Flight crystal pricking path planning method and device

Abstract

The invention discloses a flight crystal spur path planning method and device, and the method comprises the steps: carrying out the initialization of a plurality of control point coordinates of a flight crystal spur, and generating a plurality of initial control point coordinates; constructing a kinematics constraint matrix and a control point basis function matrix according to each initial control point coordinate and preset mass transfer motion data; determining a target control order by adopting a preset solution algorithm according to the kinematics constraint matrix, the control point basis function matrix and each initial control point coordinate; according to the target control order, the kinematics constraint matrix and the control point basis function matrix, determining a target Bessel motion curve of the flight crystal thorns; the technical problem that the obtained Bessel path is too complex due to an existing flight crystal path planning method is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of path optimization, and in particular to a flying thorn crystal path planning method and device. Background Art

[0002] With the continuous development of display technology, Mini / Micro LED has become a revolutionary technology in the new display field with its excellent display performance and longer service life. However, the mass production of Mini / Micro LED screens still faces a series of technical challenges such as substrate selection, detection and repair. The most important technical difficulty is the mass transfer of chips. Mass transfer refers to the process of transferring Mini / micro LED chips from the source substrate to the target circuit substrate. From the chip transfer method to the design of the transfer equipment motion parameters and motion path, the chip transfer effect (positioning accuracy, response speed) will be affected, thereby determining the yield rate and mass production efficiency of the finished Mini / Micro LED display.

[0003] At present, a mass transfer device based on flying thorn crystals has been designed, but the motion planning method for the flying thorn crystal working scene has not been studied in detail. For the flying thorn crystal scene, the motion trajectory of the needle must not only have a smooth transition and meet the characteristics of smooth start / stop, but also ensure that the chip is transferred at the same speed as the substrate to avoid scratching the chip.

[0004] Most existing path planning methods in other fields are based on polynomial curve design to obtain motion paths. Among them, Bezier curves are widely used due to their strong plasticity and path freedom. However, most planning methods can only optimize Bezier curves based on a set fixed control order. When the motion requirements are too strict, the control order has to be set to a large value, resulting in the Bezier path being too complex. Summary of the invention

[0005] The present invention provides a flying thorn crystal path planning method and device, which are used to solve the technical problem that the existing flying thorn crystal path planning method causes the obtained Bessel path to be too complicated.

[0006] A first aspect of the present invention provides a flying thorn crystal path planning method, comprising:

[0007] In response to the planning request, the coordinates of multiple control points of the flying thorn crystal are initialized to generate multiple initial control point coordinates;

[0008] Constructing a kinematic constraint matrix and a control point basis function matrix according to the coordinates of each of the initial control points and the preset massive transfer motion data;

[0009] Determine the target control order according to the kinematic constraint matrix, the control point basis function matrix and the coordinates of each initial control point by using a preset solution algorithm;

[0010] The target Bezier motion curve of the flying thorn crystal is determined according to the target control order, the kinematic constraint matrix and the control point basis function matrix.

[0011] Optionally, the preset mass transfer motion data includes a path motion starting point, a path motion end point, velocity vector restriction data, and upper and lower limit data of motion space; the kinematic constraint matrix includes a left-side constraint matrix of a kinematic equation, a right-side constraint matrix of a kinematic equation, and a right-side constraint matrix of a kinematic inequality; the control point basis function matrix includes an initial control point matrix and a basis function matrix corresponding to the initial control point matrix; the step of constructing a kinematic constraint matrix according to the coordinates of each of the initial control points and the preset mass transfer motion data includes:

[0012] Using the coordinates of each of the initial control points to construct an initial control point matrix;

[0013] Using the basis functions corresponding to the coordinates of each of the initial control points, a constraint matrix and a basis function matrix on the left side of the kinematic equation corresponding to the initial control point matrix are constructed;

[0014] According to the path motion starting point, path motion end point and velocity vector restriction data, the right-hand constraint matrix of the kinematic equation is constructed;

[0015] The constraint matrix on the right side of the kinematic inequality is constructed using the upper and lower limit data of the motion space.

[0016] Optionally, the step of using a preset solution algorithm to determine the target control order according to the kinematic constraint matrix, the control point basis function matrix and the coordinates of each initial control point includes:

[0017] Using the initial control point matrix as the initial order control point matrix;

[0018] Based on the order of the initial order control point matrix, a first optimization inequality constraint matrix is ​​constructed using the right-hand side constraint matrix of the kinematic inequality;

[0019] Constructing a first-order performance function according to the first optimized inequality constraint matrix, the right-side constraint matrix of the kinematic equation, the left-side constraint matrix of the preset kinematic inequality, the initial order control point matrix, and the left-side constraint matrix of the kinematic equation corresponding to the initial order control point matrix and the basis function matrix;

[0020] Using the preset solution algorithm to solve the first-order performance function, and determine a first optimization control point matrix and a first performance index;

[0021] Determining an increased-order matrix based on the order of the first optimized control point matrix;

[0022] Determining an intermediate order control point matrix according to the raised order matrix and the first optimized control point matrix;

[0023] Based on the order of the intermediate-order control point matrix, a second optimization inequality constraint matrix is ​​constructed using the right-hand side constraint matrix of the kinematic inequality;

[0024] Constructing a second-order performance function according to the second optimized inequality constraint matrix, the right-side constraint matrix of the kinematic equation, the left-side constraint matrix of the preset kinematic inequality, the intermediate-order control point matrix, and the left-side constraint matrix of the kinematic equation corresponding to the intermediate-order control point matrix and the basis function matrix;

[0025] Solving the second-order performance function using the preset solving algorithm to determine a second optimization control point matrix and a second performance index;

[0026] comparing the second performance indicator with the first performance indicator;

[0027] If the first performance index is less than or equal to the second performance index, the order of the first optimization control point matrix is ​​used as the target control order.

[0028] Optionally, it also includes:

[0029] If the first performance index is greater than the second performance index, taking the second optimization control point matrix as a new first optimization control point matrix and taking the second performance index as a new first performance index;

[0030] Determine a second performance index by using the preset solution algorithm according to the new first optimization control point matrix, the right-side constraint matrix of the kinematic equation, the left-side constraint matrix of the preset kinematic inequality, and the order of the new first optimization control point matrix until the first performance index is less than or equal to the second performance index;

[0031] The order of the first optimization control point matrix determined when the first performance index is less than or equal to the second performance index is used as the target control order.

[0032] Optionally, the step of determining a target Bezier motion curve of the flying thorn crystal according to the target control order, the kinematic constraint matrix and the control point basis function matrix comprises:

[0033] Constructing an initial Bezier curve using the initial control point matrix and a basis function matrix corresponding to the initial control point matrix;

[0034] Determining a target raised-order matrix based on the target control order and the order of the initial control point matrix;

[0035] Constructing a raised-order control point matrix according to the target raised-order matrix and the first optimized control point matrix;

[0036] Determine a multi-time point basis function matrix based on the column vectors in the raised-order control point matrix and the initial Bezier curve;

[0037] Based on the order of the initial control point matrix, an initial optimization inequality constraint matrix is ​​constructed using the right-hand constraint matrix of the kinematic inequality;

[0038] The target order optimization performance function is constructed by using the multi-time point basis function matrix, the initial control point matrix, the basis function matrix corresponding to the initial control point matrix and the left-side constraint matrix of the kinematic equation, the right-side constraint matrix of the kinematic equation, the left-side constraint matrix of the preset kinematic inequality, the target raised-order matrix, and the initial optimization inequality constraint matrix;

[0039] Using the preset solution algorithm to solve the target order optimization performance function to determine the target control point matrix;

[0040] The target Bezier motion curve of the flying thorn crystal is determined according to the basis function matrix corresponding to the target control point matrix and the initial control point matrix.

[0041] Optionally, the target order optimization performance function is specifically:

[0042]

[0043] Among them, J(P n ) is the target order optimization performance function; P n is the initial control point matrix, which corresponds to the n-order control point; is a time derivative; B n (t) is the basis function matrix corresponding to the initial control point matrix, the order of this matrix is ​​n; T is the transpose of the matrix; A eq (n) is the constraint matrix on the left side of the kinematic equation corresponding to the initial control point matrix, and the order of this matrix is ​​n; c eq A is the constraint matrix on the right side of the kinematic equation; ie The constraint matrix on the left side of the preset kinematic inequality; is the target raised rank matrix; is the multi-time point basis function matrix; is the initial optimization inequality constraint matrix, the order of the matrix is ​​n; It is the preset step-up fault tolerance distance.

