A visual-aided chip mounting positioning method and system

Abstract

The application discloses a visual auxiliary chip mounting positioning method and system, and relates to the field of chip mounters.The method comprises the following steps: obtaining the current image of a circuit board to be mounted, combining a circuit board design drawing to obtain a target coordinate set of a component to be mounted; taking the current coordinate of a suction nozzle of a chip mounter as a starting coordinate; obtaining an initial coordinate set of the component to be mounted according to the current image of the component to be mounted; and calculating the coordinates of a midpoint and an endpoint of the movement of the suction nozzle of the chip mounter according to the initial coordinate set and the target coordinate set.Based on the initial coordinate set and the target coordinate set, the angle of the suction nozzle is adjusted by using an angle adjustment model to rotate and adjust the angle, so that the component to be mounted can be accurately rotated to an angle consistent with the target position, and the angle deviation of the component is prevented.Based on the starting point, the midpoint and the endpoint, the path of the suction nozzle is planned by using a path planning model, so that the mechanical arm drives the suction nozzle to transport the component to be mounted along the optimal path, and the position deviation of the component is prevented.The application realizes the rapid and accurate positioning of the chip mounter.

Description

Technical Field

[0001] The present invention relates to the technical field of chip placement machines, and in particular to a vision-assisted chip placement positioning method and system. Background Art

[0002] Chip placement machines are a very critical equipment in the modern electronics manufacturing industry. They are used to accurately place surface mount components (such as chips, diodes, capacitors, resistors, etc.) onto circuit boards. In recent years, with the increasing miniaturization, complexity of functions and improvement of production requirements of electronic products, the technology of chip placement machines has also been continuously developed.

[0003] Most existing placement machine positioning methods detect whether the patch components are placed correctly by comparing the actual position with the theoretical position, thereby determining whether to correct them. However, the components need to be placed before correction, and direct and accurate positioning cannot be achieved, which reduces the efficiency of the patch to a certain extent. Summary of the Invention

[0004] The technical problem solved by the present invention is that most of the existing technologies place components first and then perform deviation correction, which cannot achieve direct and accurate positioning, and to a certain extent reduces the efficiency of patching.

[0005] To solve the above technical problems, the present invention provides the following technical solution: a vision-assisted chip placement positioning method, comprising:

[0006] Step S100, constructing a spatial coordinate system and determining the origin position;

[0007] Step S200: Acquire a current image of the circuit board to be mounted, analyze the current position of the circuit board to be mounted in the spatial coordinate system, acquire a circuit board design drawing and establish a circuit board coordinate system, construct a standard three-dimensional model drawing based on the circuit board design drawing, and calculate a corner point coordinate set of components in the standard three-dimensional model drawing in the circuit board coordinate system. Convert the corner point coordinate set of the component in the circuit board coordinate system into the corner point coordinate set of the component in the spatial coordinate system through coordinate conversion, and record the corner point coordinate set of the component in the spatial coordinate system as the target coordinate set.

[0008] Step S300: Acquire the current image of the nozzle of the placement machine to obtain the current coordinates of the nozzle of the placement machine, record them as the starting coordinates, acquire the current image of the component to be placed, analyze the current state of the component to be placed, obtain the current corner point coordinate set of the component to be placed, record them as the initial coordinate set;

[0009] Step S400: Calculate the midpoint coordinates and the end point coordinates based on the initial coordinate set and the target coordinate set, input the initial coordinate set, target coordinate set, starting point coordinates, midpoint coordinates and end point coordinates into the pre-trained path planning model and angle adjustment model respectively, perform optimal path planning on the moving trajectory of the placement machine nozzle, and adjust the angle of the placement machine nozzle.

[0010] Preferably, the step S1 specifically includes:

[0011] Step S101: taking the center of the placement machine workbench as the coordinate origin of the three-dimensional space coordinate system;

[0012] Step S102: The left and right directions of the placement machine working direction are used as the X-axis of the three-dimensional space coordinate system, and the right is used as the positive direction of the X-axis;

[0013] Step S103: The front-to-back direction of the placement machine's working direction is used as the Y axis of the three-dimensional space coordinate system, with the front direction being the positive direction of the Y axis;

[0014] Step S104: The up and down directions of the placement machine working direction are used as the Z axis of the three-dimensional space coordinate system, and the direction above is the positive direction of the Z axis;

[0015] Step S105: The rotation axis of the suction nozzle is set as the θ axis.

