A method for online optical measurement of robot automated loading and unloading

Abstract

The present invention relates to the technical field of three-dimensional scanning detection, and specifically, to a method for online optical measurement of automated loading and unloading of robots. The present invention comprises installing a camera and a sensor on an electric gripper of a robot, processing and identifying and classifying images of tooling, collecting acceleration and angular velocity during the movement of the tooling, integrating displacement and angle change, establishing a gain function with measurement error, modeling the robot workspace, taking the motion path planning of the electric gripper as a target and performing path planning, controlling the movement of the electric gripper according to the planned path, monitoring and comparing acceleration and angular velocity in real time, and adjusting the gain function if there is a deviation. The method improves production efficiency and measurement accuracy through automated and intelligent means, reduces manual intervention, and can more accurately complete loading and unloading and online optical measurement tasks.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional scanning detection, and in particular to a method for online optical measurement of robot automated loading and unloading. Background Art

[0002] 3D scanning and inspection is an important technology. In the field of 3D scanning and inspection, traditional loading and unloading and measurement methods usually rely on manual operation, which has problems such as low efficiency, low precision, and high labor intensity. Manual operation is easily affected by human factors, making it difficult to ensure the consistency and accuracy of measurement results. In addition, traditional methods are difficult to meet the needs of fast and accurate measurement when facing mass production and complex working environments.

[0003] Due to the lack of automated and intelligent technical means, the existing technology cannot achieve automatic identification and classification of tooling, accurate path planning and motion control, which greatly limits the efficiency and accuracy of the production process. At the same time, traditional methods are insufficient in real-time monitoring and deviation adjustment, and cannot promptly detect and correct deviations in the robot's motion process, thus affecting the accuracy and reliability of the measurement. In order to solve this technical problem, we provide a method for online optical measurement of robot automated loading and unloading. Summary of the invention

[0004] The purpose of the present invention is to provide a method for online optical measurement of robot automated loading and unloading materials to solve the problems raised in the above background technology.

[0005] To achieve the above object, one of the objects of the present invention is to provide a method for online optical measurement of robot automated loading and unloading, comprising the following steps:

[0006] S1. Install a micro camera and a sensor on the electric gripper of the robot, wherein the micro camera is used to capture a tooling image, and grayscale and median filter the tooling image to obtain a pre-processed tooling image, and input the pre-processed tooling image into a tooling recognition model, and the tooling recognition model recognizes and classifies the pre-processed tooling image;

[0007] S2. After the identification and classification are completed, the robot electric gripper grabs the tooling from the material table to the turntable. The sensor on the robot electric gripper collects the acceleration and angular velocity of the tooling during the movement from the material table to the turntable, and obtains the displacement and angle change of the robot electric gripper according to the integration of the acceleration and angular velocity, and establishes a gain function with the measurement error;

[0008] S3, modeling the robot workspace, taking the motion path planning of the robot's electric gripper from the initial position to the tooling position and then to the scanner position as the goal, and using the path planning algorithm to plan the path of the robot gripper;

[0009] S4. Control the movement of the robot's electric gripper according to the planned path, monitor its acceleration and angular velocity in real time during movement and compare them with the planned path. If there is a deviation from the planned path, adjust the robot's electric gripper through the gain function.

[0010] As a further improvement of the technical solution, the micro camera in S1 is used to capture the tooling image, and grayscale and median filter the tooling image, specifically including:

[0011] The neural network is used to learn the distribution law of color features of different tooling images, and the weight of each pixel in the tooling image converted to grayscale value is automatically adjusted. After the grayscale conversion of the tooling image is completed, the tooling image is divided into several sub-regions of fixed size, and the variance of the pixel values ​​in each sub-region is calculated. The average value of the variance is used as the threshold. The area above the threshold is defined as a complex area, and the area below the threshold is defined as a smooth area. For the sub-regions of the complex area, the window size is set to , for the sub-region of the smooth region, the window size is set to , The window size is used to preserve the details of complex areas. The window size is used to remove noise.