[0044] A second aspect of the present invention provides a flying thorn crystal path planning device, comprising:

[0045] A response module is used to respond to the planning request, initialize the coordinates of multiple control points of the flying thorn crystal, and generate multiple initial control point coordinates;

[0046] A matrix construction module is used to construct a kinematic constraint matrix and a control point basis function matrix according to the coordinates of each of the initial control points and the preset massive transfer motion data;

[0047] A control order determination module is used to determine a target control order according to the kinematic constraint matrix, the control point basis function matrix and the coordinates of each initial control point by using a preset solution algorithm;

[0048] The target path determination module is used to determine the target Bezier motion curve of the flying thorn crystal according to the target control order, the kinematic constraint matrix and the control point basis function matrix.

[0049] A third aspect of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the flying spike crystal path planning method as described in any one of the above items.

[0050] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the steps of the flying spike crystal path planning method as described in any one of the above items.

[0051] A fifth aspect of the present invention provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer executes the steps of the flying thorn crystal path planning method as described in any one of the above items.

[0052] It can be seen from the above technical solutions that the present invention has the following advantages:

[0053] The above scheme of the present invention provides a flying thorn crystal path planning method. When it is necessary to plan the path of the flying thorn crystal, firstly, the coordinates of multiple control points of the flying thorn crystal are initialized to generate multiple initial control point coordinates; then, according to the coordinates of each initial control point and the preset massive transfer motion data, a kinematic constraint matrix and a control point basis function matrix are constructed; a preset solution algorithm is used to determine the target control order according to the kinematic constraint matrix, the control point basis function matrix and the initial control point coordinates; finally, the target Bezier motion curve of the flying thorn crystal is determined according to the target control order, the kinematic constraint matrix and the control point basis function matrix; based on the above scheme, a preset solution algorithm is used to determine the target control order according to the constructed kinematic constraint matrix, the control point basis function matrix and the initial control point coordinates obtained after initialization, and the process of determining the target Bezier motion curve of the flying thorn crystal in combination with the kinematic constraint matrix and the control point basis function matrix does not need to set a fixed control order in advance, and a low-order optimal Bezier curve can be obtained even under strict motion requirements, making the Bezier path more concise and easy to use. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0055] Figure 1 A flowchart of a flying thorn crystal path planning method provided by an embodiment of the present invention;

[0056] Figure 2 A schematic diagram of pre-setting a large amount of transfer motion data provided by an embodiment of the present invention;

[0057] Figure 3 A diagram showing the displacement, velocity and acceleration changes of the flying thorn crystal in the X-axis direction provided by an embodiment of the present invention;

[0058] Figure 4 A diagram showing the displacement, velocity and acceleration changes of the flying thorn crystal in the Z-axis direction provided by an embodiment of the present invention;

[0059] Figure 5 A schematic diagram of the overall movement route, control points and movement space of the flying thorn crystal provided by an embodiment of the present invention;

[0060] Figure 6 A flowchart of another flying thorn crystal path planning method provided by an embodiment of the present invention;

[0061] Figure 7A schematic diagram of the motion of a flying thorn crystal mass transfer provided by an embodiment of the present invention;

[0062] Figure 8 A schematic diagram of a flying thorn crystal path planning method provided by an embodiment of the present invention;

[0063] Fig. 9 A structural block diagram of a flying spike crystal path planning device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The embodiments of the present invention provide a flying sting crystal path planning method and device, which are used to solve the technical problem that the existing flying sting crystal path planning method causes the obtained Bezier path to be too complicated.

[0065] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] See also Figure 1 , Figure 1 A flowchart of the steps of a flying spike crystal path planning method provided by an embodiment of the present invention.

[0067] The present invention provides a flying thorn crystal path planning method, comprising:

[0068] Step 101, in response to a planning request, initialize the coordinates of multiple control points of the flying thorn crystal to generate multiple initial control point coordinates.

[0069] It should be noted that, during the stage movement time, the flying thorn crystal will have corresponding multiple initial control point coordinates; wherein, the number of initial control point coordinates can be set as needed, and the present invention is not limited thereto.

[0070] Step 102: construct a kinematic constraint matrix and a control point basis function matrix according to the coordinates of each initial control point and the preset mass transfer motion data.

[0071] The kinematic constraint matrix includes the constraint matrix on the left side of the kinematic equation, the constraint matrix on the right side of the kinematic equation, and the constraint matrix on the right side of the kinematic inequality.

[0072] The control point basis function matrix includes an initial control point matrix and a basis function matrix corresponding to the initial control point matrix.

[0073] Please note that Figure 2, preset a large amount of transfer motion data including the path motion starting point u 0 =[x 0 ,z 0 ] T 、Path motion end point u e =[x e ,z e ] T , velocity vector restriction data (i.e., the restriction v on the velocity vector of the thorn crystal at the end point of the motion path e =[x ve ,z ve ] T ), the upper and lower limit data of the motion space, the upper and lower limit data of the motion space include the lower limit u of the motion space m =[x m ,z m ] T and the upper limit u of the motion space M =[x M ,z M ] T , these data represent the limits on the spatial position of the transfer device during movement; where x 0 Represents the coordinate of the starting point of the movement in the X-axis direction, z 0 Represents the coordinate of the starting point of the movement in the Z-axis direction, x e Represents the coordinate of the end point of the movement in the X-axis direction, z e Represents the coordinate of the end point of the movement in the Z-axis direction, x m Represents the lower limit coordinate of the motion space in the X-axis direction, z m Represents the lower limit coordinate of the motion space in the Z-axis direction, x M Represents the upper limit coordinate of the motion space in the X-axis direction, z M Represents the upper limit coordinate of the motion space in the Z-axis direction, x ve Represents the limit value of the motion path end point on the X-axis of the thorn crystal velocity vector, z ve Represents the limit value of the motion path end point on the Z-axis of the thorn crystal velocity vector.

[0074] Furthermore, the preset massive transfer motion data also includes the j-order smooth start requirement of the motion process and the initial control order (representing the complexity of the Bessel motion path). The initial control order is the order of the initial control point matrix. The j-order smooth start requirement is defined as: at the starting point of the motion, the 1st to jth time derivative values ​​of the path are all 0.

[0075] Specifically, the coordinates of each initial control point are used to construct an initial control point matrix, which can be expressed as: n =[p 0 ,p 1 ,…,p n], where P n The initial control point coordinates p 0 ,p 1 ,…,p n The initial control point matrix, p 0 is the coordinate of the first initial control point, p 1 is the coordinate of the second initial control point, p n is the coordinate of the nth initial control point.

[0076] It is worth mentioning that, according to the initial control point matrix and the basis function matrix corresponding to the initial control point matrix, the motion performance index function can be obtained, which represents the motion energy consumed by the motion path. The motion performance index function can be expressed as:

[0077]

[0078] Among them, J(P n ) is the motion performance index function; B n (t) is the basis function matrix corresponding to the initial control point matrix; is a time derivative; P n is the initial control point matrix; T is the matrix transpose.

[0079] Furthermore, the basis functions corresponding to the coordinates of each initial control point are used to construct the constraint matrix and basis function matrix on the left side of the kinematic equation corresponding to the initial control point matrix; wherein the constraint matrix on the left side of the kinematic equation corresponding to the initial control point matrix can be expressed as:

[0080]

[0081] Among them, A eq (n) is the constraint matrix on the left side of the kinematic equation corresponding to the initial control point matrix of order n; is the jth time derivative; T is the transpose of the matrix; B n (t) is the basis function of the Berstein basis function (the basis function corresponding to the coordinates of each initial control point ) is an n-dimensional Berstein matrix (a basis function matrix of order n) that represents the time-dependent part of the Bezier motion curve, t∈[0,1].

