi>b</mi> <mi>e</mi> <mi>s</mi> <mi>t</mi> <mo>&amp;lsqb;</mo> <mi>n</mi> <mo>&amp;rsqb;</mo> <mo>|</mo> <msub> <mo>|</mo> <mn>2</mn> </msub> <mo>,</mo> </mrow>

Calculate particle i and whole population by position minimum distance r_nearest,i：

<mrow> <msub> <mi>r</mi> <mrow> <mi>n</mi> <mi>e</mi> <mi>a</mi> <mi>r</mi> <mi>e</mi> <mi>s</mi> <mi>t</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> <mo>=</mo> <mi>m</mi> <mi>i</mi> <mi>n</mi> <mo>|</mo> <mo>|</mo> <msub> <mover> <mi>x</mi> <mo>&amp;RightArrow;</mo> </mover> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mover> <mi>x</mi> <mo>&amp;RightArrow;</mo> </mover> <mi>p</mi> </msub> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> <mo>|</mo> <msub> <mo>|</mo> <mn>2</mn> </msub> <mo>,</mo> </mrow>

Wherein, p ∈ [1,2 ... I], q ∈ [1,2 ... n-1]；

After each particle updates oneself position, particle is according to the adaptive value fit for setting Policy Updates oneself_i,n；

Step (2-6)：Repeat step (2-4)-(2-5), until meeting maximum iteration, and the globally optimal solution pair calculated The coordinate answered is as thick matched position

4. the micromotion platform displacement measurement method based on micro-vision as claimed in claim 3, it is characterized in that, the particle root According to the adaptive value fit of setting Policy Updates oneself_i,nIt is as follows：

A. if r_gbest,i＜ r₀, r in formula₀For given threshold, then new particle i updates its adaptive value according to formula (2)；

B. if r_gbest,i＞ r₀And r_nearest,i＞ r₀, then new particle i its adaptive value is updated according to formula (2)；

C. if r_gbest,i＞ r₀And r_nearest,i＜ r₀, then with new particle i apart from whole population by position closest approach Adaptive value replaces the adaptive value of new particle, fit_i,n=SAD_nearest。

5. the micromotion platform displacement measurement method based on micro-vision as claimed in claim 1, it is characterized in that, the step (3) in, thick matched position is thoughtCentered on, a small region of search is selected, the size of the small region of search is arranged to 7 × 7 pixels, with Full-search block matching algorithm search in small region of search, calculate in small region of search 49 pixels Adaptive value, the minimum point of selection wherein adaptive value is as final matching results

6. the micromotion platform displacement measurement method based on micro-vision as claimed in claim 5, it is characterized in that, the step (4) in, according to step (1), imaging model is reduced to pin-hole model by CCD target planes parallel to label upper surface； The Jacobin matrix of image space and micromotion platform is as follows：

<mrow> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>x</mi> <mn>0</mn> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>y</mi> <mn>0</mn> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>&amp;epsiv;</mi> <mn>1</mn> </msub> </mtd> <mtd> <msub> <mi>&amp;epsiv;</mi> <mn>2</mn> </msub> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>-</mo> <msub> <mi>&amp;epsiv;</mi> <mn>2</mn> </msub> </mrow> </mtd> <mtd> <msub> <mi>&amp;epsiv;</mi> <mn>1</mn> </msub> </mtd> </mtr> </mtable> </mfenced> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>x</mi> <mrow> <mi>i</mi> <mi>m</mi> <mi>g</mi> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>y</mi> <mrow> <mi>i</mi> <mi>m</mi> <mi>g</mi> </mrow> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>;</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow>

X in formula_img, y_imgFor image space coordinate, x₀, y₀For micromotion platform position coordinates,It is that a parameter is constant Jacobin matrix.

7. the micromotion platform displacement measurement method based on micro-vision as claimed in claim 6, it is characterized in that,

According to final matching results required in step (3)Calculate the position corresponding to micromotion platform：

<mrow> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>x</mi> <mn>0</mn> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>y</mi> <mn>0</mn> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>&amp;epsiv;</mi> <mn>1</mn> </msub> </mtd> <mtd> <msub> <mi>&amp;epsiv;</mi> <mn>2</mn> </msub> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>-</mo> <msub> <mi>&amp;epsiv;</mi> <mn>2</mn> </msub> </mrow> </mtd> <mtd> <msub> <mi>&amp;epsiv;</mi> <mn>1</mn> </msub> </mtd> </mtr> </mtable> </mfenced> <msub> <mover> <mi>x</mi> <mo>&amp;RightArrow;</mo> </mover> <mrow> <mi>f</mi> <mi>i</mi> <mi>n</mi> <mi>a</mi> <mi>l</mi> </mrow> </msub> <mo>;</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>5</mn> <mo>)</mo> </mrow> <mo>.</mo> </mrow>

8. the micromotion platform displacement measurement system based on micro-vision, it is characterized in that, including：Memory, processor and it is stored in The computer instruction run on memory and on a processor, following walk is completed when the computer instruction is run on a processor Suddenly：

Step (1)：Image sequence acquisition：One group of image sequence is gathered by micro- vision system；

Step (2)：Thick matched position obtains：Using improved particle swarm optimization algorithm in whole region of search fast search, obtain Obtain the thick matched position of image block；

Step (3)：Best match position obtains：Centered on thick matched position, using area coverage matching algorithm small Searched in neighborhood, obtain best match position；

Step (4)：Micromotion platform displacement calculates：Image Jacobin matrix is established according to micro- vision system imaging model, by image Displacement corresponding to best match position is converted to micromotion platform actual displacement in space.

9. the micromotion platform displacement measurement system based on micro-vision as claimed in claim 8, it is characterized in that, the step (1) in, micro- vision system, including：Microscope, the microscope top installation CCD camera, the CCD camera are whole with computer End connection, the XY two degrees of freedom micromotion platform are fixed on microscope carrier, and the XY two degrees of freedom micromotion platform passes through Label is posted in PZT drive controls, the XY two degrees of freedom micromotion platform upper surface, and label surface is smooth；Axis light is incident Be imaged to label back reflection by microscopes optical path in CCD camera target plane, microscopes optical axis perpendicular to label upper surface, Meanwhile microscopes optical axis is also perpendicularly to CCD camera target plane, CCD target planes are parallel to label upper surface.

10. a kind of computer-readable recording medium, is stored thereon with computer instruction, it is characterized in that, the computer instruction exists Following steps are completed when being run on processor：

Step (1)：Image sequence acquisition：One group of image sequence is gathered by micro- vision system；

Step (2)：Thick matched position obtains：Using improved particle swarm optimization algorithm in whole region of search fast search, obtain Obtain the thick matched position of image block；

Step (3)：Best match position obtains：Centered on thick matched position, using area coverage matching algorithm small Searched in neighborhood, obtain best match position；

Step (4)：Micromotion platform displacement calculates：Image Jacobin matrix is established according to micro- vision system imaging model, by image Displacement corresponding to best match position is converted to micromotion platform actual displacement in space.