[0012] As a further improvement of the technical solution, in S1, the pre-processed tooling image is input into the tooling recognition model, and the tooling recognition model recognizes and classifies the pre-processed tooling image in a manner that specifically includes:

[0013] Standard tool images of various types of tooling are collected as templates, each standard tooling image is grayed and median filtered, and the edge contour features of the standard tooling image are extracted through the edge detection algorithm to establish a tooling recognition model. When the robot electric gripper grasps the tooling from the material table, the micro camera on the robot electric gripper collects the tooling image to be identified and extracts the contour features of the tooling image to be identified. The contour features of the tooling image to be identified are compared with the edge contour features of the standard tooling image in the tooling recognition model for similarity. According to the similarity value, the tooling image to be identified is identified and classified. If other tools that do not conform to the batch appear, the workbench stops working and an alarm sounds.

[0014] As a further improvement of the technical solution, the sensor on the robot electric gripper in S2 collects the acceleration and angular velocity of the tooling during the process of grabbing the tooling from the material table to the turntable, specifically including:

[0015] The sensor is installed at the joint of the robot's electric gripper and connected to the robot's control system. When the robot gripper starts to grab and move the tooling, the sensor's data acquisition function is triggered, and the collected acceleration and angular velocity are temporarily stored in the control system cache. The noise is removed by using a second-order low-pass filtering algorithm, and then the numerical code output by the acceleration sensor is converted into acceleration values ​​in meters per second squared and angular velocity values ​​in radians per second according to the sensor's sensitivity coefficient, so as to obtain the acceleration and angular velocity of the robot's electric gripper in the process of grabbing and moving from the material table to the turntable.

[0016] As a further improvement of the technical solution, the method of obtaining the displacement and angle change of the robot electric gripper according to the integration of acceleration and angular velocity in S2 and establishing a gain function with the measurement error specifically includes:

[0017] When the robot's electric gripper grabs the tooling and moves from the material table to the turntable, the sensor on the gripper measures the acceleration in real time. and angular velocity , assuming that the initial speed of the robot electric gripper is , initial position The position of the material table. In the process of moving from the material table to the turntable, the acceleration Integrate to get the speed, , and then the speed The displacement of the robot electric gripper in space is obtained by integration. ,in, is represented as the inner integral variable, Represented as different moments The instantaneous acceleration of the robot's electric gripper is , angular velocity Integrate to get the angle change of the robot electric gripper during movement , ;

[0018] During the measurement process, there is a measurement error. According to the historical data, the influence coefficient of the displacement change on the final position of the measuring tool is: , the influence coefficient of the angle change on the posture measurement of the tooling is , the environmental impact coefficient is , then the gain function of the measurement error is expressed as:

[0019] ;

[0020] in, Expressed as a gain function of the measurement error.

[0021] As a further improvement of the technical solution, the method of modeling the robot workspace in S3 specifically includes:

[0022] The entire movement process of the robot's electric gripper from the initial position to the tooling position and then to the scanner position is compared to a circular motion. The circular motion is that the robot's arm rotates around a fixed axis and has a telescopic range in a plane perpendicular to the rotation axis. The rotation radius and height range are measured, and the rotation radius is recorded as , the height range is recorded as , then the expression for modeling the robot workspace is ,in, are the three coordinate axes of the three-dimensional space.

[0023] As a further improvement of the technical solution, the method of taking the motion path planning of the robot's electric gripper from the initial position to the tooling position and then to the scanner position as the goal and using the path planning algorithm to plan the path of the robot gripper has the following specific steps:

[0024] S3.1, record the initial position of the robot electric gripper as , the tooling position is recorded as and the scanner position is recorded as , calculate the straight line equation of the robot electric gripper from the initial position to the tooling position;

[0025] S3.2. Discretize the path points along this line with a step size of 0.1 to obtain a series of intermediate points. For each intermediate point, check whether it meets the cylindrical space working constraints. If not, move the excess points along the boundary of the cylinder to a legal position. The range of the legal position is ;

[0026] S3.3. Repeat the above steps to calculate the straight line parameter equation of the robot gripper from the tooling position to the scanner position, and combine the straight line equation of the robot electric gripper from the initial position to the tooling position and the straight line equation from the tooling position to the scanner position to obtain the planned path.

