Robot working space visualization construction method and system based on digital twinning

Abstract

The invention relates to the field of digital twinning technology application, in particular to a visual construction method and system for a robot working space based on digital twinning. According to the visual construction method for the working space of the robot based on the digital twinning, simulation verification is carried out based on the digital twinning model, and the initial point cloud working space is constructed by using a certain amount of data, so that the consumption of computing resources is reduced, and the construction efficiency is improved. Through the processes of point cloud space contour extraction, point cloud working space precise topology and boundary point cloud correction, the situation of point cloud non-uniformity caused by errors and random sampling is reduced, and through the mode of triangular mesh rolling generation and multi-pass smooth modeling, the point cloud visualization accuracy is improved. The method provided by the invention has relatively large universality and application range.

Description

Technical Field

[0001] The present invention relates to the application field of digital twin technology, and in particular to a method and system for constructing a robot workspace visualization based on digital twin. Background Art

[0002] With the rise of the wave of intelligent manufacturing, the manufacturing industry is constantly transforming and upgrading towards intelligence, digitization, and automation. Robots are widely used. The personalized and diversified characteristics of the upstream demand side of products lead to frequent changes in production and lines in the downstream production process. Changes in the robot's working environment increase hidden dangers in its production, and it is necessary to ensure that the robot can reach the working point. Related technologies use robot workspace visualization software to monitor the robot's operation in real time.

[0003] However, there are the following problems in constructing a robot visual workspace under the existing technology:

[0004] 1. Lack of near-physical simulation operation testing and real-time performance; the platform mainly uses MATLAB software to build a visualization space, and is unable to make corresponding changes and adaptations to the production environment, and thus is unable to adjust the actual robot operations;

[0005] Second, the robot visualization workspace is three-dimensional, with a large amount of calculation data, slow convergence, and large data noise. The workspace formed by a single algorithm such as the Monte Carlo algorithm after calculation and processing has large errors and high complexity costs;

[0006] 3. The conversion from point cloud to mesh is complex, low in accuracy, and not smooth enough.

[0007] Therefore, it is necessary to provide a new method for visualizing and constructing robot workspace to solve the above technical problems. Summary of the invention

[0008] The present invention aims to solve the technical problems of large computational complexity, low precision and poor real-time performance in the robot visualization workspace constructed by the prior art.

[0009] In order to solve the above technical problems, in a first aspect, the present invention provides a method for visually constructing a robot workspace based on digital twins, comprising the following steps:

[0010] S1. Construct a corresponding robot digital twin model according to the motion joint structure of the physical robot, wherein the motion joint structure includes a joint angle, a connecting rod offset, a connecting rod length, a connecting rod torsion angle, and a joint angle range;

[0011] S2. Constructing a total kinematic transformation matrix of the robot end according to the workspace of the robot digital twin model, wherein the workspace of the robot digital twin model is a set of points in the space that can be reached by the reference point describing the motion of the robot end effector;

[0012] S3, generating initial point cloud workspace data according to the total kinematic transformation matrix of the robot terminal and a preset point cloud generation method;

[0013] S4, extracting a point cloud space contour from the initial point cloud workspace;

[0014] S5, generating accurate point cloud workspace data according to the point cloud space contour;

[0015] S6, performing boundary point cloud correction on the precise point cloud workspace data using a preset correction method to obtain corrected point cloud workspace data;

[0016] S7. Perform surface reconstruction based on the calibrated point cloud workspace data to obtain a visualization data model of the workspace of the physical robot.

[0017] Furthermore, the total kinematic transformation matrix of the robot end is defined as 0 T m , which satisfies the following relation (1):

[0018]

[0019] Where m represents the number of robot axes. i-1 T i Represents the homogeneous transformation matrix that transforms between the i-th axis and the i-1-th axis of the robot.