[0016] Preferably, the step S200 specifically includes:

[0017] Step S201: using a binocular camera to capture a current image of a circuit board to be mounted, performing grayscale processing and denoising processing on the current image of the circuit board to be mounted to obtain a first processed image, and using a target detection algorithm to detect the position of the circuit board to be mounted;

[0018] Step S202: Obtaining a circuit board design drawing, and analyzing the positions, directions, and connection relationships between components in the circuit board design drawing;

[0019] Step S203: Establishing a circuit board coordinate system, constructing the positions, directions, and connection relationships between components in the design drawing into a standard three-dimensional model diagram, and calculating the corner point coordinate set of the components in the circuit board coordinate system based on the positions of the standard three-dimensional model diagram in the circuit board coordinate system;

[0020] Step S204: converting the corner point coordinate set of the component in the circuit board coordinate system into the corner point coordinate set in the space coordinate system through coordinate translation, rotation and scaling, which is the target coordinate set;

[0021] Step S205: numbering each component to be mounted one by one, and each component to be mounted corresponding to a number has its corresponding target coordinate set.

[0022] Preferably, in step S204, the coordinate conversion formula is:

[0023] x1=xT;

[0024] T=R z *T x *T s ;

[0025] Among them, x1 represents the coordinate in the space coordinate system, x represents the coordinate in the circuit board coordinate system, T represents the transformation matrix, R z represents the rotation matrix, T x represents the translation matrix, T s Represents the scaling matrix.

[0026] Preferably, the step S300 specifically includes:

[0027] Step S301: Acquire a current image of a component to be mounted, perform grayscale processing and denoising on the current image of the component to be mounted to obtain a second processed image, identify contour features of the second processed image, determine the shape, size, and name of the component to be mounted, match the shape, size, and name with the number, and find a target coordinate set for the component to be mounted;

[0028] Step S302: performing corner point analysis on the contour features of the second processed image to determine the current coordinates of each corner point in the spatial coordinate system, merging the current coordinates of each corner point and outputting them as a corner point coordinate set, recorded as an initial coordinate set;

[0029] Step S303: Acquire the current image of the nozzle of the placement machine, and determine the current coordinates of the nozzle of the placement machine according to the relative position of the nozzle and the robot arm.

[0030] Preferably, the training process of the path planning model is:

[0031] Collect multiple groups of first sample sets, and divide the first sample sets into a first training set and a first test set according to a preset ratio, wherein the first sample sets include starting point coordinates A (x1, y1, z1), midpoint coordinates B (x2, y2, z2), and end point coordinates C (x3, y3, z3);

[0032] Taking the coordinates of the starting point, midpoint, and end point as input and the shortest path as output for training, a preliminary path planning model is obtained;

[0033] The preliminary path planning model is tested using a first test set, and training is stopped when a first accuracy is reached to obtain a trained path planning model.

[0034] Preferably, the training process of the angle adjustment model is:

[0035] Collecting multiple groups of second sample sets, and dividing the second sample sets into a second training set and a second test set according to a preset ratio;

[0036] The second sample set includes an initial coordinate set M{M1(x1, y1, z1), M2(x2, y2, z2)...M m (xm, ym, zm)} and the target coordinate set N{N1(x1, y1, z1), N2(x2, y2, z2)...N n (xn, yn, zn)};

[0037] Taking the initial coordinate set M and the target coordinate set N as input and the rotation angle of the nozzle rotation axis as output for training, a preliminary angle adjustment model is obtained;

[0038] The preliminary angle adjustment model is trained using the second test set, and when the second accuracy is reached, training is stopped to obtain a trained angle adjustment model.