[0027] As a further improvement of the technical solution, the method of adjusting the electric gripper of the robot by the gain function in S4 specifically includes:

[0028] According to the path planning algorithm, the planned path of the robot's electric gripper from the initial position to the tooling position and then to the scanner position is obtained. The planned path consists of a series of discrete points, and the spatial coordinates corresponding to each point are , compare the calculated displacement and angle with the position and posture at the corresponding time in the planned path, and set the position coordinates at the corresponding time on the planned path to be , the attitude angle is , the position deviations are , as well as , the angle deviation is , the measurement error gain function Substituting in, the adjusted speed and angular velocity are:

[0029] ;

[0030] ;

[0031] The robot control system controls the movement of the electric gripper according to the adjusted speed and angular velocity, so that it returns to the planned path and reaches the final scanner position.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] In a method for online optical measurement of robot automatic loading and unloading, the robot automatic loading and unloading and online optical measurement are realized, the production efficiency is improved, the error and labor intensity caused by manual operation are reduced, the tooling image is recognized and classified through the tooling recognition model, different types of tooling can be accurately grasped and processed, the accuracy and reliability of production are improved, the robot workspace is modeled, and a path planning algorithm is adopted, the shortest path distance and motion smoothness are considered, the robot's motion path can be optimized, the work efficiency and stability are improved, the movement of the robot's electric gripper is monitored in real time, and the gain function is adjusted to correct the deviation from the planned path in time, ensure that the robot accurately reaches the target position, and improve the accuracy of measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is the overall workflow diagram of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Example 1

[0037] See also Figure 1 As shown, this embodiment provides a method for online optical measurement of robot automated loading and unloading, comprising the following steps:

[0038] S1. Install a micro camera and a sensor on the electric gripper of the robot. The micro camera is used to capture a tooling image, and grayscale and median filter the tooling image to obtain a pre-processed tooling image. The pre-processed tooling image is input into a tooling recognition model. The tooling recognition model recognizes and classifies the pre-processed tooling image.

[0039] Different tooling images have different color feature distribution patterns. By learning these patterns through neural networks and automatically adjusting the grayscale weights, color images can be converted into grayscale images more accurately, highlighting the important features of the tooling.

[0040] By dividing the tooling image into sub-regions and calculating the variance, the complexity of the region can be judged based on the local features of the image. Complex regions usually contain more details and texture information, and a smaller window is required to retain these details; while the noise in smooth regions is relatively small, and a larger window can be used to remove the noise while reducing the amount of calculation.

[0041] The micro camera in S1 is used to capture the tooling image, and grayscale and median filter the tooling image, specifically including:

[0042] The neural network is used to learn the distribution law of color features of different tooling images, and the weight of each pixel in the tooling image converted to grayscale value is automatically adjusted. After the grayscale conversion of the tooling image is completed, the tooling image is divided into several sub-regions of fixed size, and the variance of the pixel values ​​in each sub-region is calculated. The average value of the variance is used as the threshold. The area above the threshold is defined as a complex area, and the area below the threshold is defined as a smooth area. For the sub-regions of the complex area, the window size is set to , for the sub-region of the smooth region, the window size is set to , The window size is used to preserve the details of complex areas. The window size is used to remove noise, which can better adapt to different types of tooling images and improve the accuracy and reliability of processing.