[0020] Furthermore, the preset point cloud generation method is a Monte Carlo method, and step S3 includes the following sub-steps:

[0021] S31, setting a preset number of iterations and starting iteration;

[0022] S32, obtain a random joint angle by adding random values, and assign the random joint angle to the joint angle, define the random joint angle as θ, the random value as rand, and the joint angle interval range as [θmin, θmax], then the random joint angle θ satisfies the following relationship (2):

[0023] θ=θ min +(θ max -θ min )*rand (2);

[0024] S33, obtaining a homogeneous transformation matrix corresponding to the joint angle, and substituting the homogeneous transformation matrix into the robot terminal kinematics total transformation matrix to obtain corresponding terminal position and posture data;

[0025] S34, update the current number of iterations, and determine whether the preset number of iterations is reached. If not, return to step S32; if so, output all the terminal pose data as the initial point cloud workspace data.

[0026] Furthermore, the preset point cloud generation method is a simulation driven debugging method, and step S3 includes the following sub-steps:

[0027] S31, setting a preset number of iterations and starting iteration;

[0028] S32, assigning the joint angle by a fixed step size, defining the joint angle as θ, the step size as step, and the joint angle interval as [θmin, θmax], then the joint angle θ satisfies the following relationship (3):

[0029] θ=(θ max -θ min ) / step (3);

[0030] S33, obtaining the joint angle to drive the physical robot to obtain corresponding terminal position and posture data;

[0031] S34, update the current number of iterations, and determine whether the preset number of iterations is reached. If not, return to step S32; if so, output all the terminal pose data as the initial point cloud workspace data.

[0032] Furthermore, step S4 includes the following sub-steps:

[0033] S41, based on the longitude and latitude scanning method, the initial point cloud workspace data is layered from bottom to top to obtain multiple planes perpendicular to the Z axis in the spatial coordinate system, point cloud scanning is performed in each plane, and points in the initial point cloud workspace data intersecting with the longitude and latitude are marked to obtain a first scanning result;

[0034] S42, based on the longitude and latitude scanning method, the initial point cloud workspace data is layered from left to right to obtain multiple planes perpendicular to the X-axis in the spatial coordinate system, point cloud scanning is performed in each layer of longitude and latitude, and points in the initial point cloud workspace data that intersect with the longitude and latitude are obtained and marked to obtain a second scanning result;

[0035] S43: Integrate and remove duplicates of the first scanning result and the second scanning result to obtain the point cloud spatial contour.

[0036] Furthermore, step S5 includes the following sub-steps:

[0037] S51, dividing the point cloud space outline into a plurality of point cloud regions of equal volume by using a three-dimensional rasterization method;

[0038] S52, filling each of the point cloud areas with a sphere;

[0039] S53, performing sphere traversal in each of the point cloud areas, determining whether the center position of the current sphere has the corresponding point cloud space contour, and if not, calculating the analytical joint angle of the center position of the sphere by a preset robot inverse kinematics method, wherein if the analytical joint angle is within the joint angle interval, adding the posture data corresponding to the analytical joint angle as new point cloud data into the initial point cloud workspace;

[0040] S54: After completing the sphere traversal of all the point cloud areas, output the current initial point cloud workspace as the precise initial point cloud workspace.

[0041] Furthermore, the preset robot inverse kinematics method is at least one of an algebraic method, a geometric method, and a numerical method.

[0042] Furthermore, the preset correction method is longitude and latitude scanning, and step S6 is specifically as follows:

[0043] Based on the longitude and latitude scanning method, the precise point cloud workspace data is layered from bottom to top to obtain multiple planes perpendicular to the Z axis in the spatial coordinate system. Point cloud scanning is performed in each plane, and points in the precise point cloud workspace data that do not intersect with the longitude and latitude are deleted.

[0044] Furthermore, step S7 includes the following sub-steps:

[0045] S71, randomly selecting a point cloud data in the precise point cloud workspace data as a starting point pi;

[0046] S72, using the starting point pi as the sphere center and searching other point cloud data within a fixed radius a to obtain a spherical domain point set;

[0047] S73, connecting the starting point pi with any point qi in the spherical domain point set, and generating two circumscribed circles passing through the point pi and the point qi at the same time and with a radius a. If any of the circumscribed circles does not contain other point cloud data in the precise point cloud workspace data, then connecting the point pi with qi to generate a model edge;

[0048] S74, selecting point qi+1 in the spherical domain point set, and returning to step S73, until all points in the spherical domain point set are selected and the step S73 is executed;

[0049] S75, selecting the next starting point pi+1, and returning to step S72, until all points in the precise point cloud workspace data are selected and steps S72-S74 are executed;

[0050] S76. Output all the model edges as the visualization data model.