[0039] Preferably, step S400 includes:

[0040] Step S401: Obtain the initial coordinate set M{M1(x1, y1, z1), M2(x2, y2, z2)...M m (xm, ym, zm)};

[0041] Step S402: Obtain the target coordinate set N{N1(x1, y1, z1), N2(x2, y2, z2)...N n (xn, yn, zn)};

[0042] Step S403: inputting the initial coordinate set M of the component to be mounted and the target coordinate set N of the component to be mounted into the trained angle adjustment model, and outputting the adjustment angle θ0;

[0043] Step S404: the nozzle rotates around the θ axis, and the rotation angle is θ0.

[0044] Preferably, step S400 further includes:

[0045] Step S405: Obtain the initial coordinate set M{M1(x1, y1, z1), M2(x2, y2, z2)...M m (xm, ym, zm)}, calculate the center coordinates of the initial coordinate set, record it as the midpoint (M x , M y , M z ), the midpoint is calculated as follows:

[0046] M x=(x1+x2+…+xm) / m;

[0047] M y =(y1+y2+…+ym) / m;

[0048] M z =(z1+z2+……+zm) / m;

[0049] Among them, M x is the x coordinate of the midpoint, M y is the y coordinate of the midpoint, M z is the z coordinate of the midpoint;

[0050] Step S406: Obtain the target coordinate set N{N1(x1, y1, z1), N2(x2, y2, z2)...N n (xn, yn, zn)}, calculate the center coordinates of the target coordinate set using the same logic as in step S405 as the end point (N x , N y , N z );

[0051] Step S407: Obtain the current coordinates of the nozzle as the starting point A (x1, y1, z1);

[0052] Step S408: The starting point A (x1, y1, z1), the midpoint (M x , M y , M z )、End point(N x , N y , N z ) is input into the trained path planning model and the optimal path is output;

[0053] Step S409: The robotic arm drives the placement machine nozzle to move along the optimal path.

[0054] On the other hand, the present invention provides a vision-assisted chip placement positioning system, comprising a model building module, an image acquisition module, an image processing module, a solution generation module, and a control module;

[0055] The model building module is used to build a path planning model and an angle adjustment model;

[0056] The image acquisition module is used to acquire the current image of the circuit board to be mounted, the current image of the component to be mounted, and the current image of the nozzle of the placement machine;

[0057] The image processing module is used to perform image processing and image analysis on the current image of the circuit board to be mounted, the current image of the component to be mounted, and the current image of the nozzle of the placement machine to obtain an initial coordinate set, a target coordinate set, a starting point, a midpoint, and an end point;

[0058] The solution generation module is used to generate a positioning solution based on the initial coordinate set, the target coordinate set, the starting point, the midpoint and the end point using a path planning model and an angle adjustment model, perform optimal path planning on the moving path of the placement machine nozzle, and adjust the angle of the placement machine nozzle;

[0059] The control module is used to receive the positioning solution output by the path planning model and the angle adjustment model, and control the movement of the robotic arm and the rotation of the rotation axis according to the positioning solution.

[0060] The beneficial effects of the present invention are as follows: by collecting the current image of the circuit board to be mounted, the current image of the component to be mounted and the current image of the nozzle of the placement machine, the initial coordinate set, the target coordinate set, the starting point, the midpoint and the end point are obtained; based on the initial coordinate set and the target coordinate set, the angle of the nozzle is adjusted by using the angle adjustment model, so that the component to be mounted can be successfully rotated to an angle consistent with the target position to prevent the component angle from shifting; based on the starting point, the midpoint and the end point, the path planning model is used to plan the nozzle path, so that the robotic arm drives the nozzle to transport the component to be mounted along the optimal path to prevent the component position from shifting, thereby achieving accurate and rapid positioning of the chip patch. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A schematic diagram of the basic flow of a vision-assisted chip placement positioning method provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0062] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0063] Example 1, with reference to Figure 1 , as an embodiment of the present invention, provides a vision-assisted chip placement positioning method, comprising:

[0064] Step S100, constructing a spatial coordinate system and determining the origin position;

[0065] Step S200: Acquire a current image of the circuit board to be mounted, analyze the current position of the circuit board to be mounted in the spatial coordinate system, acquire a circuit board design drawing and establish a circuit board coordinate system, construct a standard three-dimensional model drawing based on the circuit board design drawing, and calculate a corner point coordinate set of components in the standard three-dimensional model drawing in the circuit board coordinate system. Convert the corner point coordinate set of the component in the circuit board coordinate system into the corner point coordinate set of the component in the spatial coordinate system through coordinate conversion, and record the corner point coordinate set of the component in the spatial coordinate system as the target coordinate set.

