Robot gluing track interpolation queue synchronization and optimization method based on online perception

Abstract

The invention relates to the technical field of industrial robot high-precision track control, solves the technical problem of gluing track deviation caused by the fact that current moment data collected by a sensor is not matched with a robot pose, and particularly relates to a robot gluing track interpolation queue synchronization and optimization method based on online sensing. Comprising the following steps: acquiring image data and point cloud data of a gluing gap at the current moment, and performing timestamp calibration and delay compensation on the image data to obtain an actual acquisition timestamp; the upper computer receives pose data of the robot in real time, and the pose data are stored in the annular buffer area according to the timestamp sequence to form a track interpolation queue; and positioning adjacent robot pose points in the trajectory interpolation queue according to the actual acquisition timestamps, and calculating the average movement speed of the robot according to the time interval and the position difference of the robot pose points. According to the method, the accuracy of the track is obviously improved in the gluing track planning process, the goodness of fit between the track and a theoretical track is obviously enhanced, and therefore the gluing quality is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of high-precision trajectory control of industrial robots, and particularly to a method for synchronizing and optimizing a robot glue application trajectory interpolation queue based on online perception. Background Art

[0002] In the glue application operation of industrial robots, line laser sensors are widely used to scan the surface of workpieces in real time to generate three-dimensional point cloud data of the glue application path. However, the following key problems exist in the actual application of the existing technology.

[0003] The image acquisition frequency of the line laser sensor does not match the robot pose transmission frequency, resulting in the sensor data at a certain moment not being directly corresponding to the real-time pose of the robot. For example, when the robot moves at a high speed (such as 50 mm / s), the displacement of the robot within the adjacent image interval is significant, and the traditional cyclic acquisition method will cause cumulative deviation of the glue application path due to data asynchronization.

[0004] There is a fixed delay when the sensor data is transmitted through Ethernet, and the existing system usually directly uses the receiver timestamp without compensating for the actual acquisition time. This error will be further amplified in a dynamic scenario, resulting in coordinate mapping deviation in three-dimensional reconstruction.

[0005] The line laser sensor has the characteristics of non-contact measurement, high precision and resolution, and adaptability to complex surfaces and environments. However, factors such as temperature, humidity, and air flow may affect the scattering or refraction of the laser beam, resulting in measurement errors. In addition, high-precision sensors need to operate in a stable environment. Due to carrying the end effector for glue application, if the serial robot has poor rigidity, it is prone to vibration interference during movement, resulting in problems such as deviation in the acquisition of the line laser sensor and difficulty in extracting feature points. Summary of the Invention

[0006] Aiming at the deficiencies of the existing technology, the present invention provides a method for synchronizing and optimizing a robot glue application trajectory interpolation queue based on online perception, which solves the technical problem of glue application trajectory deviation caused by the mismatch between the data collected at the current moment by the sensor and the robot pose.

[0007] To solve the above technical problems, the present invention provides the following technical solution: A method for synchronizing and optimizing a robot glue application trajectory interpolation queue based on online perception, the method comprising the following steps: S1. Collect image data and point cloud data of the glue application gap at the current moment, and perform timestamp calibration and delay compensation on the image data to obtain the actual acquisition timestamp; S2. The host computer receives the pose data of the robot in real time and stores it in the circular buffer area in the order of timestamps to form a trajectory interpolation queue; S3. Locate adjacent robot pose points in the trajectory interpolation queue according to the actual acquisition timestamp, and calculate the average motion speed of the robot based on the time interval and position difference between the robot pose points; S4. Select to use first-order linear interpolation or second-order acceleration interpolation according to the average motion speed to obtain the robot pose at the corresponding acquisition image data time; S5. Extract the points in the gluing gap from the point cloud data as feature points, and perform coordinate transformation on the feature points to obtain the gluing trajectory feature points in the robot base coordinate system; S6. Sparsify the gluing trajectory feature points and perform fitting to generate the gluing trajectory.

[0008] Further, in step S2, it includes: Attach the reception timestamp to the image data collected by the line laser sensor, and calculate the actual acquisition timestamp according to the transmission delay. The calculation method of the transmission delay is: ; And define the actual acquisition timestamp as: .

[0009] Further, in step S2, the pose data includes a timestamp , three-dimensional position and quaternion attitude ; And the trajectory interpolation queue satisfies: the queue capacity is dynamically adjusted according to the maximum motion speed of the robot and the data frequency; it adopts a lock-free circular buffer area structure and supports multi-threaded concurrent reading and writing.

