A method for adapting a frame-type assembly member

Abstract

The application relates to the field of automatic assembly of automobile manufacturing production lines, and discloses a kind of adaptation method for frame type assembly component, by detecting and recording the space pose of the component to be assembled and the space pose of the frame type component, inputting data into the adaptation mathematical model, calculating the space pose transformation relationship between them, calculating the adaptation pose offset vector, and the robot completes the installation according to the adaptation pose offset vector. Compared with the prior art, the laser vision measurement method based on laser technology and traditional laser vision detection technology can obtain high-resolution contour point feature information, and can accurately measure the shape and features of the surface of the frame type component; the measured point cloud data can provide accurate point feature information, and the detection algorithm formed has good adaptation effect for the frame type component, the algorithm calculation amount is small, is suitable for online real-time assembly, and has good self-adaptability for frame type component contour machining error.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic assembly of automobile manufacturing production line, and particularly relates to an adaptation method for frame type assembly component. BACKGROUND

[0002] Intelligent visual measurement and adaptation technology can improve industrial production efficiency and product quality, and is an important content of future intelligent manufacturing.

[0003] Positioning and assembly of frame type component is a key link in automatic assembly in the field of automobile manufacturing, and positioning of automobile body is mostly combined with cameras in automatic assembly of vehicle cover, vehicle door and windshield, such as calibration plate based method, coordinate measurement method and SNK camera based method; among them, the calibration plate has high accuracy requirement, cannot be damaged in use and occupies storage space, and positioning of the calibration plate in the camera field of view needs high repeatability and high cost; the coordinate measurement method needs manual operation of the measurement arm to adjust the position of the camera, and also needs to accurately aim at the measured object through the rotating and tilting device, which is relatively complicated and time-consuming, and can seriously reduce production efficiency; when multiple SNK cameras are used for measurement, it is not easy to realize accurate calibration, thereby causing measurement error and affecting workpiece positioning accuracy. SUMMARY

[0004] Therefore, the present application provides an adaptation method for frame type component to solve the problems of low production efficiency and difficult accurate calibration.

[0005] In order to achieve the above purpose, the present application provides the following technical scheme: an adaptation method for frame type component, the steps of the method are as follows:

[0006] S1: a robot is used to grab an assembly component, local point feature information of the assembly component is detected by a laser vision sensor to determine the spatial pose of the assembly component, and the spatial pose of the assembly component is recorded;

[0007] S2: local point feature information of the frame type component is detected by the laser vision sensor to determine the spatial pose of the frame type component, and the spatial pose of the frame type component is recorded; wherein the laser vision sensor can form a point cloud image;

[0008] S3: an adaptation mathematical model between the assembly component and the frame type component is constructed, the local point feature information of the assembly component and the spatial pose of the assembly component are input into the adaptation mathematical model, and the local point feature information of the frame type component and the spatial pose of the frame type component are input into the adaptation mathematical model;

[0009] S4: an adaptation pose offset vector between the assembly component and the frame type component is calculated by the adaptation mathematical model.

[0010] S5: The robot installs the component to be assembled on the frame-shaped component according to the adaptive pose offset vector.

[0011] As a preferred technical solution of the present application, the relationship between the local point feature information of the component to be assembled and the spatial pose of the component to be assembled is as follows:

[0012]

[0013] wherein, is the local point feature information of the component to be assembled extracted by the laser vision sensor in the robot coordinate system, is the spatial pose function of the component to be assembled, is the homogeneous transformation matrix of the spatial pose of the component to be assembled relative to the robot coordinate system.

[0014] As a preferred technical solution of the present application, the relationship between the local point feature information of the frame-shaped component and the spatial pose of the frame-shaped component is as follows:

[0015]

[0016] wherein, is the local point feature information of the frame-shaped component in the robot coordinate system, is the spatial pose function of the frame-shaped component, is the homogeneous transformation matrix of the spatial pose of the frame-shaped component relative to the robot coordinate system.

