A path planning method and device for synchronized machining of a robotic arm and a laser

Abstract

The present invention relates to the field of automatic control technology, and discloses a path planning method and device for synchronized machining of a robotic arm and a laser, including: obtaining a three-dimensional model of a workpiece, material and process parameters, and extracting an irregular surface to generate a discrete point set. Calculating the pose of the end of the robotic arm through inverse kinematics to obtain preliminary joint angles and a trajectory sequence. Combining parameter matching to optimize the laser parameters, generating a variation curve and synchronously mapping it to the joint angle sequence, and obtaining a synchronized joint angle sequence through spline interpolation fine-tuning. Fitting a smooth trajectory with a B-spline curve, and generating a motion trajectory control instruction in combination with the synchronized joint angles. The robotic arm and the laser move according to the instruction. When the environment changes, the trajectory compensation coefficient is dynamically adjusted, and online compensation is performed using an adaptive Kalman filtering algorithm to obtain a corrected control instruction. The present invention improves the adaptability of path planning by extracting the machining details of the irregular surface, aligning the working sequences of the robotic arm and the laser, and real-time sensing the machining environment, so as to perform efficient and precise machining.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and particularly to a path planning method and device for synchronous processing of a robotic arm and a laser. Background Art

[0002] The technology of synchronous processing of a robotic arm and a laser is widely used in the field of industrial manufacturing. For example, in automobile manufacturing, the robotic arm cooperates with the laser for high-precision cutting and welding of components, ensuring the precise assembly of automobile parts; in the aerospace field, this technology is used to manufacture complex structural parts, such as the skin and frame of an aircraft wing, ensuring the strength and precision of the structural parts; in the electronics industry, the robotic arm and the laser work together to achieve cutting and welding of small parts of precision electronic components, meeting the high-precision requirements.

[0003] Currently, the synchronous processing of a robotic arm and a laser mainly relies on an industrial control computer to coordinate the movements of the robotic arm and the laser. For example, the industrial control computer precisely controls the movement path of the robotic arm and the turning on and off of the laser according to the preset processing trajectory data, so as to achieve synchronous operation of the two and complete complex processing tasks.

[0004] However, the existing technology has obvious deficiencies in path planning. The movement trajectories of the robotic arm and the laser mostly rely on preset programs and lack real-time adaptability to the shapes of complex workpieces and changes in the dynamic processing environment. For example, when processing an irregular curved surface, the fixed path planning method may cause the robotic arm to be unable to accurately track the predetermined trajectory, thereby affecting the processing accuracy and making it difficult to guarantee the processing quality in high-precision processing scenarios. Summary of the Invention

[0005] The present invention provides a path planning method and device for synchronous processing of a robotic arm and a laser, which improve the adaptability of path planning by extracting the processing details of irregular curved surfaces, aligning the working sequences of the robotic arm and the laser, and real-time sensing of the processing environment, so as to perform efficient and precise processing.

[0006] In a first aspect, the present invention provides a path planning method for synchronous processing of a robotic arm and a laser, mainly including:

[0007] Obtain the three-dimensional model data of the workpiece, the workpiece material parameters, the processing process parameters, obtain the displacement ranges and dynamic constraint conditions of each joint of the robotic arm, and obtain the processing environment parameters;

[0008] Extract the irregular curved surface from the three-dimensional model data, and use a point cloud sampling algorithm to generate a high-density discrete point set;

[0009] According to the high-density discrete point set, an inverse kinematics algorithm is used to calculate the target pose of the end effector of the robotic arm at each discrete point, and combined with the displacement range and the dynamic constraint conditions, a preliminary joint angle sequence and a preliminary trajectory sequence are obtained;

[0010] According to the workpiece material parameters and the machining process parameters, the optimal laser working parameters are matched from a pre-established laser processing parameter database, and a laser parameter variation curve matching the preliminary trajectory sequence is generated;