[0066] Step S300: Acquire the current image of the nozzle of the placement machine to obtain the current coordinates of the nozzle of the placement machine, record them as the starting coordinates, acquire the current image of the component to be placed, analyze the current state of the component to be placed, obtain the current corner point coordinate set of the component to be placed, record them as the initial coordinate set;

[0067] Step S400: Calculate the midpoint coordinates and the end point coordinates based on the initial coordinate set and the target coordinate set, input the initial coordinate set, target coordinate set, starting point coordinates, midpoint coordinates and end point coordinates into the pre-trained path planning model and angle adjustment model respectively, perform optimal path planning on the moving trajectory of the placement machine nozzle, and adjust the angle of the placement machine nozzle.

[0068] Specifically, by collecting the current image of the circuit board to be mounted, the current image of the component to be mounted and the current image of the placement machine nozzle, the initial coordinate set, the target coordinate set, the starting point, the midpoint and the end point are obtained. Based on the initial coordinate set and the target coordinate set, the angle of the nozzle is adjusted using the angle adjustment model, so that the component to be mounted can be successfully rotated to an angle consistent with the target position to prevent the component angle from shifting. Based on the starting point, the midpoint and the end point, the path planning model is used to plan the nozzle path, so that the robotic arm drives the nozzle to transport the component to be mounted along the optimal path to prevent the component position from shifting, thereby achieving precise positioning of the chip placement.

[0069] The step S1 specifically includes:

[0070] Step S101: taking the center of the placement machine workbench as the coordinate origin of the three-dimensional space coordinate system;

[0071] Step S102: The left and right directions of the placement machine working direction are used as the X-axis of the three-dimensional space coordinate system, and the right is used as the positive direction of the X-axis;

[0072] Step S103: The front-to-back direction of the placement machine's working direction is used as the Y axis of the three-dimensional space coordinate system, with the front direction being the positive direction of the Y axis;

[0073] Step S104: The up and down directions of the placement machine working direction are used as the Z axis of the three-dimensional space coordinate system, and the direction above is the positive direction of the Z axis;

[0074] Step S105: The rotation axis of the suction nozzle is set as the θ axis.

[0075] Specifically, the spatial coordinate system helps the placement machine define the position of each component relative to the circuit board, ensuring that each component is placed in the correct position and direction. Through the coordinate system, the placement machine can accurately calculate the angle, direction and position of each component to avoid component orientation errors or offsets.

[0076] The step S200 specifically includes:

[0077] Step S201: using a binocular camera to capture a current image of a circuit board to be mounted, performing grayscale processing and denoising processing on the current image of the circuit board to be mounted to obtain a first processed image, and using a target detection algorithm to detect the position of the circuit board to be mounted;

[0078] Step S202: Obtain a circuit board design drawing, and analyze the positions, directions, and connection relationships between components in the circuit board design drawing;

[0079] Step S203: Establishing a circuit board coordinate system, constructing the positions, directions, and connection relationships between components in the design drawing into a standard three-dimensional model diagram, and calculating the corner point coordinate set of the components in the circuit board coordinate system based on the positions of the standard three-dimensional model diagram in the circuit board coordinate system;

[0080] Step S204: converting the corner point coordinate set of the component in the circuit board coordinate system into the corner point coordinate set in the space coordinate system through coordinate translation, rotation and scaling, which is the target coordinate set;

[0081] Step S205: numbering each component to be mounted one by one, and each component to be mounted corresponding to a number has its corresponding target coordinate set.

[0082] Specifically, in this embodiment, the object detection algorithm can be edge detection or Hough transform in traditional computer vision methods, or deep learning methods such as the YOLO object detection model. Assume that a rectangular component with dimensions of 10 mm x 5 mm is included in the design drawing. The standard 3D model of this component might be a rectangular block with a length of 10 mm and a width of 5 mm.