[0082] The construction process of the basis function matrix corresponding to the initial control point matrix can be expressed as:

[0083]

[0084] Among them, B n (t) is the basis function matrix of order n; t is the stage motion time; is the basis function corresponding to the coordinates of the first initial control point; is the basis function corresponding to the coordinates of the second initial control point; is the basis function corresponding to the coordinates of the n+1th initial control point; is the basis function corresponding to the coordinates of the i+1th initial control point; t i is the stage motion time corresponding to the coordinates of the i+1th initial control point.

[0085] Furthermore, according to the path motion starting point, path motion end point and velocity vector restriction data, a right-side constraint matrix of the kinematic equation is constructed, wherein the right-side constraint matrix of the kinematic equation can be expressed as:

[0086]

[0087] Among them, c eq is the constraint matrix on the right side of the kinematic equation; x 0 is the coordinate of the starting point of the movement in the X-axis direction; z 0 is the coordinate of the starting point of the movement in the Z-axis direction; e is the coordinate of the end point of the movement in the X-axis direction; z e is the coordinate of the end point of the movement in the Z-axis direction; ve x is the limit value of the velocity vector of the thorn crystal on the X axis at the end point of the motion path; ze The limit value of the velocity vector of the thorn crystal on the Z axis at the end point of the motion path.

[0088] Furthermore, the right-hand constraint matrix of the kinematic inequality is constructed through the upper and lower limit data of the motion space; wherein the right-hand constraint matrix of the kinematic inequality can be expressed as: c ie =[x M -x m z M -z m ] T , where c ie is the constraint matrix on the right side of the kinematic inequality, x M is the upper limit coordinate of the motion space in the X-axis direction, x m is the lower limit coordinate of the motion space in the X-axis direction, z M is the upper limit coordinate of the motion space in the Z-axis direction, z m It is the lower limit coordinate of the motion space in the Z-axis direction.

[0089] Step 103: using a preset solution algorithm to determine the target control order according to the kinematic constraint matrix, the control point basis function matrix and the coordinates of each initial control point.

[0090] Specifically, the initial control point matrix is ​​used as the initial order control point matrix, that is, in the process of increasing the matrix order, the initial order control point matrix uses the initial control point matrix to initialize the order control point matrix to obtain the initial order control point matrix, wherein the initial order control point matrix can be expressed as: k =[p 0 ,p 1 ,…,p k ], P k is the initial order control point matrix of order k, p 0 is the first control point in the initial order control point matrix, p 1 is the second control point in the initial order control point matrix, p k is the kth control point in the initial order control point matrix.

[0091] Furthermore, based on the order of the initial order control point matrix, the first optimization inequality constraint matrix is ​​constructed using the right-hand side constraint matrix of the kinematic inequality; wherein the first optimization inequality constraint matrix can be expressed as: is the first optimization inequality constraint matrix composed of k+1 right-hand constraint matrices of kinematic inequalities. The order of this matrix is ​​k, representing p k The corresponding optimization inequality constraint matrix), c ie is the constraint matrix on the right side of the kinematic inequality.

[0092] Furthermore, P k All control points in the kinematic inequality are restricted by the mechanical energy of the right-side constraint matrix, and the first-order performance function is constructed according to the first optimized inequality constraint matrix, the right-side constraint matrix of the kinematic equation, the preset left-side constraint matrix of the kinematic inequality, the initial order control point matrix, and the left-side constraint matrix of the kinematic equation corresponding to the initial order control point matrix, and the basis function matrix; wherein, by solving the first-order performance function, P is found under the condition of not exceeding the motion range and satisfying the 1-j order smooth start requirements. k Make the motion performance index function reach the minimum value; the first-order performance function can be expressed as:

[0093]

[0094] Among them, J(P k 0 is the first-order performance function; P k is the initial order control point matrix; is a time derivative; B k (t0 is the basis function matrix corresponding to the initial order control point matrix, the order of this matrix is ​​k; T is the matrix transpose; A eq(k) is the constraint matrix on the left side of the kinematic equation corresponding to the initial order control point matrix, and the order of this matrix is ​​k; c eq A is the constraint matrix on the right side of the kinematic equation; ie The constraint matrix on the left side of the preset kinematic inequality can be expressed as is the first optimization inequality constraint matrix, and the order of the matrix is ​​k.

[0095] It is worth mentioning that according to the above-mentioned principle of using the basis functions corresponding to the coordinates of each initial control point to construct the constraint matrix and basis function matrix on the left side of the kinematic equation corresponding to the initial control point matrix, the constraint matrix and basis function matrix on the left side of the kinematic equation corresponding to the control point matrix are composed of the basis functions corresponding to the coordinates of the control points. Therefore, the constraint matrix and basis function matrix on the left side of the kinematic equation corresponding to the initial order control point matrix can be obtained based on the principle of this step; wherein, the basis function matrix corresponding to the initial order control point matrix can be expressed as:

[0096]

[0097] Among them, B k (t) is the basis function matrix corresponding to the initial order control point matrix, the order of this matrix is ​​k, representing the k-dimensional Berstein matrix; is the first basis function in the basis function matrix corresponding to the initial order control point matrix; is the second basis function in the basis function matrix corresponding to the initial order control point matrix; is the kth basis function in the basis function matrix corresponding to the initial order control point matrix; T is the matrix transpose.

[0098] Furthermore, a preset solution algorithm is used to solve the first-order performance function to determine the first optimization control point matrix and the first performance index; wherein the preset solution algorithm is a projection gradient algorithm, a penalty function method, a multiplier method and other solution methods; by solving the above k-order optimization problem (first-order performance function), the k-order optimal control point matrix is ​​obtained (first optimization control point matrix) and k-order suboptimal performance index J k (the first performance indicator), that is

[0099] Further, based on the order of the first optimized control point matrix, an increased order matrix is ​​determined; wherein the increased order matrix is ​​a k+1 dimensional increased order matrix having k+1 rows and k+2 columns, which represents the initial order control point matrix P kThe calculation process of increasing from k+1 control points to k+2 control points; when constructing the intermediate order control point matrix, it is necessary to traverse and calculate the elements of each row and column in the raised order matrix. The calculation formula of the element located in the x+1th row and y+1th column in the raised order matrix, that is, the construction process in the raised order matrix, can be expressed as:

[0100]

[0101] in, is the element in the x+1th row and y+1th column in the raised-order matrix; x is the xth row of the raised-order matrix; y is the yth column of the raised-order matrix; k is the order of the first optimization control point matrix.

[0102] Further, according to the raised order matrix and the first optimized control point matrix, an intermediate order control point matrix is ​​determined. The intermediate order control point matrix is ​​a k+1-dimensional control point matrix obtained after the first optimized control point matrix is ​​raised in order. The intermediate order control point matrix consists of a plurality of control points p 0 ,p 1 ,…,p k+1 The construction process of the intermediate-order control point matrix can be expressed as: P k+1 is the intermediate order control point matrix, is the first optimized control point matrix, is a raised rank matrix.

[0103] Furthermore, based on the order of the intermediate-order control point matrix, the right-hand constraint matrix of the kinematic inequality is used to construct the second optimization inequality constraint matrix. The second optimization inequality constraint matrix is ​​a k+1-order optimization inequality constraint matrix, which can be expressed as The second optimization inequality constraint matrix consists of k+2 kinematic inequality right-hand constraint matrices, representing the intermediate-order control point matrix P k+1 All control points in the ie Make restrictions.

[0104] Further, according to the second optimization inequality constraint matrix, the right-side constraint matrix of the kinematic equation, the left-side constraint matrix of the preset kinematic inequality, the intermediate-order control point matrix, the left-side constraint matrix of the kinematic equation corresponding to the intermediate-order control point matrix, and the basis function matrix, a second-order performance function is constructed; wherein, with respect to the intermediate-order control point matrix P k+1 The specific form of the k+1-order optimization problem is as follows, that is, the second-order performance function can be expressed as:

[0105]

[0106] Among them, J(P k+1 ) is the second-order performance function; Pk+1 is the intermediate order control point matrix; is a time derivative; B k+1 (t) is the basis function matrix corresponding to the intermediate-order control point matrix, and the order of this matrix is ​​k+1; T is the matrix transpose; A eq (k+10 is the constraint matrix on the left side of the kinematic equation corresponding to the intermediate-order control point matrix, and the order of this matrix is ​​k+1; c eq A is the constraint matrix on the right side of the kinematic equation; ie The constraint matrix on the left side of the preset kinematic inequality can be expressed as is the second optimization inequality constraint matrix, and the order of the matrix is ​​k+1.