[0051] In a second aspect, the present invention further provides a robot workspace visualization construction system based on digital twins, comprising:

[0052] A digital twin modeling module is used to construct a corresponding robot digital twin model according to the motion joint structure of the physical robot, wherein the motion joint structure includes joint angle, connecting rod offset, connecting rod length, connecting rod torsion angle and joint angle range;

[0053] A kinematic modeling module, used to construct a total kinematic transformation matrix of the robot end according to the workspace of the robot digital twin model, wherein the workspace of the robot digital twin model is a set of points in the space that can be reached by the reference point describing the motion of the robot end effector;

[0054] A point cloud generation module, used to generate initial point cloud workspace data according to the total kinematic transformation matrix of the robot terminal and a preset point cloud generation method;

[0055] A point cloud extraction module, used to extract a point cloud space contour from the initial point cloud workspace;

[0056] A point cloud precision module, used for generating precise point cloud workspace data according to the point cloud space contour;

[0057] A point cloud correction module, used to perform boundary point cloud correction on the precise point cloud workspace data using a preset correction method to obtain corrected point cloud workspace data;

[0058] A visualization module is used to perform surface reconstruction based on the corrected point cloud workspace data to obtain a visualization data model of the workspace of the physical robot.

[0059] The beneficial effect achieved by the present invention is that a method for constructing a robot workspace visualization based on digital twins is proposed. The method performs simulation verification based on a digital twin model, and constructs an initial point cloud workspace with a certain amount of data, thereby reducing the consumption of computing resources. The point cloud space contour is extracted, the point cloud workspace is accurately topologically supplemented, and the boundary point cloud is corrected, thereby reducing the point cloud unevenness caused by errors and random sampling. The accuracy of point cloud visualization is improved by rolling to generate triangular meshes and multiple pass smoothing modeling. The method of the present invention has great versatility and scope of application. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a flowchart of the steps of a method for visualizing a robot workspace based on digital twins provided in an embodiment of the present invention;

[0061] Figure 2 is a schematic diagram of the initial point cloud workspace data obtained by using the Monte Carlo method provided by an embodiment of the present invention;

[0062] Figure 3 is a schematic diagram of the initial point cloud workspace data obtained by using the simulation driven debugging method provided by an embodiment of the present invention;

[0063] Figure 4 is with Figure 2 Schematic diagram of the corresponding longitude and latitude scanning results;

[0064] Figure 5 is with Figure 3 Schematic diagram of the corresponding longitude and latitude scanning results;

[0065] Figure 6 It is a schematic diagram of boundary point cloud correction provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0067] Please refer to Figure 1 , Figure 1 The present invention provides a flowchart of a method for visualizing a robot workspace based on digital twins, which includes the following steps:

[0068] S1. Construct a corresponding robot digital twin model according to the motion joint structure of the physical robot, wherein the motion joint structure includes joint angle, connecting rod offset, connecting rod length, connecting rod torsion angle and joint angle range.

[0069] Specifically, for different types of robots, the embodiments of the present invention use three-dimensional modeling software to establish a corresponding robot digital twin model during the implementation process, and the model should maintain a kinematic relationship corresponding to the real object, that is, each motion joint of the robot needs to be modeled independently.

[0070] S2. Construct a total kinematic transformation matrix of the robot end according to the workspace of the robot digital twin model. The workspace of the robot digital twin model is a set of points in the space that can be reached by the reference point describing the motion of the robot end effector.

[0071] The total kinematic transformation matrix of the robot end is defined as 0 T m , which satisfies the following relation (1):

[0072]

[0073] Where m represents the number of robot axes. i-1 T i Represents the homogeneous transformation matrix that transforms between the i-th axis and the i-1-th axis of the robot.

[0074] S3. Generate initial point cloud workspace data according to the total kinematic transformation matrix of the robot terminal and a preset point cloud generation method.

[0075] The purpose of step S3 is to change the joint angles of each joint to change the position and posture of each joint of the robot, and to obtain the position and posture of the end effector by using matrix transformation, so as to obtain the initial reachable workspace of the robot.