[0083] In step S204, the coordinate conversion formula is:

[0084] x1=xT;

[0085] T=R z *T x *T s ;

[0086] Among them, x1 represents the coordinate in the space coordinate system, x represents the coordinate in the circuit board coordinate system, T represents the transformation matrix, R z represents the rotation matrix, T x represents the translation matrix, T s Represents the scaling matrix.

[0087] The step S300 specifically includes:

[0088] Step S301: Acquire a current image of a component to be mounted, perform grayscale processing and denoising on the current image of the component to be mounted to obtain a second processed image, identify contour features of the second processed image, determine the shape, size, and name of the component to be mounted, match the shape, size, and name with the number, and find a target coordinate set for the component to be mounted;

[0089] Step S302: performing corner point analysis on the contour features of the second processed image to determine the current coordinates of each corner point in the spatial coordinate system, merging the current coordinates of each corner point and outputting them as a corner point coordinate set, recorded as an initial coordinate set;

[0090] Step S303: Acquire the current image of the nozzle of the placement machine, and determine the current coordinates of the nozzle of the placement machine according to the relative position of the nozzle and the robot arm.

[0091] Specifically, the chip component is identified to obtain its shape, size, and name. For example, if a rectangular component with a size of 10mm×5mm is detected, the matched component name may be "SOT-23 transistor". The number corresponding to the SOT-23 transistor is found, and the target coordinate set of the component is determined, thereby determining the position and angle at which the component should be placed, and then calculating the target coordinate point set of the component corner points.

[0092] The training process of the path planning model is as follows:

[0093] Collect multiple groups of first sample sets, and divide the first sample sets into a first training set and a first test set according to a preset ratio, wherein the first sample sets include starting point coordinates A (x1, y1, z1), midpoint coordinates B (x2, y2, z2), and end point coordinates C (x3, y3, z3);

[0094] Taking the coordinates of the starting point, midpoint, and end point as input and the shortest path as output for training, a preliminary path planning model is obtained;

[0095] The preliminary path planning model is tested using a first test set, and training is stopped when a first accuracy is reached to obtain a trained path planning model.

[0096] Specifically, the training goal of the model is to predict the shortest path output by inputting the starting point, midpoint and end point coordinates. This is crucial for the path planning and navigation of the placement machine nozzle. After path planning, the shortest path can effectively improve the efficiency of the nozzle in transporting the components to be placed, saving time and resources while reducing unnecessary consumption.

[0097] The training process of the angle adjustment model is:

[0098] Collecting multiple groups of second sample sets, and dividing the second sample sets into a second training set and a second test set according to a preset ratio;

[0099] The second sample set includes an initial coordinate set M{M1(x1, y1, z1), M2(x2, y2, z2)...M m (xm, ym, zm)} and the target coordinate set N{N1(x1, y1, z1), N2(x2, y2, z2)...N n (xn, yn, zn)};

[0100] Taking the initial coordinate set M and the target coordinate set N as input and the rotation angle of the nozzle rotation axis as output for training, a preliminary angle adjustment model is obtained;

[0101] The preliminary angle adjustment model is trained using the second test set, and when the second accuracy is reached, training is stopped to obtain a trained angle adjustment model.

[0102] Specifically, the initial coordinate set M includes the current spatial position and posture of the component to be mounted, and the target coordinate set N represents the position and posture that the component to be mounted needs to reach, which is a target area and has a specific direction. The offset angle between the two coordinate sets is obtained through the initial coordinate set M and the target coordinate set N, thereby determining the rotation angle of the rotation axis, that is, the adjustment angle is calculated through the input coordinate information so that the suction nozzle can accurately drive the component to be mounted to rotate to prevent the component angle from offset.

[0103] Step S400 includes:

[0104] Step S401: Obtain the initial coordinate set M{M1(x1, y1, z1), M2(x2, y2, z2)...M m (xm, ym, zm)};

[0105] Step S402: Obtain the target coordinate set N{N1(x1, y1, z1), N2(x2, y2, z2)...N n (xn, yn, zn)};

[0106] Step S403: inputting the initial coordinate set M of the component to be mounted and the target coordinate set N of the component to be mounted into the trained angle adjustment model, and outputting the adjustment angle θ0;

[0107] Step S404: the nozzle rotates around the θ axis, and the rotation angle is θ0.