[0107] It is worth mentioning that the principle of the matrix construction steps of the constraint matrix and the basis function matrix on the left side of the kinematic equation corresponding to the intermediate order control point matrix is ​​consistent with the principle of the matrix construction steps of the constraint matrix and the basis function matrix on the left side of the kinematic equation corresponding to the initial order control point matrix, and the present invention will not go into details; wherein, the basis function matrix corresponding to the intermediate order control point matrix can be expressed as:

[0108]

[0109] Among them, B k+1 (t) is the basis function matrix corresponding to the intermediate order control point matrix, the order of this matrix is ​​k+1, representing a k+1 dimensional Berstein matrix; is the first basis function in the basis function matrix corresponding to the intermediate order control point matrix; is the second basis function in the basis function matrix corresponding to the intermediate order control point matrix; is the k+2th basis function in the basis function matrix corresponding to the intermediate order control point matrix; T is the matrix transpose.

[0110] Furthermore, under the condition that the motion range is not exceeded and the 1-j order smooth start requirements are met, find P k+1 In order to make the motion performance index function reach the minimum value, the preset solution algorithm is used to solve the second-order performance function to determine the second optimal control point matrix and the second performance index. That is, by solving the above k+1 order optimization problem (second-order performance function), the k+1 order optimal control point matrix (second optimal control point matrix) is obtained. ) and the k+1-order suboptimal performance index (the second performance index), which can be expressed as

[0111] Further, the second performance index is compared with the first performance index; if the first performance index is less than or equal to the second performance index, the order of the first optimization control point matrix is ​​used as the target control order; if the first performance index is greater than the second performance index, the second optimization control point matrix is ​​used as the new first optimization control point matrix, the second performance index is used as the new first performance index, and the step of determining the upgraded matrix based on the order of the first optimization control point matrix is ​​jumped to execution until the first performance index is less than or equal to the second performance index; the order of the first optimization control point matrix determined when the first performance index is less than or equal to the second performance index is used as the target control order.

[0112] Step 104: Determine the target Bezier motion curve of the flying thorn crystal according to the target control order, the kinematic constraint matrix and the control point basis function matrix.

[0113] Specifically, the initial control point matrix and the basis function matrix corresponding to the initial control point matrix are used to construct the initial Bezier curve; wherein the initial Bezier curve can be expressed as:

[0114]

[0115] Where f(t) is the initial Bezier curve; p i is the coordinate of the i+1th initial control point corresponding to the initial Bezier curve; is the i+1th basis function in the basis function matrix corresponding to the initial control point matrix; P n is the initial control point matrix; B n (t) is the basis function matrix corresponding to the initial control point matrix; t is the stage motion time;

[0116] Further, based on the target control order and the order of the initial control point matrix, a target raised-order matrix is ​​determined; the target raised-order matrix is ​​an n+1-dimensional raised-order matrix with n+1 rows and m+1 columns, which represents the initial control point matrix P n The operation process of increasing the number of control points from n+1 to m+1 requires traversing the elements of each row and column in the matrix when constructing the matrix. The calculation formula of the element located in the x+1th row and y+1th column, that is, the construction process of the target raised-order matrix, can be expressed as:

[0117]

[0118] in, is the element in the x+1th row and y+1th column of the target raised-order matrix; n is the order of the initial control point matrix; m is the target control order; x is the xth row of the target raised-order matrix; y is the yth column of the target raised-order matrix.

[0119] Furthermore, a raised-order control point matrix is ​​constructed according to the target raised-order matrix and the first optimized control point matrix; wherein the first optimized control point matrix used to construct the raised-order control point matrix is ​​the first optimized control point matrix obtained in the first matrix order raising process, that is, the first optimized control point matrix obtained by solving the first-order performance function based on the initial-order control point matrix; the raised-order control point matrix can be expressed as: Q m is the raised-order control point matrix, is the first optimized control point matrix, is the target raised rank matrix.

[0120] Furthermore, based on the column vectors in the raised-order control point matrix and the initial Bezier curve, a multi-time point basis function matrix is ​​determined, wherein the construction process of the multi-time point basis function matrix can be expressed as:

[0121]

[0122] in, is the multi-time point basis function matrix (multi-time point Berstein matrix); is the first basis function matrix in the multi-time point basis function matrix; is the m+1th basis function matrix in the multi-time point basis function matrix; are multiple time points corresponding to the multi-time point basis function; is the vector 2-norm; q j is the column vector of the jth column in the ascending control point matrix; f(t) is the initial Bezier curve; It is the second basis function matrix in the multi-time point basis function matrix.

[0123] Furthermore, based on the order of the initial control point matrix, the kinematic inequality right-hand constraint matrix is ​​used to construct the initial optimization inequality constraint matrix; wherein the initial optimization inequality constraint matrix represents the initial control point matrix p n All control points in the ie Restrictions can be expressed as: is the initial optimization inequality constraint matrix composed of n+1 right-hand constraint matrices of kinematic inequalities. The order of this matrix is ​​n, representing the n-order optimization inequality constraint matrix. ie is the constraint matrix on the right side of the kinematic inequality.

[0124] Furthermore, the target order optimization performance function is constructed by using the multi-time point basis function matrix, the initial control point matrix, the basis function matrix corresponding to the initial control point matrix, the left-side constraint matrix of the kinematic equation, the right-side constraint matrix of the kinematic equation, the left-side constraint matrix of the preset kinematic inequality, the target order matrix, and the initial optimization inequality constraint matrix; wherein the target order optimization performance function can be expressed as:

[0125]

[0126] Among them, J(P n ) is the target order optimization performance function; P n is the initial control point matrix, the order of the matrix is ​​n; is a time derivative; B n (t) is the basis function matrix corresponding to the initial control point matrix, the order of this matrix is ​​n; T is the transpose of the matrix; A eq (n) is the constraint matrix on the left side of the kinematic equation corresponding to the initial control point matrix, and the order of this matrix is ​​n; c eq A is the constraint matrix on the right side of the kinematic equation; ie The constraint matrix on the left side of the preset kinematic inequality; is the target raised rank matrix; is the multi-time point basis function matrix; is the initial optimization inequality constraint matrix, the order of the matrix is ​​n; To preset the tolerance distance for the order increase, set the value between [0,1], which represents the maximum distance between the Bezier curve and the constraint boundary during optimization, to avoid excessive optimization leading to violation of constraints.

[0127] Furthermore, by solving the above m-order optimization problem (target order optimization performance function), the optimal control point matrix (target control point matrix) is obtained. ), that is, the preset solution algorithm is used to solve the target order optimization performance function and determine the target control point matrix. The process can be expressed as:

[0128] For further information, see Figure 3-Figure 5 , according to the basis function matrix corresponding to the target control point matrix and the initial control point matrix, the target Bezier motion curve (motion path) of the flying thorn crystal is determined; the target Bezier motion curve obtained by the above-mentioned step of temporarily mapping the low-order control points to high-order control points for optimization can meet the strict motion requirements and make the motion path more concise and easy to use; wherein, the target Bezier motion curve can be expressed as: t∈[0,1], is the target Bezier motion curve, is the target control point matrix, B n(t) is the basis function matrix corresponding to the initial control point matrix, and t is the stage motion time.

[0129] In an embodiment of the present invention, the present invention provides a flying thorn crystal path planning method. When it is necessary to plan the path of the flying thorn crystal, firstly, the coordinates of multiple control points of the flying thorn crystal are initialized to generate multiple initial control point coordinates; then, according to the initial control point coordinates and the preset massive transfer motion data, a kinematic constraint matrix and a control point basis function matrix are constructed; a preset solution algorithm is used to determine the target control order according to the kinematic constraint matrix, the control point basis function matrix and the initial control point coordinates; finally, the target Bezier motion curve of the flying thorn crystal is determined according to the target control order, the kinematic constraint matrix and the control point basis function matrix; based on the above scheme, a preset solution algorithm is used to determine the target control order according to the constructed kinematic constraint matrix, the control point basis function matrix and the initial control point coordinates obtained after initialization, and the process of determining the target Bezier motion curve of the flying thorn crystal in combination with the kinematic constraint matrix and the control point basis function matrix does not need to set a fixed control order in advance, and a low-order optimal Bezier curve can be obtained even under strict motion requirements, making the Bezier path more concise and easy to use.