[0076] The embodiment of the present invention uses two methods to realize point cloud generation. The first method is the Monte Carlo method. Correspondingly, step S3 includes the following sub-steps:

[0077] S31, setting a preset number of iterations and starting iteration;

[0078] S32, obtain a random joint angle by adding random values, and assign the random joint angle to the joint angle, define the random joint angle as θ, the random value as rand, and the joint angle interval range as [θmin, θmax], then the random joint angle θ satisfies the following relationship (2):

[0079] θ=θ min +(θ max -θ min )*rand (2);

[0080] S33, obtaining a homogeneous transformation matrix corresponding to the joint angle, and substituting the homogeneous transformation matrix into the robot terminal kinematics total transformation matrix to obtain corresponding terminal position and posture data;

[0081] S34, update the current number of iterations, and determine whether the preset number of iterations is reached. If not, return to step S32; if so, output all the terminal pose data as the initial point cloud workspace data.

[0082] The initial point cloud workspace data obtained by the Monte Carlo method is as follows: Figure 2 shown.

[0083] The second method is a simulation driven debugging method. Accordingly, step S3 includes the following sub-steps:

[0084] S31, setting a preset number of iterations and starting iteration;

[0085] S32, assigning the joint angle by a fixed step size, defining the joint angle as θ, the step size as step, and the joint angle interval as [θmin, θmax], then the joint angle θ satisfies the following relationship (3):

[0086] θ=(θ m ax-θ min ) / step (3));

[0087] S33, obtaining the joint angle to drive the physical robot to obtain corresponding terminal position and posture data;

[0088] S34, update the current number of iterations, and determine whether the preset number of iterations is reached. If not, return to step S32; if so, output all the terminal pose data as the initial point cloud workspace data.

[0089] The initial point cloud workspace data obtained by the simulation driven debugging method is as follows: Figure 3 shown.

[0090] S4. Extracting a point cloud space contour from the initial point cloud workspace.

[0091] After completing step S3, since what is obtained is the initial workspace and a point cloud collection, it cannot well reflect the robot's workspace. In order to accurately topology the point cloud workspace, it is necessary to perform contour extraction on the initial workspace to provide a basic boundary for topology. Point cloud space contour extraction involves three-dimensional space. It is necessary to scan the x, y, and z directions respectively, and compare the coordinate data of the point cloud to obtain the boundary contour. At the same time, due to the particularity of the robot's workspace, it may have an internal contour, so it is necessary to extract both the internal and external contours during extraction.

[0092] Step S4 includes the following sub-steps:

[0093] S41, based on the longitude and latitude scanning method, the initial point cloud workspace data is layered from bottom to top to obtain multiple planes perpendicular to the Z axis in the spatial coordinate system, point cloud scanning is performed in each plane, and points in the initial point cloud workspace data intersecting with the longitude and latitude are marked to obtain a first scanning result;

[0094] S42, based on the longitude and latitude scanning method, the initial point cloud workspace data is layered from left to right to obtain multiple planes perpendicular to the X-axis in the spatial coordinate system, point cloud scanning is performed in each layer of longitude and latitude, and points in the initial point cloud workspace data that intersect with the longitude and latitude are obtained and marked to obtain a second scanning result;

[0095] S43: Integrate and remove duplicates of the first scanning result and the second scanning result to obtain the point cloud spatial contour.

[0096] Specifically, when scanning, first establish the minimum bounding box of the point set in the two-dimensional plane, and calculate the maximum and minimum values ​​of the point cloud data on the X, Y and Z axes respectively; let the meridian start from the minimum value of X, take the step length of dx, calculate all the points where each meridian intersects within the value range of X, and put their index values ​​in a newly created linked list to scan the entire X interval; based on the same method, scan the latitude.

[0097] The longitude and latitude scanning results obtained by scanning the initial point cloud workspace data obtained by the Monte Carlo method are as follows: Figure 4 As shown, the longitude and latitude scanning results obtained by scanning the initial point cloud workspace data obtained by the simulation driven debugging method are as follows Figure 5 shown.

[0098] S5. Generate precise point cloud workspace data according to the point cloud space contour.