[0017] As a preferred technical solution of the present application, the application function of the adaptive mathematical model is:

[0018]

[0019] wherein, n is the number of local point features of the component contour, is the local point feature information of the component to be assembled after adaptation in the robot coordinate system, is the pose adaptation transformation matrix between the component to be assembled and the frame-shaped component; is the pose adaptation offset vector, containing spatial position and attitude information; represents the Euclidean norm; is the adaptation function, including distance optimization adaptation of local point features and attitude optimization adaptation of local point features; is the distance adaptation matrix of the i-th local point, is the adapted distance of the i-th local point.

[0020] As a preferred technical solution of the present application,

[0021] S401: setting a distance adaptation matrix of the i-th local point , setting an adaptation distance of the i-th local point , setting a pose adaptation initial offset vector ;

[0022] S402: setting an adaptation function according to the adaptation mathematical model of the adaptable target value , using a nonlinear least square algorithm to iteratively solve the pose adaptation offset vector z between the to-be-assembled component and the frame-shaped component, the solving formula being:

[0023]

[0024] wherein J is a Jacobian matrix of the mathematical model, I is a unit matrix, is a damping parameter, h is an iteration step length vector, is a distance optimization adaptation and pose optimization adaptation vector of each local point feature, and K is the number of iterations;

[0025] S403: calculating the pose adaptation offset vector z that satisfies the adaptable target value ε in S402. .

[0026] As a preferred technical solution of the present application, the pose adaptation offset vector z includes an adaptation position and an adaptation pose; and its description form can be based on any one of a homogeneous transformation matrix, Euler angles, RPY angles, and a quaternion.

[0027] As a preferred technical solution of the present application, the number of local point feature information of the to-be-assembled component is selected from a range of 6-10.

[0028] As a preferred technical solution of the present application, the number of local point feature information of the frame-shaped component is selected from a range of 6-10.

[0029] As a preferred technical solution of the present application, the robot adopts one or several of an industrial standardized joint robot, a collaborative robot, and a humanoid robot.

[0030] The present application has the following beneficial effects:

[0031] Compared with the prior art, the laser vision measurement method based on the laser technology and the traditional laser vision detection technology can obtain high-resolution contour point feature information, and can accurately measure the shape and features of the surface of the frame-shaped component; the measured point cloud data can provide accurate point feature information, the formed detection algorithm has good adaptation effect on the frame-shaped component, the algorithm calculation amount is small, is suitable for online real-time assembly, and has good self-adaptability for frame-shaped component contour machining errors. Attached Figure Description

[0032] Figure 1 This is a flowchart illustrating an embodiment of the present invention;

[0033] Figure 2 This is a schematic diagram showing the sequence of features collected by the robot from various local points of the component according to an embodiment of the present invention.

[0034] Figure 3 This is a schematic diagram of the mathematical model of the adaptation method between the component to be assembled and the frame component based on local point feature information according to an embodiment of the present invention. Detailed Implementation

[0035] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0037] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0038] An adaptation method for frame-type assembled components, the method steps include:

[0039] S1: The robot grasps the component to be assembled, and the spatial pose of the component is determined by detecting the local point feature information of the component through the laser vision sensor and recorded.

[0040] S2: determining the spatial pose of the frame-shaped component through the laser vision sensor to detect the local point feature information of the frame-shaped component, and recording the spatial pose of the frame-shaped component; wherein the laser vision sensor can form a point cloud image;

[0041] S3: constructing an adaptive mathematical model between the to-be-assembled component and the frame-shaped component, inputting the local point feature information of the to-be-assembled component and the spatial pose of the to-be-assembled component into the adaptive mathematical model, and inputting the local point feature information of the frame-shaped component and the spatial pose of the frame-shaped component into the adaptive mathematical model;

[0042] S4: calculating the adaptive pose offset vector between the to-be-assembled component and the frame-shaped component through the adaptive mathematical model;

[0043] S5: the robot installs the to-be-assembled component on the frame-shaped component according to the adaptive pose offset vector.