[0130] See also Figure 6 , Figure 6 A flowchart of the steps of another flying spike crystal path planning method provided by an embodiment of the present invention.

[0131] The present invention provides a flying thorn crystal path planning method, comprising:

[0132] Step 601, in response to a planning request, initialize the coordinates of multiple control points of the flying thorn crystal to generate multiple initial control point coordinates.

[0133] In this embodiment, the specific implementation process of step 601 is similar to step 101 and will not be repeated here.

[0134] Step 602: Use the coordinates of each initial control point to construct an initial control point matrix.

[0135] It should be noted that the initial control point matrix can be expressed as: P n =[p 0 ,p 1 ,…,p n ], where P n The initial control point coordinates p 0 ,p 1 ,…,p n The initial control point matrix, p 0 is the coordinate of the first initial control point, p 1 is the coordinate of the second initial control point, p nis the coordinate of the nth initial control point.

[0136] Step 603: Using the basis functions corresponding to the coordinates of each initial control point, construct the constraint matrix and basis function matrix on the left side of the kinematic equation corresponding to the initial control point matrix.

[0137] The preset massive transfer motion data includes the path motion starting point, the path motion end point, the velocity vector restriction data, and the upper and lower limit data of the motion space; the kinematic constraint matrix includes the constraint matrix on the left side of the kinematic equation, the constraint matrix on the right side of the kinematic equation, and the constraint matrix on the right side of the kinematic inequality; the control point basis function matrix includes the initial control point matrix and the basis function matrix corresponding to the initial control point matrix.

[0138] Step 604: construct a constraint matrix on the right side of the kinematic equation according to the path motion starting point, path motion end point and velocity vector restriction data.

[0139] It should be noted that the constraint matrix on the right side of the kinematic equation can be expressed as:

[0140]

[0141] Among them, c eq is the constraint matrix on the right side of the kinematic equation; x 0 is the coordinate of the starting point of the movement in the X-axis direction; z 0 is the coordinate of the starting point of the movement in the Z-axis direction; e is the coordinate of the end point of the movement in the X-axis direction; z e is the coordinate of the end point of the movement in the Z-axis direction; ve x is the limit value of the velocity vector of the thorn crystal on the X axis at the end point of the motion path; ze The limit value of the velocity vector of the thorn crystal on the Z axis at the end point of the motion path.

[0142] Step 605: Construct the right-hand side constraint matrix of the kinematic inequality through the upper and lower limit data of the motion space.

[0143] It should be noted that the right-hand constraint matrix of the kinematic inequality is constructed through the upper and lower limit data of the motion space. The right-hand constraint matrix of the kinematic inequality can be expressed as: ie =[x M -x m z M -z m ] T , where c ie is the constraint matrix on the right side of the kinematic inequality, x M is the upper limit coordinate of the motion space in the X-axis direction, x m is the lower limit coordinate of the motion space in the X-axis direction, z M is the upper limit coordinate of the motion space in the Z-axis direction, zm It is the lower limit coordinate of the motion space in the Z-axis direction.

[0144] Step 606: Determine the target control order using a preset solution algorithm based on the kinematic constraint matrix, the control point basis function matrix and the coordinates of each initial control point.

[0145] Further, step 606 may include the following sub-steps:

[0146] S61, using the initial control point matrix as the initial order control point matrix;

[0147] S62, based on the order of the initial order control point matrix, using the right-hand side constraint matrix of the kinematic inequality to construct a first optimization inequality constraint matrix;

[0148] S63, constructing a first-order performance function according to the first optimized inequality constraint matrix, the right-side constraint matrix of the kinematic equation, the left-side constraint matrix of the preset kinematic inequality, the initial order control point matrix, the left-side constraint matrix of the kinematic equation corresponding to the initial order control point matrix, and the basis function matrix;

[0149] S64, using a preset solution algorithm to solve the first-order performance function to determine a first optimization control point matrix and a first performance index;

[0150] S65, determining an increased-order matrix based on the order of the first optimized control point matrix;

[0151] S66, determining an intermediate order control point matrix according to the raised order matrix and the first optimized control point matrix;

[0152] S67, based on the order of the intermediate order control point matrix, using the right-hand side constraint matrix of the kinematic inequality to construct a second optimization inequality constraint matrix;

[0153] S68, constructing a second-order performance function according to the second optimized inequality constraint matrix, the right-side constraint matrix of the kinematic equation, the left-side constraint matrix of the preset kinematic inequality, the intermediate-order control point matrix, the left-side constraint matrix of the kinematic equation corresponding to the intermediate-order control point matrix, and the basis function matrix;

[0154] S69, using a preset solution algorithm to solve the second-order performance function to determine a second optimization control point matrix and a second performance index;

[0155] S610, comparing the second performance index and the first performance index;

[0156] S611. If the first performance index is less than or equal to the second performance index, the order of the first optimization control point matrix is ​​used as the target control order.

[0157] Optionally, it also includes:

[0158] If the first performance index is greater than the second performance index, the second optimized control point matrix is ​​used as a new first optimized control point matrix, and the second performance index is used as a new first performance index;

[0159] Determine the second performance index by using a preset solving algorithm according to the new first optimization control point matrix, the right-side constraint matrix of the kinematic equation, the left-side constraint matrix of the preset kinematic inequality, and the order of the new first optimization control point matrix until the first performance index is less than or equal to the second performance index;

[0160] The order of the first optimization control point matrix determined when the first performance index is less than or equal to the second performance index is used as the target control order.

[0161] It should be noted that if the first performance indicator is greater than the second performance indicator, the second optimization control point matrix is ​​used as the new first optimization control point matrix and the second performance indicator is used as the new first performance indicator; and the process jumps to S65 until the first performance indicator is less than or equal to the second performance indicator; the order of the first optimization control point matrix determined when the first performance indicator is less than or equal to the second performance indicator is used as the target control order.

[0162] Step 607: Determine the target Bezier motion curve of the flying thorn crystal according to the target control order, the kinematic constraint matrix and the control point basis function matrix.

[0163] Further, step 607 may include the following sub-steps:

[0164] S71, constructing an initial Bezier curve using an initial control point matrix and a basis function matrix corresponding to the initial control point matrix;

[0165] S72, determining a target raised-order matrix based on the target control order and the order of the initial control point matrix;

[0166] S73, constructing a raised-order control point matrix according to the target raised-order matrix and the first optimized control point matrix;

[0167] S74, determining a multi-time point basis function matrix based on the column vectors in the raised-order control point matrix and the initial Bezier curve;

[0168] S75. Based on the order of the initial control point matrix, an initial optimization inequality constraint matrix is ​​constructed using the right-hand side constraint matrix of the kinematic inequality;

[0169] S76, using a multi-time point basis function matrix, an initial control point matrix, a basis function matrix corresponding to the initial control point matrix, a left-side constraint matrix of a kinematic equation, a right-side constraint matrix of a kinematic equation, a left-side constraint matrix of a preset kinematic inequality, a target order-raising matrix, and an initial optimization inequality constraint matrix, to construct a target order optimization performance function;

[0170] S77, using a preset solution algorithm to solve the target order optimization performance function and determine the target control point matrix;

[0171] S78. Determine the target Bezier motion curve of the flying thorn crystal according to the basis function matrix corresponding to the target control point matrix and the initial control point matrix.

[0172] It should be noted that the target order optimizes the performance function, specifically:

[0173]

[0174] Among them, J(P n ) is the target order optimization performance function; P n is the initial control point matrix, which corresponds to the n-order control point; is a time derivative; B n 9t) is the basis function matrix corresponding to the initial control point matrix, and the order of the matrix is ​​n; T is the transpose of the matrix; A eq 9n) is the constraint matrix on the left side of the kinematic equation corresponding to the initial control point matrix, and the order of this matrix is ​​n; c eq A is the constraint matrix on the right side of the kinematic equation; ie The constraint matrix on the left side of the preset kinematic inequality; is the target raised rank matrix; is the multi-time point basis function matrix; is the initial optimization inequality constraint matrix, the order of the matrix is ​​n; It is the preset step-up fault tolerance distance.