[0099] Step S5 includes the following sub-steps:

[0100] S51, dividing the point cloud space outline into a plurality of point cloud regions of equal volume by using a three-dimensional rasterization method;

[0101] S52, filling each of the point cloud areas with a sphere;

[0102] S53, performing sphere traversal in each of the point cloud areas, determining whether the center position of the current sphere has the corresponding point cloud space contour, and if not, calculating the analytical joint angle of the center position of the sphere by a preset robot inverse kinematics method, wherein if the analytical joint angle is within the joint angle interval, adding the posture data corresponding to the analytical joint angle as new point cloud data into the initial point cloud workspace; the preset robot inverse kinematics method is at least one of an algebraic method, a geometric method, and a numerical method;

[0103] S54: After completing the sphere traversal of all the point cloud areas, output the current initial point cloud workspace as the precise initial point cloud workspace.

[0104] Preferably, in step S53, analytical methods such as algebraic method and geometric method are preferably used for calculation and solution to obtain a faster solution speed.

[0105] S6. Perform boundary point cloud correction on the precise point cloud workspace data using a preset correction method to obtain corrected point cloud workspace data.

[0106] The preset correction method is longitude and latitude scanning, and step S6 is specifically as follows:

[0107] Based on the longitude and latitude scanning method, the precise point cloud workspace data is layered from bottom to top to obtain multiple planes perpendicular to the Z axis in the spatial coordinate system. Point cloud scanning is performed in each plane, and points in the precise point cloud workspace data that do not intersect with the longitude and latitude are deleted.

[0108] The schematic diagram of performing boundary point cloud correction on the precise point cloud workspace data is as follows: Figure 6 As shown, the points in the lower left part are the points that need to be deleted.

[0109] S7. Perform surface reconstruction based on the calibrated point cloud workspace data to obtain a visualization data model of the workspace of the physical robot.

[0110] The embodiment of the present invention uses the rolling ball method to realize the surface reconstruction of the visualization data model. Step S7 includes the following sub-steps:

[0111] S71, randomly selecting a point cloud data in the precise point cloud workspace data as a starting point pi;

[0112] S72, using the starting point pi as the sphere center and searching other point cloud data within a fixed radius a to obtain a spherical domain point set;

[0113] S73, connecting the starting point pi with any point qi in the spherical domain point set, and generating two circumscribed circles passing through the point pi and the point qi at the same time and with a radius a. If any of the circumscribed circles does not contain other point cloud data in the precise point cloud workspace data, then connecting the point pi with qi to generate a model edge;

[0114] S74, selecting point qi+1 in the spherical domain point set, and returning to step S73, until all points in the spherical domain point set are selected and the step S73 is executed;

[0115] S75, selecting the next starting point pi+1, and returning to step S72, until all points in the precise point cloud workspace data are selected and steps S72-S74 are executed;

[0116] S76. Output all the model edges as the visualization data model.

[0117] The beneficial effect achieved by the present invention is that a method for constructing a robot workspace visualization based on digital twins is proposed. The method performs simulation verification based on a digital twin model, and constructs an initial point cloud workspace with a certain amount of data, thereby reducing the consumption of computing resources. The point cloud space contour is extracted, the point cloud workspace is accurately topologically supplemented, and the boundary point cloud is corrected, thereby reducing the point cloud unevenness caused by errors and random sampling. The accuracy of point cloud visualization is improved by rolling to generate triangular meshes and multiple pass smoothing modeling. The method of the present invention has great versatility and scope of application.

[0118] The present invention also provides a robot workspace visualization construction system based on digital twins, comprising:

[0119] A digital twin modeling module is used to construct a corresponding robot digital twin model according to the motion joint structure of the physical robot, wherein the motion joint structure includes joint angle, connecting rod offset, connecting rod length, connecting rod torsion angle and joint angle range;

[0120] A kinematic modeling module, used to construct a total kinematic transformation matrix of the robot end according to the workspace of the robot digital twin model, wherein the workspace of the robot digital twin model is a set of points in the space that can be reached by the reference point describing the motion of the robot end effector;

[0121] A point cloud generation module, used to generate initial point cloud workspace data according to the total kinematic transformation matrix of the robot terminal and a preset point cloud generation method;

[0122] A point cloud extraction module, used to extract a point cloud space contour from the initial point cloud workspace;

[0123] A point cloud precision module, used for generating precise point cloud workspace data according to the point cloud space contour;

[0124] A point cloud correction module, used to perform boundary point cloud correction on the precise point cloud workspace data using a preset correction method to obtain corrected point cloud workspace data;

[0125] A visualization module is used to perform surface reconstruction based on the corrected point cloud workspace data to obtain a visualization data model of the workspace of the physical robot.