[0108] Specifically, through image acquisition and image processing, the coordinates of the corner points of the patch component are calculated to obtain the initial coordinate set M of the component to be patched, and the target coordinate set N is obtained. The two coordinate sets are input into the pre-built angle adjustment model, so that the suction nozzle can accurately drive the component to be patched to rotate and prevent the angle of the component from shifting.

[0109] Step S400 also includes:

[0110] Step S405: Obtain the initial coordinate set M{M1(x1, y1, z1), M2(x2, y2, z2)...M m (xm, ym, zm)}, calculate the center coordinates of the initial coordinate set, record it as the midpoint (M x , M y , M z ), the midpoint is calculated as follows:

[0111] M x =(x1+x2+…+xm) / m;

[0112] M y =(y1+y2+…+ym) / m;

[0113] M z =(z1+z2+……+zm) / m;

[0114] Among them, M x is the x coordinate of the midpoint, M y is the y coordinate of the midpoint, M z is the z coordinate of the midpoint;

[0115] Step S406: Obtain the target coordinate set N{N1(x1, y1, z1), N2(x2, y2, z2)...N n (xn, yn, zn)}, calculate the center coordinates of the target coordinate set using the same logic as in step S405 as the end point (N x , N y , N z );

[0116] Step S407: Obtain the current coordinates of the nozzle as the starting point A (x1, y1, z1);

[0117] Step S408: The starting point A (x1, y1, z1), the midpoint (M x , M y , M z )、End point(N x , N y , N z ) is input into the trained path planning model and the optimal path is output;

[0118] Step S409: The robotic arm drives the placement machine nozzle to move along the optimal path.

[0119] Specifically, in this embodiment, the placement machine nozzle is treated as a point, simplifying the calculation steps. The center coordinates of the initial coordinate set of the component to be placed and the center coordinates of the target coordinate set are calculated, serving as the midpoint and end point of the placement machine nozzle's movement, respectively. The placement machine nozzle moves from the starting point to the midpoint, picks up the component to be placed, and then moves along the planned path to transport the component to the target location. This invention utilizes a machine learning model to improve the efficiency of the nozzle's component transport and, in conjunction with the coordinate system, positions the component, thereby improving the placement accuracy of the placement machine.

[0120] Example 2 is a vision-assisted chip placement positioning system provided by the present invention, comprising a model building module, an image acquisition module, an image processing module, a solution generation module and a control module;

[0121] The model building module is used to build a path planning model and an angle adjustment model;

[0122] The image acquisition module is used to acquire the current image of the circuit board to be mounted, the current image of the component to be mounted, and the current image of the nozzle of the placement machine;

[0123] The image processing module is used to perform image processing and image analysis on the current image of the circuit board to be mounted, the current image of the component to be mounted, and the current image of the nozzle of the placement machine to obtain an initial coordinate set, a target coordinate set, a starting point, a midpoint, and an end point;

[0124] The solution generation module is used to generate a positioning solution based on the initial coordinate set, the target coordinate set, the starting point, the midpoint and the end point using a path planning model and an angle adjustment model, perform optimal path planning on the moving path of the placement machine nozzle, and adjust the angle of the placement machine nozzle;

[0125] The control module is used to receive the positioning solution output by the path planning model and the angle adjustment model, and control the movement of the robotic arm and the rotation of the rotation axis according to the positioning solution.