[0044] Specifically, the robot in step S1 can adopt an industrial standardized joint robot, a collaborative robot and a humanoid robot. The industrial standardized joint robot is usually composed of multiple joints, each joint can realize a specific angle or direction movement, and through the coordination of the joints, complex tasks can be completed. Its advantages are high position accuracy and repeatability, stable operation under long-time and high-intensity working conditions after strict quality control and testing, mature programming language and control system for realizing automatic production. The collaborative robot can safely complete tasks with construction personnel in the working environment. Its advantages are lightweight, equipped with various sensors to realize safe and efficient human-machine cooperation, and simple programming. The humanoid robot has a similar body structure, joint configuration and movement ability to humans, can walk, operate objects and interact with people in various environments like humans. Its advantages are high performance operation and control ability, high perception interaction ability and adaptability.

[0045] Specifically, the laser vision sensor can form a point cloud image. By calculating the time of the reflected laser returning, the distance between each point on the surface of the target object and the laser vision sensor is calculated, and then the three-dimensional coordinate information of each point on the surface of the target object is obtained. The point cloud image can be used to accurately measure the size, shape and other parameters of the object, and improve the production quality and efficiency. By extracting the local point feature information of the to-be-assembled component, the spatial pose of the to-be-assembled component is determined. By extracting the local point feature information of the frame-shaped component, the spatial pose of the frame-shaped component is determined.

[0046] Specifically, the local point feature information of the to-be-assembled component and the spatial pose of the to-be-assembled component have the following relationship:

[0047]

[0048] wherein, This refers to the local point feature information of the component to be assembled, extracted by the laser vision sensor in the robot coordinate system. Let be the spatial pose function of the component to be assembled. Let be the homogeneous transformation matrix of the spatial pose of the component to be assembled relative to the robot coordinate system.

[0049] Specifically, the local point feature information of the frame-type component and the spatial pose of the frame-type component have the following relationship:

[0050]

[0051] in, This provides local point feature information for frame-type components in the robot coordinate system. For the spatial pose function of the frame-type component, This is the homogeneous transformation matrix of the spatial pose of the frame-type component relative to the robot coordinate system.

[0052] Specifically, the application function that adapts to the mathematical model is:

[0053]

[0054] Where n is the number of local point features of the component contour. This refers to the local point feature information of the component to be assembled in the robot coordinate system after adaptation. The pose adaptation transformation matrix between the component to be assembled and the frame component; The pose adaptation offset vector contains spatial position and attitude information; Denotes the Euclidean norm; The adaptation function includes distance optimization adaptation and pose optimization adaptation of local point features; Let be the distance adaptation matrix for the i-th local point. Let be the adaptation distance of the i-th local point.

[0055] The adaptive mathematical model uses a nonlinear least squares function. The description consists of two parts, including:

[0056] The first part is the distance-optimal fitting evaluation function for local point features. The function Calculate the distance between the local point features of the i-th frame component and the local point features of the component to be assembled after adaptation, and then calculate the absolute value of the difference between the distance between the local point features of the i-th frame component and the local point features of the component to be assembled after adaptation and the adaptation distance of the i-th local point.

[0057] The second part is the pose optimal fit evaluation function for local point features. , the function computing the deviation between two poses, the first pose being represented by a homogeneous transformation matrix of the spatial pose of the frame-like member with respect to the robot coordinate system ; the second pose being represented by the ideal assembly pose of the component to be assembled, the second pose being equal to the pose adaptation transformation matrix between the component to be assembled and the frame-like member and .

[0058] The adaptation relationship between the component to be assembled and the frame-like member is determined by applying the formula of the mathematical model, and the technical features such as local point feature information, pose adaptation transformation matrix, pose adaptation offset vector, Euclidean norm, adaptation function, distance adaptation matrix, and adaptation distance are described. These technical features cooperate with each other to accurately calculate the pose offset between the component to be assembled and the frame-like member, thereby realizing high-precision assembly. The parameters and variables in the formula work together to ensure the accuracy and efficiency of the adaptation process, which helps to improve the assembly precision and efficiency.

[0059] Specifically, the pose adaptation offset vector z includes an adaptation position and an adaptation attitude, and its description form can be based on any one of the homogeneous transformation matrix, Euler angle, RPY angle, and quaternion. The homogeneous transformation matrix realizes translation operation by adding the fourth dimension, so that rotation and translation can be uniformly represented, facilitating the calculation of coordinate transformation and composite transformation, and is used in robot applications to describe the position and attitude of the robot end effector in three-dimensional space and the transformation relationship between different coordinate systems.