[0043] In S1, the pre-processed tooling image is input into the tooling recognition model. The tooling recognition model recognizes and classifies the pre-processed tooling image in the following manner:

[0044] Collecting standard tooling images of various types of tooling as templates, graying and median filtering each standard tooling image can reduce noise and interference in the image, making the tooling image clearer, which is conducive to subsequent identification and classification. The edge detection algorithm is used to extract the edge contour features of the standard tooling image to establish a tooling recognition model. When the robot electric gripper grasps the tooling from the material table, the micro camera on the robot electric gripper collects the tooling image to be identified and extracts the contour features of the tooling image to be identified. The contour features of the tooling image to be identified are compared with the edge contour features of the standard tooling image in the tooling recognition model for similarity. According to the similarity value, the tooling image to be identified is identified and classified. If other tools that do not conform to the batch appear, the workbench stops working and issues an alarm. The optimized tooling image can be better identified and classified by the tooling recognition model, thereby improving the accuracy and efficiency of online optical measurement of robot automated loading and unloading.

[0045] S2. After the identification and classification are completed, the robot electric gripper grabs the tooling from the material table to the turntable. The sensor on the robot electric gripper collects the acceleration and angular velocity of the tooling during the movement from the material table to the turntable, and obtains the displacement and angle change of the robot electric gripper according to the integration of the acceleration and angular velocity, and establishes a gain function with the measurement error;

[0046] The sensors on the robot's electric gripper in S2 collect the acceleration and angular velocity of the tooling during its movement from the material table to the turntable, specifically including:

[0047] The sensor is installed at the joint of the robot's electric gripper, and the sensor is connected to the robot's control system. When the robot gripper starts to grab and move the tooling, the sensor's data acquisition function is triggered, and the collected acceleration and angular velocity are temporarily stored in the control system cache. Acceleration and angular velocity are important parameters for describing the motion state of an object. By collecting these data, we can better understand the motion of the gripper and provide a basis for subsequent path planning, control and error analysis. By using a second-order low-pass filtering algorithm to remove noise, we can effectively reduce the impact of environmental noise and interference on sensor data and improve data quality and reliability. Then, according to the sensor's sensitivity coefficient, the numerical code output by the acceleration sensor is converted into an acceleration value in meters per second squared and an angular velocity value in radians per second. The acceleration and angular velocity of the robot's electric gripper in the process of grabbing the material table and moving to the turntable are obtained. The numerical code output by the sensor can be converted into actual physical quantities, which is convenient for subsequent analysis and processing.

[0048] In order to more accurately describe the motion state of the robot electric gripper and establish a relationship with the measurement error, so as to achieve precise control of the robot motion and error correction, the acceleration is integrated to obtain the velocity, the velocity is integrated to obtain the displacement, and the angular velocity is integrated to obtain the angle change. This is based on the basic principles of physics and can accurately calculate the motion trajectory and posture change of the gripper in space. The advantage of establishing a gain function for the measurement error is that it can quantify the influence of displacement, angle change and environmental factors on the measurement error, thus providing a basis for error correction. By adjusting the coefficients in the gain function, the error can be compensated according to the actual situation to improve the accuracy of the measurement.

[0049] In S2, the displacement and angle change of the robot electric gripper are obtained according to the integration of acceleration and angular velocity, and the gain function of the measurement error is established, which specifically includes:

[0050] When the robot's electric gripper grabs the tooling and moves from the material table to the turntable, the sensor on the gripper measures the acceleration in real time. and angular velocity , assuming that the initial speed of the robot electric gripper is , initial position The position of the material table. In the process of moving from the material table to the turntable, the acceleration Integrate to get the speed, speed , and then the speed Integrate to get the displacement of the robot electric gripper in space. ,in, is represented as the inner integral variable, Represented as different moments The instantaneous acceleration of the robot's electric gripper is , angular velocity Integrate to get the angle change of the robot electric gripper during movement , , which can significantly improve the accuracy of robots in loading and unloading and online optical measurement processes;

[0051] During the measurement process, there is a measurement error. According to the historical data, the influence coefficient of the displacement change on the final position of the measuring tool is: , the influence coefficient of the angle change on the posture measurement of the tooling is , the environmental impact coefficient is , then the gain function of the measurement error is expressed as:

[0052] ;

[0053] in, The gain function expressed as the measurement error can timely discover and correct the measurement error, making the robot's movement more stable and reliable and reducing system failures caused by error accumulation. According to the gain function, a more reasonable control strategy can be formulated to enable the robot to better adapt to different working environments and task requirements.