[0126] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a fully hardware embodiment, a fully software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium may be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0127] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A vision-assisted chip placement positioning method, characterized in that: include: Step S100, constructing a spatial coordinate system and determining the origin position; Step S200: Acquire a current image of the circuit board to be mounted, analyze the current position of the circuit board to be mounted in the spatial coordinate system, acquire a circuit board design drawing and establish a circuit board coordinate system, construct a standard three-dimensional model drawing based on the circuit board design drawing, and calculate a corner point coordinate set of components in the standard three-dimensional model drawing in the circuit board coordinate system. Convert the corner point coordinate set of the component in the circuit board coordinate system into the corner point coordinate set of the component in the spatial coordinate system through coordinate conversion, and record the corner point coordinate set of the component in the spatial coordinate system as the target coordinate set. Step S300: Acquire the current image of the nozzle of the placement machine to obtain the current coordinates of the nozzle of the placement machine, record them as the starting coordinates, acquire the current image of the component to be placed, analyze the current state of the component to be placed, obtain the current corner point coordinate set of the component to be placed, record them as the initial coordinate set; Step S400: Calculate the midpoint coordinates and the end point coordinates based on the initial coordinate set and the target coordinate set, input the initial coordinate set, target coordinate set, starting point coordinates, midpoint coordinates and end point coordinates into the pre-trained path planning model and angle adjustment model respectively, perform optimal path planning on the moving trajectory of the placement machine nozzle, and adjust the angle of the placement machine nozzle.

2. The visually assisted chip placement positioning method according to claim 1, wherein: The step S100 specifically includes: Step S101: taking the center of the placement machine workbench as the coordinate origin of the three-dimensional space coordinate system; Step S102: The left and right directions of the placement machine working direction are used as the X-axis of the three-dimensional space coordinate system, and the right is used as the positive direction of the X-axis; Step S103: The front-to-back direction of the placement machine's working direction is used as the Y axis of the three-dimensional space coordinate system, with the front direction being the positive direction of the Y axis; Step S104: The up and down directions of the placement machine working direction are used as the Z axis of the three-dimensional space coordinate system, and the direction above is the positive direction of the Z axis; Step S105: The rotation axis of the suction nozzle is set as the θ axis.

3. The visually assisted chip placement positioning method according to claim 1, wherein: The step S200 specifically includes: Step S201: using a binocular camera to capture a current image of a circuit board to be mounted, performing grayscale processing and denoising processing on the current image of the circuit board to be mounted to obtain a first processed image, and using a target detection algorithm to detect the position of the circuit board to be mounted; Step S202: Obtain a circuit board design drawing, and analyze the positions, directions, and connection relationships between components in the circuit board design drawing; Step S203: Establishing a circuit board coordinate system, constructing the positions, directions, and connection relationships between components in the design drawing into a standard three-dimensional model diagram, and calculating the corner point coordinate set of the components in the circuit board coordinate system based on the positions of the standard three-dimensional model diagram in the circuit board coordinate system; Step S204: converting the corner point coordinate set of the component in the circuit board coordinate system into the corner point coordinate set in the space coordinate system through coordinate translation, rotation and scaling, which is the target coordinate set; Step S205: numbering each component to be mounted one by one, and each component to be mounted corresponding to a number has its corresponding target coordinate set.

4. The visually assisted chip placement positioning method according to claim 3, wherein: In step S204, the coordinate conversion formula is: x1=xT; T=R z *T x *T s ; Among them, x1 represents the coordinate in the space coordinate system, x represents the coordinate in the circuit board coordinate system, T represents the transformation matrix, R z represents the rotation matrix, T x represents the translation matrix, T s Represents the scaling matrix.

5. The visually assisted chip placement positioning method according to claim 3, wherein: The step S300 specifically includes: Step S301: Acquire a current image of a component to be mounted, perform grayscale processing and denoising on the current image of the component to be mounted to obtain a second processed image, identify contour features of the second processed image, determine the shape, size, and name of the component to be mounted, match the shape, size, and name with the number, and find a target coordinate set for the component to be mounted; Step S302: performing corner point analysis on the contour features of the second processed image to determine the current coordinates of each corner point in the spatial coordinate system, merging the current coordinates of each corner point and outputting them as a corner point coordinate set, recorded as an initial coordinate set; Step S303: Acquire the current image of the nozzle of the placement machine, and determine the current coordinates of the nozzle of the placement machine according to the relative position of the nozzle and the robot arm.