[0126] The robot workspace visualization construction system based on digital twins can implement the steps in the robot workspace visualization construction method based on digital twins in the above-mentioned embodiments, and can achieve the same technical effects. Please refer to the description in the above-mentioned embodiments and will not be repeated here.

[0127] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0128] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0129] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0130] The embodiments of the present invention are described above in conjunction with the accompanying drawings. What is disclosed is only the preferred embodiment of the present invention. However, the present invention is not limited to the above-mentioned specific implementation manner. The above-mentioned specific implementation manner is only illustrative rather than restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms and equivalent changes without departing from the scope of protection of the purpose of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A method for visualizing a robot workspace based on digital twins, characterized in that: The following steps are involved: S1. Construct a corresponding robot digital twin model according to the motion joint structure of the physical robot, wherein the motion joint structure includes a joint angle, a connecting rod offset, a connecting rod length, a connecting rod torsion angle, and a joint angle range; S2. Constructing a total kinematic transformation matrix of the robot end according to the workspace of the robot digital twin model, wherein the workspace of the robot digital twin model is a set of points in the space that can be reached by the reference point describing the motion of the robot end effector; S3, generating initial point cloud workspace data according to the total kinematic transformation matrix of the robot terminal and a preset point cloud generation method; S4, extracting a point cloud space contour from the initial point cloud workspace; S5, generating accurate point cloud workspace data according to the point cloud space contour; S6, performing boundary point cloud correction on the precise point cloud workspace data using a preset correction method to obtain corrected point cloud workspace data; S7. Perform surface reconstruction based on the calibrated point cloud workspace data to obtain a visualization data model of the workspace of the physical robot.

2. The method for visualizing a robot workspace based on digital twins according to claim 1, characterized in that: The total kinematic transformation matrix of the robot end is defined as 0 T m , which satisfies the following relation (1): Where m represents the number of robot axes. i-1 T i Represents the homogeneous transformation matrix that transforms between the i-th axis and the i-1-th axis of the robot.

3. The method for constructing a robot workspace visualization based on digital twins according to claim 1, characterized in that: The preset point cloud generation method is the Monte Carlo method, and step S3 includes the following sub-steps: S31, setting a preset number of iterations and starting iteration; S32, obtain a random joint angle by adding random values, and assign the random joint angle to the joint angle, define the random joint angle as θ, the random value as rand, and the joint angle interval range as [θmin, θmax], then the random joint angle θ satisfies the following relationship (2): θ=θ min +(θ max -θ min )*rand (2); S33, obtaining a homogeneous transformation matrix corresponding to the joint angle, and substituting the homogeneous transformation matrix into the robot terminal kinematics total transformation matrix to obtain corresponding terminal position and posture data; S34, updating the current number of iterations, and determining whether the preset number of iterations has been reached, if not, returning to step S32; If so, output all the terminal pose data as the initial point cloud workspace data.

4. The method for visualizing a robot workspace based on digital twins according to claim 1, characterized in that: The preset point cloud generation method is a simulation driven debugging method, and step S3 includes the following sub-steps: S31, setting a preset number of iterations and starting iteration; S32, assigning the joint angle by a fixed step size, defining the joint angle as θ, the step size as step, and the joint angle interval as [θmin, θmax], then the joint angle θ satisfies the following relationship (3): θ=(θ max -θ min ) / step (3); S33, obtaining the joint angle to drive the physical robot to obtain corresponding terminal position and posture data; S34, updating the current number of iterations, and determining whether the preset number of iterations has been reached, if not, returning to step S32; If so, output all the terminal pose data as the initial point cloud workspace data.