[0175] For example, B1. Assume that the number of the coordinates of the multiple control points of the flying thorn crystal is 8, and set the motion information of this mass transfer, that is, preset the mass transfer motion data, including the motion starting point u of the path 0 =[0,1] T 、Movement end point u e =[3,0] T , the lower limit of the motion space u m =[-1,-1] T and the upper limit u M =[4,2] T , the motion process is required to maintain a 2nd order smooth start, and the velocity vector of the uniform pricking stage is [1,-1] TIn particular, the initial control order n=7 is set, that is, the order of the initial control point matrix is ​​7.

[0176] Furthermore, a kinematic constraint matrix is ​​constructed based on the set motion information, including the constraint matrix A on the left side of the kinematic equation eq (k), the constraint matrix A on the left side of the kinematic inequality ie 、The constraint matrix c on the right side of the kinematic equation eq 、The right-hand constraint matrix c of the kinematic inequality ie , the matrix expressions are as follows:

[0177]

[0178] Further, B2. Set the initial Bezier curve to in, P 7 =[p 0 ,p 1 ,…,p 7 ], and then construct the motion performance index function J(P 7 )for Set the optimization iteration count to k=7 and set the up-order error tolerance distance

[0179] Furthermore, B3. Construct a k-order optimization inequality constraint matrix It is expressed as Based on the above basic construction, the control point matrix P k The k-order optimization problem of is solved and the k-order optimal control point matrix is ​​obtained. And the k-order suboptimal performance index J k ; About the control point matrix P k The k-order optimization problem is specifically:

[0180]

[0181] Among them, J(P k ) is the control point matrix P k k-order optimization problem of P k is the k-order control point matrix; is the first time derivative; B k (t0 is the basis function matrix corresponding to the k-order control point matrix, the order of the matrix is ​​k-order; T is the matrix transpose; A eq (k) is the constraint matrix on the left side of the kinematic equation corresponding to the k-order control point matrix, and the order of this matrix is ​​k-order; c eq A is the constraint matrix on the right side of the kinematic equation; ie The constraint matrix on the left side of the preset kinematic inequality can be expressed as is the k-order optimization inequality constraint matrix.

[0182] Further, B4. Construct a k+1-order ascending control point matrix P k+1 , which can be expressed as: Construct a k+1 order optimization inequality constraint matrix, which can be expressed as; Construct the control point matrix P k+1 The k+1 order optimization problem is solved and the k+1 order optimal control point matrix is ​​obtained. And the k+1-order suboptimal performance index J k+1 , control point matrix P k+1 The k+1-order optimization problem is specifically:

[0183]

[0184] Among them, J(P k+1 ) is the control point matrix P k+1 k+1 order optimization problem of k+1 is the k+1 order control point matrix; is the first time derivative; B k+1 (t) is the basis function matrix corresponding to the k+1-order control point matrix, and the order of this matrix is ​​k+1; T is the matrix transpose; A eq (k+1) is the constraint matrix on the left side of the kinematic equation corresponding to the k+1-order control point matrix, and the order of this matrix is ​​k+1; c eq A is the constraint matrix on the right side of the kinematic equation; ie The constraint matrix on the left side of the preset kinematic inequality can be expressed as is the k+1-order optimization inequality constraint matrix.

[0185] Furthermore, B5. If J k <J k+1 , then set k = k + 1, and re-execute steps B3 to B4, otherwise the optimal order is m = k. At this time, the optimal order is m = 14, and the initial control point matrix P is re-constructed. n The m-order optimization problem is:

[0186]

[0187] in, is the 7th-order optimization inequality constraint matrix, specifically is the multi-time point Berstein matrix, specifically:

[0188]

[0189] in, is the vector 2-norm; are multiple time points corresponding to the multi-time point basis function matrix; q j is the raised control point matrix Q 14 The column vector of the jth column in the ascending control point matrix Q 14 It can be expressed as:

[0190]

[0191] Furthermore, by solving the above initial control point matrix P n The m-order optimization problem is obtained by raising the optimal control point matrix And the optimal Bezier motion curve Specifically:

[0192]

[0193] It is worth mentioning that see Figure 7 According to the calculation and optimization process of the above example, a low-order optimal Bezier curve that meets the strict motion requirements is obtained, which makes the motion path more concise and easy to use, so that the flying thorn crystal can perform flying thorn crystal mass transfer according to the obtained optimal Bezier motion curve. Compared with the PnP process (Pick-and-Place), the flying thorn crystal transfer process can realize the smooth movement of the needle at the start and the uniform motion during the crystal pricking, which greatly reduces the flutter phenomenon of the working platform. In addition, the source substrate and the target substrate only need to move synchronously at a uniform speed throughout the crystal pricking process to realize chip positioning, which simplifies the positioning process, improves the transfer efficiency, and has high application potential. Among them, the calculation and optimization processes of the above examples are all run on the computing software MATLAB.

[0194] As a comparison of technical effects, it can be combined with existing technologies for reference. At present, traditional chip transfer technology mostly adopts precise pick-and-release (PnP) methods, including source substrate positioning, chip picking, chip positioning, chip mounting and other important processes. With the increasing demand for chips in the display industry, the traditional PnP process can no longer meet the requirements of high transfer rates for Mini / Micro LED chip transfer. In contrast, the "flying thorn crystal" transfer technology, which is improved based on the traditional needle-piercing transfer technology, transforms static positioning thinking into dynamic positioning thinking. The LED chip, source substrate (blue film) and target substrate maintain a synchronized feed speed at the same vertical position. The transfer component moves above the chip. After the thorn needle head on the transfer component maintains the same lateral movement speed as the chip, it pushes the chip downward and peels it off to the target substrate, thereby realizing the dynamic transfer of the chip.

[0195] Furthermore, chip transfer involves the positioning of the source substrate, target substrate and transfer components. Therefore, all chip transfer processes must take into account transfer performance, working environment and other conditions, and reasonably design the movement path, speed, acceleration, etc. of the moving components (i.e., path optimization). For the flying thorn crystal transfer process, the movement path of the thorn needle must not only have a smooth transition and meet the characteristics of smooth start / stop, but also ensure that the transfer chip maintains the same moving speed as the source substrate to avoid scratching the chip.

[0196] Common path optimization algorithms include line-circle interpolation and spiral curve interpolation. They use the most basic straight lines, circles, spiral curves and other graphics to connect several given path points to obtain the entire motion path, but these curves often have disadvantages such as discontinuous paths and unstable switching. Bezier curves are also polynomial curves, which are obtained by designing control points. Since the control points do not need to be strictly restricted to the motion path, Bezier curves have the advantages of higher ductility and freer paths. Path optimization based on Bezier curves has also become one of the mainstream path optimization algorithms. However, the order (complexity) of the Bezier curve is directly related to the number of control points. If the number of control points is too small, the freedom of curve design will be reduced when the motion requirements are strict; if the number of control points is too large, the obtained curve will be too complex and difficult to apply. Therefore, while optimizing the Bezier curve, it is also necessary to further optimize the control order (upgraded order optimization) to obtain the optimal motion path at the minimum curve complexity cost. At present, there is still no research on the Bezier curve upgraded order optimization algorithm for mass transfer technology.