6. The visually assisted chip placement positioning method according to claim 1, wherein: The training process of the path planning model is as follows: Collect multiple groups of first sample sets, and divide the first sample sets into a first training set and a first test set according to a preset ratio, wherein the first sample sets include starting point coordinates A (x1, y1, z1), midpoint coordinates B (x2, y2, z2), and end point coordinates C (x3, y3, z3); Taking the coordinates of the starting point, midpoint, and end point as input and the shortest path as output for training, a preliminary path planning model is obtained; The preliminary path planning model is tested using a first test set, and training is stopped when a first accuracy is reached to obtain a trained path planning model.

7. The visually assisted chip placement positioning method according to claim 5, wherein: The training process of the angle adjustment model is: Collecting multiple groups of second sample sets, and dividing the second sample sets into a second training set and a second test set according to a preset ratio; The second sample set includes an initial coordinate set M{M1(x1, y1, z1), M2(x2, y2, z2)...M m (xm, ym, zm)} and the target coordinate set N{N1(x1, y1, z1), N2(x2, y2, z2)...N n (xn, yn, zn)}; Taking the initial coordinate set M and the target coordinate set N as input and the rotation angle of the nozzle rotation axis as output for training, a preliminary angle adjustment model is obtained; The preliminary angle adjustment model is trained using the second test set, and when the second accuracy is reached, training is stopped to obtain a trained angle adjustment model.

8. The visually assisted chip placement positioning method according to claim 1, wherein: Step S400 includes: Step S401: Obtain the initial coordinate set M{M1(x1, y1, z1), M2(x2, y2, z2)...M m (xm, ym, zm)}; Step S402: Obtain the target coordinate set N{N1(x1, y1, z1), N2(x2, y2, z2)...N n (xn, yn, zn)}; Step S403: inputting the initial coordinate set M of the component to be mounted and the target coordinate set N of the component to be mounted into the trained angle adjustment model, and outputting the adjustment angle θ0; Step S404: the nozzle rotates around the θ axis, and the rotation angle is θ0.

9. The visually assisted chip placement positioning method according to claim 1, wherein: Step S400 also includes: Step S405: Obtain the initial coordinate set M{M1(x1, y1, z1), M2(x2, y2, z2)...M m (xm, ym, zm)}, calculate the center coordinates of the initial coordinate set, record it as the midpoint (M x , M y , M z ), the midpoint is calculated as follows: M x =(x1+x2+……+xm) / m; M y =(y1+y2+……+ym) / m; M z =(z1+z2+……+zm) / m; Among them, M x is the x coordinate of the midpoint, M y is the y coordinate of the midpoint, M z is the z coordinate of the midpoint; Step S406: Obtain the target coordinate set N{N1(x1, y1, z1), N2(x2, y2, z2)...N n (xn, yn, zn)}, calculate the center coordinates of the target coordinate set using the same logic as in step S405 as the end point (N x , N y , N z ); Step S407: Obtain the current coordinates of the nozzle as the starting point A (x1, y1, z1); Step S408: The starting point A (x1, y1, z1), the midpoint (M x , M y , N z )、End point(N x , N y , N z ) is input into the trained path planning model and the optimal path is output; Step S409: The robotic arm drives the placement machine nozzle to move along the optimal path.

10. A vision-assisted chip placement positioning system, characterized by: It includes model building module, image acquisition module, image processing module, solution generation module and control module; The model building module is used to build a path planning model and an angle adjustment model; The image acquisition module is used to acquire the current image of the circuit board to be mounted, the current image of the component to be mounted, and the current image of the nozzle of the placement machine; The image processing module is used to perform image processing and image analysis on the current image of the circuit board to be mounted, the current image of the component to be mounted, and the current image of the nozzle of the placement machine to obtain an initial coordinate set, a target coordinate set, a starting point, a midpoint, and an end point; The solution generation module is used to generate a positioning solution based on the initial coordinate set, the target coordinate set, the starting point, the midpoint and the end point using a path planning model and an angle adjustment model, perform optimal path planning on the moving path of the placement machine nozzle, and adjust the angle of the placement machine nozzle; The control module is used to receive the positioning solution output by the path planning model and the angle adjustment model, and control the movement of the robotic arm and the rotation of the rotation axis according to the positioning solution.

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