[0060] Specifically, the specific implementation steps of step S4 for calculating the adaptation pose offset vector between the component to be assembled and the frame-like member through the adaptation mathematical model are as follows:

[0061] S401: setting the distance adaptation matrix of the ith local point , setting the adaptation distance of the ith local point , and setting the pose adaptation initial offset vector ;

[0062] S402: according to the adaptation mathematical model, setting the adaptable target value of the adaptation function , using a nonlinear least squares algorithm to iteratively solve the pose adaptation offset vector z between the component to be assembled and the frame-like member, and the solving formula is:

[0063]

[0064] wherein J is the Jacobian matrix of the mathematical model, I is the unit matrix, is a damping parameter, and h is an iteration step length vector.​ K is the number of iteration steps;

[0065] S403: calculating the pose fitting offset vector in S402 that meets the adaptable target value ε .

[0066] Specifically, the number of local point feature information of the component to be assembled is set to n, and n is selected in the range of 6-10; the number of local point feature information of the frame type component is also set to n, and n is selected in the range of 6-10.

[0067] Embodiment 1:

[0068] In this embodiment, a laser vision sensor is fixed on a tool clamp of a robot for measurement.

[0069] When n=7, seven position measurement points are used to extract local point feature information, and the robot measurement sequence is as shown in Figure 2 According to the robot planning trajectory, the robot is operated to move to the specified position to collect local point feature information, the image point feature information is filtered using a pre-processing algorithm, and the current pose is recorded.

[0070] S1, the teaching robot grasps the component to be assembled, and detects the local point feature information of the component to be assembled using a laser vision sensor. When n=7, then is the local point feature information of the component to be assembled extracted by the laser vision sensor in the robot coordinate system, and the spatial pose relationship of the component to be assembled is recorded:

[0071] Formula (1)

[0072] S2, the local point feature information of the frame type component is detected using a laser vision sensor. When n=7, then is the local point feature information of the frame type component extracted by the laser vision sensor in the robot coordinate system, and the spatial pose relationship of the frame type component is recorded:

[0073] Formula (2)

[0074] As shown in Figure 3 is a mathematical model diagram of the fitting method between the component to be assembled and the frame type component based on the local point feature information (only two measurement points and the spatial pose relationship of the component are drawn in the figure, and other measurement points and the component have the same spatial pose relationship);

[0075] S3, the fitting mathematical model between the component to be assembled and the frame type component (i=1, 2,..., 7) is:

[0076] Formula (3)

[0077] local point feature information of the frame type component and the spatial pose local point feature information of the frame type component and the spatial pose ;

[0078] S4, solving the fitting pose offset vector between the component to be fitted and the frame type component;

[0079] The specific implementation process of this step S4 is as follows:

[0080] S401, setting the distance fitting matrix of the i-th local point , setting the fitting distance of the i-th local point , setting the initial offset vector of the pose fitting ;

[0081] S402, according to the fitting mathematical model in step S3 described by formula (3), setting the fitting function the adaptable target value , using the nonlinear least square algorithm to iteratively solve the pose fitting offset vector z between the component to be fitted and the frame type component, and the solving formula is:

[0082] Formula (4)

[0083] In formula (4), J is the Jacobian matrix of the mathematical model, is a damping parameter, h is an iteration step vector, is the distance optimization fitting and attitude optimization fitting vector of each local point feature, and K is the iteration step number.

[0084] S403, obtaining the pose fitting offset vector that satisfies the adaptable target value in step S402.

[0085] S5, the robot automatically installs the component to be fitted on the frame type component according to the pose offset vector.