[0197] For the above questions, please refer to Figure 8 The present invention proposes a flying thorn crystal path planning method, which calculates a stable, low-jitter Bezier motion path according to the motion requirements of chip mass transfer, thereby improving the chip transfer efficiency while ensuring the thorn crystal accuracy; specifically, A1. Set the motion information of this mass transfer, and construct a kinematic constraint matrix based on the set motion information; A2. Set the initial Bezier curve f n (t), construct the initial control point matrix P n , construct the motion performance index function J(P n ), set the optimization iteration count to k = n, and set the order tolerance distance A3. Constructing the inequality constraint matrix for order optimization Construct the control point matrix P k Solve the k-order optimization problem and get the k-order optimal control point matrix And the k-order suboptimal performance index J k ; A4. Construct k+1 order ascending control point matrix P k+1 , construct k+1 order optimization inequality constraint matrix Construct the control point matrix P k+1 Solve the k+1 order optimization problem and obtain the k+1 order optimal control point matrix And the k+1-order suboptimal performance index J k+1 ; A5. If J k <J k+1 , then set k = k + 1 and re-execute steps A3 to A4; otherwise, the optimal order is obtained. Reconstruct the initial control point matrix P n The m-order optimization problem is solved to obtain the optimal control point matrix Obtaining the optimal Bezier motion curve based on the optimal control point matrix

[0198] In summary, the present invention aims at the motion requirements of the flying thorn crystal mass transfer technology, constructs and sets a kinematic constraint matrix that meets the requirements; for different control orders, constructs Bezier curves of corresponding orders and motion performance index functions, constructs Bezier curve optimization problems of different orders, solves and obtains the optimal control point matrix of the corresponding order, and obtains the optimal control order after multiple iterations; based on the order-raising calculation, the initial order control point matrix is ​​mapped, and the constraint conditions are reconstructed according to the order-raising fault tolerance distance to obtain the optimal order Bezier curve optimization problem, and the order-raising optimal control point matrix and the order-raising optimal Bezier curve are solved. At the same time, the existing path optimization methods based on line circle and spiral curve interpolation must pass through the path points to be optimized, the extensibility and degree of freedom of the motion path are small, and the motion path is not necessarily smooth, and the path switching is unstable. Compared with the above-mentioned technologies, the motion path in the present invention does not require to pass through the control points to be optimized. Therefore, the motion path has the advantages of multi-order continuity of expression, highly smooth contour, high degree of freedom, and high ductility, which ensures the stability of the chip mass transfer process; secondly, other existing Bezier path optimization methods can only optimize the Bezier curve based on a set fixed control order. When the motion requirements are too strict, the control order can only be set to a larger value, thereby obtaining a more complex and difficult to apply high-order Bezier path. In contrast, the present invention temporarily maps low-order control points to high-order control points for optimization, and obtains a low-order optimal Bezier curve that meets stringent motion requirements, making the motion path more concise and easy to use.

[0199] In an embodiment of the present invention, the present invention provides a flying thorn crystal path planning method. When it is necessary to plan the path of the flying thorn crystal, firstly, the coordinates of multiple control points of the flying thorn crystal are initialized to generate multiple initial control point coordinates; then, according to the initial control point coordinates and the preset massive transfer motion data, a kinematic constraint matrix and a control point basis function matrix are constructed; a preset solution algorithm is used to determine the target control order according to the kinematic constraint matrix, the control point basis function matrix and the initial control point coordinates; finally, the target Bezier motion curve of the flying thorn crystal is determined according to the target control order, the kinematic constraint matrix and the control point basis function matrix; based on the above scheme, a preset solution algorithm is used to determine the target control order according to the constructed kinematic constraint matrix, the control point basis function matrix and the initial control point coordinates obtained after initialization, and the process of determining the target Bezier motion curve of the flying thorn crystal in combination with the kinematic constraint matrix and the control point basis function matrix does not need to set a fixed control order in advance, and a low-order optimal Bezier curve can be obtained even under strict motion requirements, making the Bezier path more concise and easy to use.

[0200] See also Fig. 9 , Fig. 9 A structural block diagram of a flying spike crystal path planning device provided in an embodiment of the present invention.

[0201] The present invention provides a flying thorn crystal path planning device, comprising:

[0202] The response module 901 is used to respond to the planning request, initialize the coordinates of multiple control points of the flying thorn crystal, and generate multiple initial control point coordinates;

[0203] A matrix construction module 902 is used to construct a kinematic constraint matrix and a control point basis function matrix according to the coordinates of each initial control point and the preset mass transfer motion data;

[0204] A control order determination module 903 is used to determine a target control order according to a kinematic constraint matrix, a control point basis function matrix and coordinates of each initial control point using a preset solution algorithm;

[0205] The target path determination module 904 is used to determine the target Bezier motion curve of the flying thorn crystal according to the target control order, the kinematic constraint matrix and the control point basis function matrix.

[0206] Further, the preset mass transfer motion data includes the path motion starting point, the path motion end point, the velocity vector restriction data, and the upper and lower limit data of the motion space; the kinematic constraint matrix includes the left constraint matrix of the kinematic equation, the right constraint matrix of the kinematic equation, and the right constraint matrix of the kinematic inequality; the control point basis function matrix includes the initial control point matrix and the basis function matrix corresponding to the initial control point matrix; the matrix construction module 902 is specifically used for:

[0207] Using the coordinates of each initial control point, construct an initial control point matrix;

[0208] Using the basis functions corresponding to the coordinates of each initial control point, the constraint matrix and basis function matrix on the left side of the kinematic equation corresponding to the initial control point matrix are constructed;

[0209] According to the path motion starting point, path motion end point and velocity vector restriction data, the right-hand constraint matrix of the kinematic equation is constructed;

[0210] The constraint matrix on the right side of the kinematic inequality is constructed using the upper and lower limit data of the motion space.

[0211] Furthermore, the control order determination module 903 is specifically used to:

[0212] The initial control point matrix is ​​used as the initial order control point matrix;

[0213] Based on the order of the initial order control point matrix, the first optimization inequality constraint matrix is ​​constructed using the right-hand side constraint matrix of the kinematic inequality;

[0214] Constructing a first-order performance function according to the first optimization inequality constraint matrix, the right-side constraint matrix of the kinematic equation, the left-side constraint matrix of the preset kinematic inequality, the initial order control point matrix, the left-side constraint matrix of the kinematic equation corresponding to the initial order control point matrix, and the basis function matrix;

[0215] Solving the first-order performance function using a preset solution algorithm to determine a first optimization control point matrix and a first performance index;

[0216] Determining an increased-order matrix based on the order of the first optimized control point matrix;

[0217] Determine an intermediate order control point matrix according to the raised order matrix and the first optimized control point matrix;

[0218] Based on the order of the intermediate order control point matrix, the second optimization inequality constraint matrix is ​​constructed using the right-hand side constraint matrix of the kinematic inequality;

[0219] Constructing a second-order performance function according to the second optimization inequality constraint matrix, the right-side constraint matrix of the kinematic equation, the left-side constraint matrix of the preset kinematic inequality, the intermediate-order control point matrix, the left-side constraint matrix of the kinematic equation corresponding to the intermediate-order control point matrix, and the basis function matrix;

[0220] Solving the second-order performance function using a preset solution algorithm to determine a second optimization control point matrix and a second performance index;

[0221] comparing the second performance indicator to the first performance indicator;

[0222] If the first performance index is less than or equal to the second performance index, the order of the first optimized control point matrix is ​​used as the target control order.

[0223] Optionally, it also includes:

[0224] The first module is used for taking the second optimization control point matrix as a new first optimization control point matrix and taking the second performance index as a new first performance index if the first performance index is greater than the second performance index;

[0225] The second module is used to determine the second performance index by using a preset solution algorithm according to the new first optimization control point matrix, the right-side constraint matrix of the kinematic equation, the left-side constraint matrix of the preset kinematic inequality and the order of the new first optimization control point matrix, until the first performance index is less than or equal to the second performance index;

[0226] The third module is used to use the order of the first optimization control point matrix determined when the first performance index is less than or equal to the second performance index as the target control order.

[0227] Furthermore, the target path determination module 904 is specifically configured to:

[0228] An initial Bezier curve is constructed using an initial control point matrix and a basis function matrix corresponding to the initial control point matrix;

[0229] Determine a target raised-order matrix based on the target control order and the order of the initial control point matrix;

[0230] Constructing a raised-order control point matrix according to the target raised-order matrix and the first optimized control point matrix;

[0231] Determine a multi-time point basis function matrix based on the column vectors in the raised-order control point matrix and the initial Bezier curve;

[0232] Based on the order of the initial control point matrix, the initial optimization inequality constraint matrix is ​​constructed using the right-hand constraint matrix of the kinematic inequality;

[0233] The target order optimization performance function is constructed by using a multi-time point basis function matrix, an initial control point matrix, a basis function matrix corresponding to the initial control point matrix, a left-side constraint matrix of the kinematic equation, a right-side constraint matrix of the kinematic equation, a left-side constraint matrix of the preset kinematic inequality, a target order matrix, and an initial optimization inequality constraint matrix;

[0234] A preset solution algorithm is used to solve the target order optimization performance function and determine the target control point matrix;

[0235] According to the basis function matrix corresponding to the target control point matrix and the initial control point matrix, the target Bezier motion curve of the flying thorn crystal is determined.