[0054] S3, modeling the robot workspace, taking the motion path planning of the robot's electric gripper from the initial position to the tooling position and then to the scanner position as the goal, and using the path planning algorithm to plan the path of the robot gripper;

[0055] Simplifying the robot's motion process into circular motion makes it easier to model and analyze its workspace. In practical applications, the motion trajectory of many robots can be roughly regarded as a combination of rotation around a fixed axis and telescopic motion in a vertical plane.

[0056] The way to model the robot workspace in S3 includes:

[0057] Comparing the entire movement process of the robot's electric gripper from the initial position to the tooling position and then to the scanner position into a circular motion greatly reduces the difficulty of modeling and the amount of calculation, making the description of the workspace more concise and clear. The circular motion is the rotation of the robot's arm around a fixed axis, and has a telescopic range in the plane perpendicular to the rotation axis. By measuring the rotation radius and height range, the robot's working range in three-dimensional space can be clearly determined, which is helpful for path planning and collision detection analysis. The rotation radius is recorded as , the height range is recorded as , then the expression for modeling the robot workspace is ,in, The three coordinate axes in the three-dimensional space can more quickly calculate the robot's motion path from the initial position to the tooling position and then to the scanner position, thereby improving the efficiency of path planning.

[0058] To ensure that the robot electric gripper can move accurately and efficiently from the initial position to the tooling position and then to the scanner position while meeting the constraints of the workspace.

[0059] In S3, the motion path planning of the robot's electric gripper from the initial position to the tooling position and then to the scanner position is taken as the target, and the path planning algorithm is used to plan the path of the robot gripper. The specific steps are as follows:

[0060] S3.1, record the initial position of the robot electric gripper as , the tooling position is recorded as and the scanner position is recorded as , calculate the straight line equation of the robot's electric gripper from the initial position to the tooling position, which can accurately plan the robot's motion path and reduce errors;

[0061] S3.2. Along this straight line, discretize the path points with a step size of 0.1 to obtain a series of intermediate points. For each intermediate point, check whether it meets the cylindrical space working constraints. If not, move the exceeded points along the boundary of the cylinder to the legal position to ensure that the robot will not collide with obstacles in the workspace during movement. The range of legal positions is ;

[0062] S3.3. Repeat the above steps to calculate the straight line parameter equation of the robot gripper from the tooling position to the scanner position, and combine the straight line equation of the robot electric gripper from the initial position to the tooling position and the straight line equation from the tooling position to the scanner position to obtain the planned path. Accurate path planning can enable the robot to complete tasks faster, reduce movement time, improve production efficiency, avoid collision between the robot and obstacles, and ensure the safety and reliability of the working process.

[0063] S4. Control the movement of the robot's electric gripper according to the planned path, monitor its acceleration and angular velocity in real time during movement and compare them with the planned path. If there is a deviation from the planned path, adjust the robot's electric gripper through the gain function.

[0064] In order to monitor the motion state of the robot's electric gripper in real time and adjust it through the gain function to ensure that the robot can accurately move along the planned path and reach the final scanner position.

[0065] The method of adjusting the robot's electric gripper through the gain function in S4 specifically includes:

[0066] According to the path planning algorithm, the planned path of the robot's electric gripper from the initial position to the tooling position and then to the scanner position is obtained. The planned path consists of a series of discrete points, and the spatial coordinates corresponding to each point are , compare the calculated displacement and angle with the position and posture at the corresponding time in the planned path, and set the position coordinates at the corresponding time on the planned path to be , the attitude angle is , the position deviations are , as well as , the angle deviation is By accurately calculating the position deviation and angle deviation and substituting them into the measurement error gain function, the speed and angular velocity of the robot can be adjusted more accurately to improve the accuracy of the movement. Substituting in, the adjusted speed and angular velocity are:

[0067] ;

[0068] ;

[0069] The robot control system controls the movement of the electric gripper according to the adjusted speed and angular velocity, so that it returns to the planned path and reaches the final scanner position, reducing the measurement error caused by the robot deviating from the planned path and improving production quality and product accuracy.