[0010] Further, in step S3, the calculation formula of the average motion speed is: ; ; In the formula, is the average speed in the X, Y, and Z directions between time and ; and are the coordinates of adjacent robot pose points; represents the difference between the actual acquisition timestamp and the previous robot pose timestamp .

[0011] Further, in step S4, it specifically includes: Select the interpolation method according to the set speed threshold. When the average speed is less than or equal to the speed threshold, it is low speed, and first-order linear interpolation is used to obtain the robot pose at the corresponding acquisition image data time. The first-order linear interpolation formula is: ; In the formula, is the estimated values of the position in the X, Y, and Z directions corresponding to the moment; is the coordinate of the robot pose point; is the timestamp of the previous robot pose; is the actual acquisition timestamp; When the linear velocity is greater than the velocity threshold, it is high speed. Then, the robot pose at the moment corresponding to the acquired image data is obtained by using second-order acceleration interpolation. The second-order acceleration interpolation formula is: ; ; ; ; In the formula, , , are respectively the ratios of the position differences and time differences between two adjacent robot pose points; , , are respectively the accelerations in the X, Y, and Z directions, estimated by the rate of change of velocity; represents the actual acquisition timestamp and the previous robot pose timestamp difference.

[0012] Furthermore, the velocity threshold is 10 mm / s.

[0013] Furthermore, in step S5, the expression of the coordinate transformation matrix is: ; where, is the three-dimensional coordinate of the glue application trajectory feature point in the robot base coordinate system; is the transformation matrix composed of after interpolation and the corresponding attitude; is the hand-eye relationship matrix between the line laser sensor and the robot; is the three-dimensional coordinate of the feature point in the line laser sensor coordinate system.

[0014] Furthermore, in step S6, the specific process includes the following steps: Obtain the three-dimensional trajectory data of the original glue application trajectory, including the position coordinates and the attitude parameters ; Calculate the curvature characteristics of the original glue application trajectory through cubic spline interpolation, identify the key turning points in the original glue application trajectory based on the curvature threshold, and segment the original glue application trajectory into multiple sub-segments; Perform multi-level filtering processing on each sub-segment respectively, and use a Butterworth low-pass filter to eliminate high-frequency noise; Then apply a median filter to suppress pulse interference, perform linear fitting on each filtered sub-segment to generate a straight-line trajectory segment, and at the same time retain the relevance of the attitude parameters; Finally, calculate the transition connection points of adjacent straight-line trajectory segments through a spatial geometric projection algorithm, and adjust the attitude direction in combination with the Euler angle rotation matrix to generate a continuous and smooth glue application trajectory.

[0015] With the above technical solutions, the present invention provides a method for interpolating queue synchronization and optimization of a robot glue application trajectory based on online perception, and at least has the following beneficial effects: 1. The present invention effectively solves the trajectory deviation caused by the mismatch between the data collected by the sensor at the current moment and the robot pose, and improves the accuracy of the glue application trajectory in cooperation with the trajectory optimization algorithm.

[0016] 2. The present invention calculates the transmission delay of the sensor and calibrates the time stamp, and combines the trajectory interpolation calculation with the sparse filtering algorithm, which significantly improves the synchronization between the line laser sensor and the robot pose reading, thereby improving the accuracy of the glue application trajectory.

[0017] 3. The present invention significantly improves the accuracy of the trajectory during the glue application trajectory planning process, enhances its coincidence with the theoretical trajectory, and thus effectively improves the glue application quality. Brief Description of the Drawings

[0018] The drawings described herein are used to provide a further understanding of the present application, form a part of the present application, and the illustrative embodiments and descriptions thereof of the present application are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings: Figure 1 It is a flowchart of the method for interpolating queue synchronization and optimization of a robot glue application trajectory in the present invention; Figure 2 It is a schematic diagram of a laser sensor scanning a glue application gap in the present invention; Figure 3 It is a schematic diagram of trajectory interpolation in the present invention; Figure 4 It is a schematic diagram of the characteristic points of the glue application trajectory in the present invention; Figure 5 It is a schematic diagram of the characteristic points of the glue application trajectory after sparse filtering optimization in the present invention; Figure 6 It is a schematic diagram of the original glue application trajectory in the present invention; Figure 7 This is a schematic diagram of the glue application trajectory optimized by sparse filtering in the present invention. Specific Embodiment

[0019] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. Thereby, a full understanding of how the present application uses technical means to solve technical problems and achieve technical effects can be obtained and implemented accordingly.