[0011] The laser parameter variation curve and the preliminary joint angle sequence are synchronously mapped according to the time axis, and the preliminary joint angle sequence is finely adjusted by using a spline interpolation algorithm to obtain a synchronous joint angle sequence;

[0012] A B-spline curve fitting algorithm is used to smooth the preliminary trajectory sequence, and combined with the synchronous joint angle sequence, a motion trajectory control instruction is obtained to control the operation of the robotic arm;

[0013] The robotic arm moves according to the motion trajectory control instruction. When the machining environment parameters change, the preset trajectory compensation coefficient is dynamically adjusted, and the trajectory instruction is online compensated by using an adaptive Kalman filtering algorithm to obtain a corrected motion control instruction.

[0014] In an alternative embodiment, the extraction of the irregular surface in the three-dimensional model data and the generation of a high-density discrete point set by using a point cloud sampling algorithm include:

[0015] For the irregular surface, a uniform point cloud sampling algorithm is used to generate an initial discrete point set;

[0016] It is judged whether the point set density of the initial discrete point set is lower than a preset density threshold. If so, the initial discrete point set is processed by using a local encryption sampling algorithm. If not, no processing is performed to obtain enhanced discrete point set data;

[0017] According to the enhanced discrete point set data, a random forest algorithm is used to classify the irregular surface to obtain different feature regions, and the geometric property distribution of the different feature regions is obtained;

[0018] According to the geometric property distribution, the direction consistency data of the surface of the irregular surface is determined through normal estimation;

[0019] According to the direction consistency data, for the enhanced discrete point set, a k-nearest neighbor algorithm is used to smooth the point set spacing to obtain a high-density discrete point set.

[0020] In an alternative embodiment, according to the high-density discrete point set, an inverse kinematics algorithm is used to calculate the target pose of the end effector of the robotic arm at each discrete point. Combining the displacement range and the dynamic constraint conditions, a preliminary joint angle sequence and a preliminary trajectory sequence are obtained, including:

[0021] Obtain discrete point set data, use an inverse kinematics algorithm to determine the target pose of the end effector of the robotic arm at each discrete point, and obtain an original joint angle sequence for the joint displacement range limit and dynamic constraint conditions;

[0022] Group the original joint angle sequence using the k-means clustering algorithm, and obtain a classified angle sequence based on the matching degree of each group of the original joint angle sequence and the dynamic constraint conditions;

[0023] Adjust the angle values in the classified angle sequence that exceed the joint displacement range limit to the maximum value of the joint displacement range, and perform smoothing processing using an interpolation algorithm to obtain an optimized angle sequence;

[0024] Determine the direction consistency data of the end effector of the robotic arm at each discrete point of the optimized angle sequence through normal estimation, and the robotic arm obtains motion trajectory parameters in combination with the direction consistency data;

[0025] For the dynamic constraint conditions, use a support vector machine algorithm to perform anomaly detection on the motion trajectory parameters. If an abnormal trajectory point is detected, adjust the optimized angle sequence through local resampling to obtain a preliminary joint angle sequence and a preliminary trajectory sequence.

[0026] In an alternative embodiment, according to the workpiece material parameters and the processing process parameters, match the optimal laser working parameters from a pre-established laser processing parameter database, and generate a laser parameter change curve that matches the preliminary trajectory sequence, including:

[0027] Use the k-nearest neighbor algorithm to retrieve the laser power, scanning speed, and focused spot parameters that match the workpiece material parameters and the processing process parameters from the pre-established laser processing parameter database to obtain a preliminary laser parameter set;

[0028] According to the preliminary laser parameter set, use an interpolation algorithm to process the preliminary trajectory sequence to generate a laser power and scanning speed change sequence corresponding to the trajectory points, and obtain an initial parameter change curve;

[0029] Use a support vector machine algorithm to detect abnormal parameter points in the initial parameter change curve that do not conform to the processing process parameters, and adjust the parameter values through local resampling to obtain a corrected parameter change curve;

[0030] Mapping the machining process parameter sequence to a discrete point set of the motion trajectory through grid division for the corrected parameter change curve, to obtain a spatial optimization data set;

[0031] Using a linear regression algorithm to smooth the spatial optimization data set, to obtain the final laser parameter change curve.