[0070] The above-mentioned method for online optical measurement of automated loading and unloading of robots includes installing cameras and sensors on the robot's electric grippers, processing and identifying and classifying tooling images, collecting acceleration and angular velocity during the movement of the tooling, integrating displacement and angle changes, and establishing a gain function with measurement errors, modeling the robot's workspace, taking the motion path planning of the electric grippers as a target and performing path planning, controlling the movement of the electric grippers according to the planned path, monitoring and comparing acceleration and angular velocity in real time, and adjusting the gain function if there is any deviation. This method improves production efficiency and measurement accuracy through automated and intelligent means, reduces manual intervention, and can more accurately complete loading and unloading and online optical measurement tasks.

[0071] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for online optical measurement of robot automated loading and unloading, characterized in that: The following steps are involved: S1. Install a micro camera and a sensor on the electric gripper of the robot, wherein the micro camera is used to capture a tooling image, and grayscale and median filter the tooling image to obtain a pre-processed tooling image, and input the pre-processed tooling image into a tooling recognition model, and the tooling recognition model recognizes and classifies the pre-processed tooling image; S2. After the identification and classification are completed, the robot electric gripper grasps the tooling and grabs it from the material table to the turntable. The sensor on the robot electric gripper collects the acceleration and angular velocity of the tooling during the movement from the material table to the turntable, and obtains the displacement and angle change of the robot electric gripper according to the integration of the acceleration and angular velocity, and establishes a gain function with the measurement error. The displacement and angle change of the robot electric gripper are obtained according to the integration of the acceleration and angular velocity, and the method of establishing the gain function with the measurement error specifically includes: When the robot's electric gripper grabs the tooling and moves from the material table to the turntable, the sensor on the gripper measures the acceleration in real time. and angular velocity , assuming that the initial speed of the robot electric gripper is , initial position The position of the material table. In the process of moving from the material table to the turntable, the acceleration Integrate to get the speed, , and then the speed The displacement of the robot electric gripper in space is obtained by integration. ,in, is represented as the inner integral variable, Represented as different moments The instantaneous acceleration of the robot's electric gripper is , angular velocity Integrate to get the angle change of the robot electric gripper during movement , ; During the measurement process, there is a measurement error. According to the historical data, the influence coefficient of the displacement change on the final position of the measuring tool is: , the influence coefficient of the angle change on the posture measurement of the tooling is , the environmental impact coefficient is , then the gain function of the measurement error is expressed as: ； in, Expressed as a gain function of measurement error; S3, modeling the robot workspace, taking the motion path planning of the robot's electric gripper from the initial position to the tooling position and then to the scanner position as the goal, and using the path planning algorithm to plan the path of the robot gripper; S4. Control the movement of the robot's electric gripper according to the planned path, monitor the acceleration and angular velocity of the robot during movement in real time and compare them with the planned path. If there is a deviation from the planned path, adjust the robot's electric gripper through a gain function. The method of adjusting the robot's electric gripper through the gain function specifically includes: According to the path planning algorithm, the planned path of the robot's electric gripper from the initial position to the tooling position and then to the scanner position is obtained. The planned path consists of a series of discrete points, and the spatial coordinates corresponding to each point are , compare the calculated displacement and angle with the position and posture at the corresponding time in the planned path, and set the position coordinates at the corresponding time on the planned path to be , the attitude angle is , then the position deviations are , as well as , the angle deviation is , the measurement error gain function Substituting in, the adjusted speed and angular velocity are: ； ； The robot control system controls the movement of the electric gripper according to the adjusted speed and angular velocity, so that it returns to the planned path and reaches the final scanner position.