[0236] Optionally, the target order optimizes the performance function, specifically:

[0237]

[0238] Among them, J(P n ) is the target order optimization performance function; P n is the initial control point matrix, which corresponds to the n-order control point; is a time derivative; B n (t) is the basis function matrix corresponding to the initial control point matrix, the order of this matrix is ​​n; T is the transpose of the matrix; A eq (n) is the constraint matrix on the left side of the kinematic equation corresponding to the initial control point matrix, and the order of this matrix is ​​n; c eq A is the constraint matrix on the right side of the kinematic equation; ie The constraint matrix on the left side of the preset kinematic inequality; is the target raised rank matrix; is the multi-time point basis function matrix; is the initial optimization inequality constraint matrix, the order of the matrix is ​​n; It is the preset step-up fault tolerance distance.

[0239] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0240] An embodiment of the present invention further provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory; when the computer program is executed by the processor, the processor executes the steps of the flying spike path planning method of any of the above embodiments.

[0241] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, the steps of the flying thorn crystal path planning method as in any of the above embodiments are implemented.

[0242] An embodiment of the present invention further provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the flying spike path planning method of any of the above embodiments.

[0243] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0244] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0245] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A flying thorn crystal path planning method, characterized in that: include: In response to the planning request, the coordinates of multiple control points of the flying thorn crystal are initialized to generate multiple initial control point coordinates; Constructing a kinematic constraint matrix and a control point basis function matrix according to the coordinates of each of the initial control points and the preset massive transfer motion data; Determine the target control order according to the kinematic constraint matrix, the control point basis function matrix and the coordinates of each initial control point by using a preset solution algorithm; The target Bezier motion curve of the flying spike crystal is determined according to the target control order, the kinematic constraint matrix and the control point basis function matrix.

2. The flying thorn crystal path planning method according to claim 1 is characterized in that: The preset mass transfer motion data includes path motion starting point, path motion end point, velocity vector restriction data, and motion space upper and lower limit data; the kinematic constraint matrix includes a left-side constraint matrix of the kinematic equation, a right-side constraint matrix of the kinematic equation, and a right-side constraint matrix of the kinematic inequality; the control point basis function matrix includes an initial control point matrix and a basis function matrix corresponding to the initial control point matrix; The step of constructing a kinematic constraint matrix according to the coordinates of each of the initial control points and the preset massive transfer motion data comprises: Using the coordinates of each of the initial control points, construct an initial control point matrix; Using the basis functions corresponding to the coordinates of each of the initial control points, a constraint matrix and a basis function matrix on the left side of the kinematic equation corresponding to the initial control point matrix are constructed; According to the path motion starting point, path motion end point and velocity vector restriction data, the right-hand constraint matrix of the kinematic equation is constructed; The constraint matrix on the right side of the kinematic inequality is constructed using the upper and lower limit data of the motion space.

3. The flying thorn crystal path planning method according to claim 2 is characterized in that: The step of using a preset solution algorithm to determine the target control order according to the kinematic constraint matrix, the control point basis function matrix and the coordinates of each initial control point includes: Using the initial control point matrix as the initial order control point matrix; Based on the order of the initial order control point matrix, a first optimization inequality constraint matrix is ​​constructed using the right-hand side constraint matrix of the kinematic inequality; Constructing a first-order performance function according to the first optimized inequality constraint matrix, the right-side constraint matrix of the kinematic equation, the left-side constraint matrix of the preset kinematic inequality, the initial order control point matrix, and the left-side constraint matrix of the kinematic equation corresponding to the initial order control point matrix and the basis function matrix; Using the preset solution algorithm to solve the first-order performance function, and determine a first optimization control point matrix and a first performance index; Determining an increased-order matrix based on the order of the first optimized control point matrix; Determining an intermediate order control point matrix according to the raised order matrix and the first optimized control point matrix; Based on the order of the intermediate-order control point matrix, a second optimization inequality constraint matrix is ​​constructed using the right-hand side constraint matrix of the kinematic inequality; Constructing a second-order performance function according to the second optimized inequality constraint matrix, the right-side constraint matrix of the kinematic equation, the left-side constraint matrix of the preset kinematic inequality, the intermediate-order control point matrix, and the left-side constraint matrix of the kinematic equation corresponding to the intermediate-order control point matrix and the basis function matrix; Solving the second-order performance function using the preset solving algorithm to determine a second optimization control point matrix and a second performance index; comparing the second performance indicator with the first performance indicator; If the first performance index is less than or equal to the second performance index, the order of the first optimization control point matrix is ​​used as the target control order.

4. The flying thorn crystal path planning method according to claim 3 is characterized in that: Also includes: If the first performance index is greater than the second performance index, taking the second optimization control point matrix as a new first optimization control point matrix and taking the second performance index as a new first performance index; Determine a second performance index by using the preset solution algorithm according to the new first optimization control point matrix, the right-side constraint matrix of the kinematic equation, the left-side constraint matrix of the preset kinematic inequality, and the order of the new first optimization control point matrix until the first performance index is less than or equal to the second performance index; The order of the first optimization control point matrix determined when the first performance index is less than or equal to the second performance index is used as the target control order.

5. The flying thorn crystal path planning method according to claim 2 is characterized in that: The step of determining the target Bezier motion curve of the flying thorn crystal according to the target control order, the kinematic constraint matrix and the control point basis function matrix comprises: Constructing an initial Bezier curve using the initial control point matrix and a basis function matrix corresponding to the initial control point matrix; Determining a target raised-order matrix based on the target control order and the order of the initial control point matrix; Constructing a raised-order control point matrix according to the target raised-order matrix and the first optimized control point matrix; Determine a multi-time point basis function matrix based on the column vectors in the raised-order control point matrix and the initial Bezier curve; Based on the order of the initial control point matrix, an initial optimization inequality constraint matrix is ​​constructed using the right-hand constraint matrix of the kinematic inequality; The target order optimization performance function is constructed by using the multi-time point basis function matrix, the initial control point matrix, the basis function matrix corresponding to the initial control point matrix and the left-side constraint matrix of the kinematic equation, the right-side constraint matrix of the kinematic equation, the left-side constraint matrix of the preset kinematic inequality, the target raised-order matrix, and the initial optimization inequality constraint matrix; Using the preset solution algorithm to solve the target order optimization performance function to determine the target control point matrix; The target Bezier motion curve of the flying thorn crystal is determined according to the basis function matrix corresponding to the target control point matrix and the initial control point matrix.

6. The flying thorn crystal path planning method according to claim 5 is characterized in that: The target order optimization performance function is specifically: Among them, J(P n ) is the target order optimization performance function; P n is the initial control point matrix, the order of this matrix is ​​n, that is, it corresponds to n-order control points; is a time derivative; B n (t) is the basis function matrix corresponding to the initial control point matrix, the order of this matrix is ​​n; T is the transpose of the matrix; A eq (n) is the constraint matrix on the left side of the kinematic equation corresponding to the initial control point matrix, and the order of this matrix is ​​n; c eq A is the constraint matrix on the right side of the kinematic equation; ie The constraint matrix on the left side of the preset kinematic inequality; is the target raised rank matrix; is the multi-time point basis function matrix; is the initial optimization inequality constraint matrix, the order of the matrix is ​​n; It is the preset step-up fault tolerance distance.

7. A flying thorn crystal path planning device, characterized in that: include: A response module is used to respond to the planning request, initialize the coordinates of multiple control points of the flying thorn crystal, and generate multiple initial control point coordinates; A matrix construction module is used to construct a kinematic constraint matrix and a control point basis function matrix according to the coordinates of each of the initial control points and the preset massive transfer motion data; A control order determination module is used to determine a target control order according to the kinematic constraint matrix, the control point basis function matrix and the coordinates of each initial control point by using a preset solution algorithm; The target path determination module is used to determine the target Bezier motion curve of the flying thorn crystal according to the target control order, the kinematic constraint matrix and the control point basis function matrix.

8. A computer device, characterized in that: It includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the flying spike crystal path planning method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the flying spike path planning method as described in any one of claims 1-6 is implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program comprises program instructions, wherein when the program instructions are executed by a computer, the computer is caused to execute the flying spike path planning method as described in any one of claims 1-6.

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