[0032] In an alternative embodiment, the synchronous mapping of the laser parameter change curve and the preliminary joint angle sequence along the time axis, and using a spline interpolation algorithm to finely adjust the preliminary joint angle sequence to obtain a synchronous joint angle sequence, includes:

[0033] Smoothing the joint angle sequence using a cubic spline interpolation algorithm to obtain a smoothed joint angle sequence;

[0034] Mapping the smoothed joint angle sequence to a motion speed sequence through time axis alignment, and mapping the laser parameter change curve to a laser power change sequence;

[0035] Using a Kalman filtering algorithm to filter the noise of the motion speed sequence to obtain a smoothed speed sequence;

[0036] Using a dynamic time warping algorithm to analyze the time deviation between the smoothed speed sequence and the laser power change sequence, and calibrating to obtain a calibrated synchronous sequence;

[0037] Judging the direction consistency between the speed sequence and the power change in the calibrated synchronous sequence through normal estimation to obtain a synchronous joint angle sequence.

[0038] In an alternative embodiment, the robotic arm moves according to the motion trajectory control instruction. When the machining environment parameters change, dynamically adjust the preset trajectory compensation coefficient, and perform online compensation on the trajectory instruction through an adaptive Kalman filtering algorithm to obtain a corrected motion control instruction, including:

[0039] Real-time monitoring the change of the machining environment parameters, calculating a preliminary trajectory instruction adjustment amount using a preset initial compensation coefficient to obtain a first trajectory adjustment sequence;

[0040] Dynamically adjusting the compensation coefficient according to the time distribution characteristics of the first trajectory adjustment sequence, combining with the change trend of the machining environment parameters, and using an adaptive Kalman filtering algorithm to smooth and correct the first trajectory adjustment sequence to obtain a second trajectory adjustment sequence;

[0041] According to the trajectory instruction distribution of the second trajectory adjustment sequence, suppressing high-frequency noise through a filtering algorithm to obtain a third trajectory adjustment sequence;

[0042] Regarding the control sequence continuity of the third trajectory adjustment sequence, the deviation of the trajectory command is corrected twice through online compensation to obtain a corrected motion control command.

[0043] In an alternative embodiment, it further includes: using high-precision encoders at the joints of the robotic arm and power sensors on the laser processing head to collect the actual rotation angles of each joint and the laser output parameters in real time, and comparing them with the corrected motion control command. If the deviation exceeds a preset threshold, a closed-loop feedback control is triggered to generate a feedback control command for real-time adjustment of the robotic arm motion trajectory and laser parameters, specifically including:

[0044] Obtain the actual rotation angles of each joint and the laser output parameters collected in real time by the high-precision encoders and power sensors to obtain an original data sequence;

[0045] Compare the original data sequence with the corrected motion control command, and use a deviation calculation method to determine a deviation value sequence;

[0046] Judge whether the deviation value sequence exceeds a preset deviation threshold. If so, start the closed-loop feedback mechanism; if not, do nothing and generate a preliminary feedback control sequence;

[0047] For the preliminary feedback control sequence, use a low-pass filtering algorithm to suppress high-frequency interference to obtain a smoothed control sequence;

[0048] Combined with the change trend of the actual rotation angle, adjust the smoothed control sequence through online compensation to obtain an optimized control sequence;

[0049] Regarding the matching situation between the optimized control sequence and the laser output parameters, use a proportional adjustment method to correct the optimized control sequence to obtain a feedback control command.