2. The method for online optical measurement of robot automated loading and unloading according to claim 1, characterized in that: The micro camera in S1 is used to capture the tooling image, and grayscale and median filter the tooling image, specifically including: The neural network is used to learn the distribution law of color features of different tooling images, and the weight of each pixel in the tooling image converted to grayscale value is automatically adjusted. After the grayscale conversion of the tooling image is completed, the tooling image is divided into several sub-regions of fixed size, and the variance of the pixel values ​​in each sub-region is calculated. The average value of the variance is used as the threshold. The area above the threshold is defined as a complex area, and the area below the threshold is defined as a smooth area. For the sub-regions of the complex area, the window size is set to , for the sub-region of the smooth region, the window size is set to , The window size is used to preserve the details of complex areas. The window size is used to remove noise.

3. The method for online optical measurement of robot automated loading and unloading according to claim 1, characterized in that: In S1, the pre-processed tooling image is input into the tooling recognition model. The tooling recognition model recognizes and classifies the pre-processed tooling image in a manner that specifically includes: Standard tool images of various types of tooling are collected as templates, each standard tooling image is grayed and median filtered, and the edge contour features of the standard tooling image are extracted through the edge detection algorithm to establish a tooling recognition model. When the robot electric gripper grasps the tooling from the material table, the micro camera on the robot electric gripper collects the tooling image to be identified and extracts the contour features of the tooling image to be identified. The contour features of the tooling image to be identified are compared with the edge contour features of the standard tooling image in the tooling recognition model for similarity. According to the similarity value, the tooling image to be identified is identified and classified. If other tools that do not conform to the batch appear, the workbench stops working and an alarm sounds.

4. The method for online optical measurement of robot automated loading and unloading according to claim 1, characterized in that: The sensor on the robot electric gripper in S2 collects the acceleration and angular velocity of the tooling during the process of grabbing the tooling from the material table to the turntable, specifically including: The sensor is installed at the joint of the robot's electric gripper and connected to the robot's control system. When the robot gripper starts to grab and move the tooling, the sensor's data acquisition function is triggered, and the collected acceleration and angular velocity are temporarily stored in the control system cache. The noise is removed by using a second-order low-pass filtering algorithm, and then the numerical code output by the acceleration sensor is converted into acceleration values ​​in meters per second squared and angular velocity values ​​in radians per second according to the sensor's sensitivity coefficient, so as to obtain the acceleration and angular velocity of the robot's electric gripper in the process of grabbing and moving from the material table to the turntable.

5. The method for online optical measurement of robot automated loading and unloading according to claim 1, characterized in that: The method of modeling the robot workspace in S3 specifically includes: The entire movement process of the robot's electric gripper from the initial position to the tooling position and then to the scanner position is compared to a circular motion. The circular motion is that the robot's arm rotates around a fixed axis and has a telescopic range in a plane perpendicular to the rotation axis. The rotation radius and height range are measured, and the rotation radius is recorded as , the height range is recorded as , then the expression for modeling the robot workspace is ,in, are the three coordinate axes of the three-dimensional space.

6. The method for online optical measurement of robot automated loading and unloading according to claim 1, characterized in that: In S3, the motion path planning of the robot's electric gripper from the initial position to the tooling position and then to the scanner position is taken as the target, and the path planning algorithm is used to plan the path of the robot gripper. The specific steps are as follows: S3.1, record the initial position of the robot electric gripper as , the tooling position is recorded as and the scanner position is recorded as , calculate the straight line equation of the robot electric gripper from the initial position to the tooling position; S3.

2. Discretize the path points along this line with a step size of 0.1 to obtain a series of intermediate points. For each intermediate point, check whether it meets the cylindrical space working constraints. If not, move the excess points along the boundary of the cylinder to a legal position. The range of the legal position is ; S3.

3. Repeat the above steps to calculate the straight line parameter equation of the robot gripper from the tooling position to the scanner position, and combine the straight line equation of the robot electric gripper from the initial position to the tooling position and the straight line equation from the tooling position to the scanner position to obtain the planned path.

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