[0086] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for adapting frame-type assembled components, characterized in that, The steps of the method include: S1: The robot grasps the component to be assembled, and the spatial pose of the component is determined by detecting the local point feature information of the component to be assembled through the laser vision sensor, and the spatial pose of the component to be assembled is recorded. S2: The spatial pose of the frame-type component is determined by detecting local point feature information of the frame-type component using the laser vision sensor, and the spatial pose of the frame-type component is recorded; wherein, the laser vision sensor can form a point cloud image; S3: Construct an adaptation mathematical model between the component to be assembled and the frame component. The application function of the adaptation mathematical model is: ， Input the local point feature information of the component to be assembled and the spatial pose of the component to be assembled into the adaptation mathematical model; input the local point feature information of the frame component and the spatial pose of the frame component into the adaptation mathematical model. S4: Calculate the adaptation pose offset vector between the component to be assembled and the frame component using the adaptation mathematical model; S5: The robot installs the component to be assembled onto the frame component according to the adaptive pose offset vector; The functional relationship between the local point feature information of the component to be assembled and the spatial pose of the component to be assembled is as follows: , This refers to the local point feature information of the component to be assembled, extracted by the laser vision sensor in the robot coordinate system. Let be the spatial pose function of the component to be assembled. Let be the homogeneous transformation matrix of the spatial pose of the component to be assembled relative to the robot coordinate system; The relationship between the local point feature information of the frame component and the spatial pose of the frame component is as follows: , This provides local point feature information for frame-type components in the robot coordinate system. For the spatial pose function of the frame-type component, This is the homogeneous transformation matrix of the spatial pose of the frame-type component relative to the robot coordinate system; n represents the number of local point features of the component outline. This refers to the local point feature information of the component to be assembled in the robot coordinate system after adaptation. The pose adaptation transformation matrix between the component to be assembled and the frame component; The pose adaptation offset vector contains spatial position and attitude information; Denotes the Euclidean norm; The adaptation function includes distance optimization adaptation and pose optimization adaptation of local point features; Let be the distance adaptation matrix for the i-th local point. Let be the adaptation distance of the i-th local point.

2. The adaptation method for frame-type assembly components according to claim 1, characterized in that: The adaptation function It consists of two parts, among which, The first part is the distance-optimal fitting evaluation function for local point features. The function Calculate the distance between the local point features of the i-th frame component and the local point features of the component to be assembled after adaptation, and then calculate the absolute value of the difference between the distance between the local point features of the i-th frame component and the local point features of the component to be assembled after adaptation and the adaptation distance of the i-th local point. The second part is the pose optimal fit evaluation function for local point features. The function Calculate the deviation between two poses. The first pose is the homogeneous transformation matrix of the spatial pose of the frame component relative to the robot coordinate system. The second pose is represented by the ideal assembly pose of the component to be assembled, and the second pose is equal to the pose adaptation transformation matrix between the component to be assembled and the frame component. and The product between them.

3. The adaptation method for frame-type assembly components according to claim 1, characterized in that, The step of calculating the adaptation pose offset vector between the component to be assembled and the frame component using the adaptation mathematical model includes: S401: Set the distance adaptation matrix for the i-th local point. Set the adaptation distance for the i-th local point. Set the initial offset vector for pose adaptation. ; S402: Set the adaptation function according to the aforementioned adaptation mathematical model. Adaptable target value The pose adaptation offset vector z between the component to be assembled and the frame component is solved iteratively using a nonlinear least squares algorithm. The solution formula is as follows: Where J is the Jacobian matrix of the mathematical model, and I is the identity matrix. Here, h is the damping parameter, and h is the iteration step size vector. The distance optimization and pose optimization adaptation vectors for each local point feature are given, where K is the number of iteration steps; S403: Calculate the pose adaptation offset vector in S402 that satisfies the adaptability target value ε. .

4. The adaptation method for frame-type assembly components according to claim 1, characterized in that: The pose adaptation offset vector z includes the adaptation position and the adaptation posture; its description can be based on any one of the following forms: homogeneous transformation matrix, Euler angle, RPY angle, or quaternion.

5. The adaptation method for frame-type assembly components according to claim 1, characterized in that: The number of local point feature information detected for the component to be assembled is selected in the range of 6-10.

6. The adaptation method for frame-type assembly components according to claim 1, characterized in that: The number of local point feature information detected for the frame-type component is selected in the range of 6-10.

7. The adaptation method for frame-type assembly components according to claim 1, characterized in that: The robot is one or more of the following: industrial standardized articulated robots, collaborative robots, and humanoid robots.

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