[0020] This embodiment proposes a method for robot glue application trajectory interpolation queue synchronization and optimization based on online perception. By scanning the glue application gap with a line laser sensor, image data at the current moment is collected, and timestamp calibration and delay compensation are performed; a trajectory interpolation queue is constructed, the interpolation interval is located by the binary search method, the speeds of adjacent positions are calculated, and finally the pose at the target moment is estimated by linear interpolation. The dense glue application trajectory points calculated by scanning are optimized and fitted to generate the final glue application trajectory. This method effectively solves the trajectory deviation caused by the mismatch between the data collected by the sensor at the current moment and the robot pose, and improves the accuracy of the glue application trajectory in cooperation with the trajectory optimization algorithm. As Figure 1 shown, this method includes the following steps: S1. Collect image data and point cloud data of the glue application gap at the current moment, and perform timestamp calibration and delay compensation on the image data to obtain the actual acquisition timestamp. In this embodiment, as Figure 2 shown, image data and point cloud data are collected by a line laser sensor, and at the same time, a reception timestamp is attached to the image data collected by the line laser sensor, and the actual acquisition timestamp is calculated according to the transmission delay. It is known that the frequency of the used line laser sensor is 1280 points / line, and the frame rate is 30 FPS; the robot pose output frequency is 50 Hz, and the host computer, the robot, and the line laser sensor are connected by an Ethernet cable. Thus, the transmission delay of a single frame of the line laser sensor can be calculated as: ; Then the actual acquisition timestamp is: ; Thus, timestamp calibration and delay compensation are realized to obtain the actual acquisition timestamp.

[0021] S2. The host computer receives the pose data of the robot in real time and stores it in the circular buffer area in timestamp order to form a trajectory interpolation queue, as Figure 3 shown. The trajectory interpolation queue is sorted based on time and maintains the position data read from the robot controller within a certain period of time. Among them, the pose data includes the timestamp , the three-dimensional position and the quaternion attitude , and then stored in a circular buffer area with a capacity of 500, and the data is sorted in ascending order according to the time stamp for efficient binary search. And the trajectory interpolation queue satisfies: the queue capacity is dynamically adjusted according to the maximum movement speed of the robot and the data frequency; a lock-free circular buffer area structure is adopted to support multi-threaded concurrent reading and writing.

[0022] S3. Locate adjacent robot pose points in the trajectory interpolation queue according to the actual acquisition time stamp, and calculate the average movement speed of the robot based on the time interval and position difference between the robot pose points. In this embodiment, according to the calibrated actual acquisition time stamp , locate adjacent robot pose points in the trajectory interpolation queue through binary search, where the adjacent indexes that satisfy are located in the trajectory interpolation queue. Then calculate the average velocity in the X, Y, and Z directions based on the time interval and position difference between the robot pose points, and finally obtain the average movement speed of the robot. The calculation formula for the average movement speed is: ; ; In the formula, is the time to the average velocity in the X, Y, and Z directions; and are the coordinates of adjacent robot pose points; represents the actual acquisition time stamp and the time stamp of the previous robot pose the difference.

[0023] S4. Select to use first-order linear interpolation or second-order acceleration interpolation according to the average movement speed to obtain the robot pose at the corresponding acquisition image data time for the synchronous mapping of the point cloud data and the robot coordinate system. In this embodiment, the speed threshold is 10mm / s, which specifically includes: Select the interpolation method according to the set speed threshold. When the average speed is less than or equal to the speed threshold, it is low speed, and first-order linear interpolation is used to obtain the robot pose at the corresponding acquisition image data time. The first-order linear interpolation formula is: ; In the formula, is the estimated values of the position in the X, Y, and Z directions corresponding to the time; is the coordinate of the robot pose point; is the time stamp of the previous robot pose; is the actual acquisition time stamp; When the average speed is greater than the speed threshold, it is considered high speed. In this case, the pose of the robot at the corresponding image data acquisition moment is obtained using second-order acceleration interpolation. The second-order acceleration interpolation formula is as follows: ; ; ; ; In the formula, , , are respectively the ratios of the position differences and time differences between two adjacent robot pose points; , , are respectively the accelerations in the X, Y, and Z directions, estimated through the rate of change of speed; represents the actual acquisition timestamp and the difference from the previous robot pose timestamp .