5. The method for visualizing a robot workspace based on digital twins according to claim 1, characterized in that: Step S4 includes the following sub-steps: S41, based on the longitude and latitude scanning method, the initial point cloud workspace data is layered from bottom to top to obtain multiple planes perpendicular to the Z axis in the spatial coordinate system, point cloud scanning is performed in each plane, and points in the initial point cloud workspace data intersecting with the longitude and latitude are marked to obtain a first scanning result; S42, based on the longitude and latitude scanning method, the initial point cloud workspace data is layered from left to right to obtain multiple planes perpendicular to the X-axis in the spatial coordinate system, point cloud scanning is performed in each layer of longitude and latitude, and points in the initial point cloud workspace data that intersect with the longitude and latitude are obtained and marked to obtain a second scanning result; S43: Integrate and remove duplicates of the first scanning result and the second scanning result to obtain the point cloud spatial contour.

6. The method for visualizing a robot workspace based on digital twins according to claim 1, characterized in that: Step S5 includes the following sub-steps: S51, dividing the point cloud space outline into a plurality of point cloud regions of equal volume by using a three-dimensional rasterization method; S52, filling each of the point cloud areas with a sphere; S53, performing sphere traversal in each of the point cloud areas, determining whether the center position of the current sphere has the corresponding point cloud space contour, and if not, calculating the analytical joint angle of the center position of the sphere by a preset robot inverse kinematics method, wherein if the analytical joint angle is within the joint angle interval, adding the posture data corresponding to the analytical joint angle as new point cloud data into the initial point cloud workspace; S54: After completing the sphere traversal of all the point cloud areas, output the current initial point cloud workspace as the precise initial point cloud workspace.

7. The method for visualizing a robot workspace based on digital twins according to claim 6, characterized in that: The preset robot inverse kinematics method is at least one of an algebraic method, a geometric method, and a numerical method.

8. The method for visualizing a robot workspace based on digital twins according to claim 1, characterized in that: The preset correction method is longitude and latitude scanning, and step S6 is specifically as follows: Based on the longitude and latitude scanning method, the precise point cloud workspace data is layered from bottom to top to obtain multiple planes perpendicular to the Z axis in the spatial coordinate system. Point cloud scanning is performed in each plane, and points in the precise point cloud workspace data that do not intersect with the longitude and latitude are deleted.

9. The method for visualizing a robot workspace based on digital twins according to claim 1, characterized in that: Step S7 includes the following sub-steps: S71, randomly selecting a point cloud data in the precise point cloud workspace data as a starting point pi; S72, using the starting point pi as the sphere center and searching other point cloud data within a fixed radius a to obtain a spherical domain point set; S73, connecting the starting point pi with any point qi in the spherical domain point set, and generating two circumscribed circles passing through the point pi and the point qi at the same time and with a radius a. If any of the circumscribed circles does not contain other point cloud data in the precise point cloud workspace data, then connecting the point pi with qi to generate a model edge; S74, selecting point qi+1 in the spherical domain point set, and returning to step S73, until all points in the spherical domain point set are selected and the step S73 is executed; S75, selecting the next starting point pi+1, and returning to step S72, until all points in the precise point cloud workspace data are selected and steps S72-S74 are executed; S76. Output all the model edges as the visualization data model.

10. A robot workspace visualization construction system based on digital twins, characterized in that: include: A digital twin modeling module is used to construct a corresponding robot digital twin model according to the motion joint structure of the physical robot, wherein the motion joint structure includes joint angle, connecting rod offset, connecting rod length, connecting rod torsion angle and joint angle range; A kinematic modeling module, used to construct a total kinematic transformation matrix of the robot end according to the workspace of the robot digital twin model, wherein the workspace of the robot digital twin model is a set of points in the space that can be reached by the reference point describing the motion of the robot end effector; A point cloud generation module, used to generate initial point cloud workspace data according to the total kinematic transformation matrix of the robot terminal and a preset point cloud generation method; A point cloud extraction module, used to extract a point cloud space contour from the initial point cloud workspace; A point cloud precision module, used for generating precise point cloud workspace data according to the point cloud space contour; A point cloud correction module, used to perform boundary point cloud correction on the precise point cloud workspace data using a preset correction method to obtain corrected point cloud workspace data; A visualization module is used to perform surface reconstruction based on the corrected point cloud workspace data to obtain a visualization data model of the workspace of the physical robot.

Images