[0024] S5. Collect the point cloud data collected by the line laser sensor each time, and establish a one-to-one correspondence with the robot pose based on synchronous mapping. Extract the points in the gluing gap from the point cloud data as feature points, and convert the feature points in the sensor coordinate system to the robot base coordinate system through the hand-eye calibration matrix to obtain the gluing trajectory feature points. The expression is as follows: ; Among them, is the three-dimensional coordinate of the gluing trajectory feature point in the robot base coordinate system; is the transformation matrix composed of the interpolated and the corresponding attitude; is the hand-eye relationship matrix between the line laser sensor and the robot; is the three-dimensional coordinate of the feature point in the line laser sensor coordinate system.

[0025] S6. Perform sparsification processing on the gluing trajectory feature points and fit them to generate the gluing trajectory. In this embodiment, the sparse filtering algorithm is used to process all the gluing trajectory feature points, sparsify the gluing trajectory feature points, and optimize them into the final gluing trajectory. The specific process includes the following steps: Obtain the three-dimensional trajectory data of the original gluing trajectory, including the position coordinates and the attitude parameters , as shown in Figure 6 , which is a schematic diagram of the original gluing trajectory, Figure 4 and the gluing trajectory feature points in the original gluing trajectory are as shown in Figure 4 .

[0026] The curvature characteristics of the original glue - applying trajectory are calculated by cubic spline interpolation. Key turning points in the original glue - applying trajectory are identified based on the curvature threshold, and the original glue - applying trajectory is segmented into multiple sub - segments. Multistage filtering processing is performed on each sub - segment. The Butterworth low - pass filter is used to eliminate high - frequency noise. As Figure 5 shown, the characteristic points of the glue - applying trajectory after sparse filtering optimization are presented. Then, the median filter is applied to suppress pulse interference. Linear fitting is performed on each filtered sub - segment to generate straight - line trajectory segments while retaining the relevance of attitude parameters. Finally, the transition connection points between adjacent straight - line trajectory segments are calculated through the spatial geometric projection algorithm, and the attitude direction is adjusted by combining the Euler angle rotation matrix to generate a continuous and smooth glue - applying trajectory as Figure 7 shown.

[0027] By interpolating and synchronizing the data acquisition of the sensor and the robot, the present invention improves the trajectory synchronization and makes the identification of the starting and ending points of glue - applying more accurate. At the same time, the sparse filtering algorithm is adopted to filter, fit, and disperse the extracted trajectory characteristic points to generate the point information recognizable by the host computer as the final glue - applying trajectory. During the glue - applying trajectory planning process, the accuracy of the trajectory is significantly improved, and its coincidence with the theoretical trajectory is remarkably enhanced, thus effectively improving the glue - applying quality.

[0028] Those of ordinary skill in the art can understand that all or part of the steps in the methods of the above - mentioned embodiments can be completed by instructing relevant hardware through a program. Therefore, this application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can adopt the form of a computer program product implemented on one or more computer - usable storage media (including but not limited to disk memory, CD - ROM, optical memory, etc.) containing computer - usable program code.

[0029] Each embodiment in this specification is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For the above - mentioned embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0030] The above - mentioned implementation manners have introduced the present invention in detail. Specific examples are used in this article to elaborate on the principle and implementation manners of the present invention. The description of the above - mentioned embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for robot glue - applying trajectory interpolation queue synchronization and optimization based on online perception, characterized in that, The method includes the following steps: S1. Collect the image data and point cloud data of the glue application gap at the current moment, and perform timestamp calibration and delay compensation on the image data to obtain the actual acquisition timestamp; S2. The host computer receives the pose data of the robot in real time and stores it in the circular buffer area in the order of timestamps to form a trajectory interpolation queue; S3. Locate the adjacent robot pose points in the trajectory interpolation queue according to the actual acquisition timestamp, and calculate the average motion speed of the robot based on the time interval and position difference of the robot pose points; S4. Select to use first-order linear interpolation or second-order acceleration interpolation according to the average motion speed to obtain the robot pose at the corresponding image data acquisition moment; S5. Extract the points in the glue application gap from the point cloud data as feature points, and perform coordinate transformation on the feature points to obtain the glue application trajectory feature points in the robot base coordinate system; S6. Perform sparse processing on the glue application trajectory feature points and perform fitting to generate the glue application trajectory.

2. The interpolation queue synchronization and optimization method for the robot gluing trajectory according to claim 1, wherein In step S2, it includes: Attach the reception timestamp to the image data collected by the line laser sensor, and calculate the actual acquisition timestamp according to the transmission delay. The calculation method of the transmission delay is: ； And define the actual acquisition timestamp as: .

3. The interpolation queue synchronization and optimization method for the robot gluing trajectory according to claim 1, wherein, In step S2, the pose data includes a timestamp , a three-dimensional position and a quaternion attitude ; And the trajectory interpolation queue satisfies: the queue capacity is dynamically adjusted according to the maximum motion speed and data frequency of the robot; adopt a lock-free circular buffer area structure, which supports multi-threaded concurrent reading and writing.

4. The interpolation queue synchronization and optimization method for the robot gluing trajectory according to claim 1, characterized in that In step S3, the calculation formula of the average motion speed is: ； ； In the formula, is the average velocity in the X, Y, and Z directions between and ; and are the coordinates of adjacent robot pose points; represents the actual acquisition timestamp and the difference from the previous robot pose timestamp .

5. The interpolation queue synchronization and optimization method for the robot gluing trajectory according to claim 1, wherein In step S4, it specifically includes: Select the interpolation method according to the set speed threshold. When the average speed is less than or equal to the speed threshold, it is low speed, and first-order linear interpolation is used to obtain the robot pose at the corresponding image data acquisition moment. The first-order linear interpolation formula is: ； In the formula, is the estimated values in the X, Y, and Z directions corresponding to the position at the moment; is the coordinate of the robot pose point; is the timestamp of the previous robot pose; is the actual acquisition timestamp; When the average speed is greater than the speed threshold, it is high speed, and second-order acceleration interpolation is used to obtain the robot pose at the corresponding image data acquisition moment. The second-order acceleration interpolation formula is: ； ； ； ； Wherein, , , are respectively the ratios of the position difference and the time difference between two adjacent robot pose points; , , are respectively the accelerations in the X, Y, and Z directions, estimated by the rate of change of velocity; represents the actual acquisition timestamp and the previous robot pose timestamp the difference between them.

6. The robot glue application trajectory interpolation queue synchronization and optimization method according to claim 5, wherein The speed threshold is 10mm / s.

7. The robot glue application trajectory interpolation queue synchronization and optimization method according to claim 1, characterized in that In step S5, the expression of the coordinate transformation matrix is: ； Among them, is the three-dimensional coordinate of the glue application trajectory feature point in the robot base coordinate system; After interpolation and the transformation matrix composed of the corresponding attitude; is the hand-eye relationship matrix between the line laser sensor and the robot; is the three-dimensional coordinate of the feature point in the line laser sensor coordinate system.

8. The interpolation queue synchronization and optimization method for the robot gluing trajectory according to claim 1, characterized in that In step S6, the specific process includes the following steps: Obtain the three-dimensional trajectory data of the original glue application trajectory, including position coordinates and attitude parameters ; Calculate the curvature characteristics of the original glue application trajectory through cubic spline interpolation, identify the key turning points in the original glue application trajectory based on the curvature threshold, and divide the original glue application trajectory into multiple sub-segments; Perform multi-level filtering processing on each sub-segment respectively, and use a Butterworth low-pass filter to eliminate high-frequency noise; Then apply a median filter to suppress pulse interference, perform linear fitting on each filtered sub-segment to generate a straight trajectory segment, and at the same time retain the relevance of the attitude parameters; Finally, calculate the transition connection points of adjacent straight trajectory segments through a spatial geometric projection algorithm, and adjust the attitude direction in combination with the Euler angle rotation matrix to generate a continuous and smooth glue application trajectory.

9. A robot gluing system, characterized in that, Using the robot glue application trajectory interpolation queue synchronization and optimization method as described in any one of claims 1-8, it includes: a line laser sensor for collecting the image data and point cloud data of the workpiece glue application gap; an industrial robot equipped with a line laser sensor and real-time feedback of pose information; a control computer for executing a trajectory interpolation algorithm and generating a glue application trajectory instruction.

10. A computer-readable storage medium, characterized in that, A computer program is stored, which, when executed by a processor, implements the method for synchronizing and optimizing the interpolation queue of the robot gluing trajectory as described in any one of claims 1-8.

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