[0050] The present invention provides a path planning device for synchronous processing of a robotic arm and a laser, mainly including:

[0051] A data acquisition module for acquiring three-dimensional model data of a workpiece, workpiece material parameters, processing process parameters, acquiring the displacement range and dynamic constraint conditions of each joint of the robotic arm, and acquiring processing environment parameters;

[0052] A point cloud generation module for extracting the irregular surface in the three-dimensional model data and generating a high-density discrete point set using a point cloud sampling algorithm;

[0053] A preliminary trajectory generation module for calculating the target pose of the end effector of the robotic arm at each discrete point according to the high-density discrete point set using an inverse kinematics algorithm, and combining the displacement range and the dynamic constraint conditions to obtain a preliminary joint angle sequence and a preliminary trajectory sequence;

[0054] A laser parameter matching module, configured to match optimal laser working parameters from a pre-established laser processing parameter database according to the workpiece material parameters and the processing technology parameters, and generate a laser parameter change curve that matches the preliminary trajectory sequence;

[0055] A synchronous mapping module, configured to perform synchronous mapping of the laser parameter change curve and the preliminary joint angle sequence along the time axis, and fine-tune the preliminary joint angle sequence by using a spline interpolation algorithm to obtain a synchronous joint angle sequence;

[0056] A trajectory smoothing module, configured to perform smoothing processing on the preliminary trajectory sequence by using a B-spline curve fitting algorithm, and combine with the synchronous joint angle sequence to obtain a motion trajectory control instruction for controlling the operation of the robotic arm;

[0057] A dynamic adjustment module, configured to enable the robotic arm to move according to the motion trajectory control instruction, dynamically adjust a preset trajectory compensation coefficient when the processing environment parameters change, and perform online compensation on the trajectory instruction through an adaptive Kalman filtering algorithm to obtain a corrected motion control instruction.

[0058] In a third aspect, the present invention further provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a path planning method for synchronous processing of a robotic arm and a laser as described in any one of the above.

[0059] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute a path planning method for synchronous processing of a robotic arm and a laser as described in any one of the above.

[0060] The technical solution provided by the embodiments of the present invention may include the following beneficial effects:

[0061] The present invention discloses a precision machining method based on a robotic arm and a laser, including:

[0062] Aiming at the machining problem of irregular curved surface workpieces, the present invention first obtains a high-density discrete point set through point cloud sampling, and calculates the motion trajectory of the robotic arm in combination with the inverse kinematics algorithm. Then, the optimal laser parameters are matched according to the workpiece characteristics, and they are synchronously mapped with the joint angle sequence;

[0063] To improve the machining accuracy, the present invention adopts the B-spline curve algorithm to optimize the trajectory and introduces the adaptive Kalman filter for real-time compensation, so as to achieve the smoothness of the trajectory during the machining process and compensate for the possible errors during the machining process, realizing high-precision machining;

[0064] In addition, the present invention also establishes a closed-loop feedback control mechanism, which dynamically adjusts the motion trajectory and laser parameters through real-time monitoring and comparison. This innovative method significantly improves the machining accuracy and efficiency of complex curved surface workpieces, providing a new technical solution for the precision manufacturing field. Brief Description of the Drawings

[0065] Figure 1 It is a flowchart of a path planning method for synchronized machining of a robotic arm and a laser according to the present invention.

[0066] Figure 2 It is a structural schematic diagram of a path planning device for synchronized machining of a robotic arm and a laser according to the present invention. Detailed Embodiments

[0067] To further understand the content of the present invention, the present invention will be described in detail in combination with the drawings and embodiments. The following further describes the present application in detail with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related invention, rather than limiting the invention. In addition, it should be noted that only the parts related to the invention are shown in the drawings for the convenience of description.

[0068] Referring to Figure 1 , a path planning method for synchronized machining of a robotic arm and a laser in this embodiment may specifically include the following steps:

[0069] Step S101, obtaining the three-dimensional model data of the workpiece, the workpiece material parameters, the machining process parameters, obtaining the displacement ranges and dynamic constraint conditions of each joint of the robotic arm, and obtaining the machining environment parameters;

[0070] Step S102, extracting the irregular curved surface from the three-dimensional model data and generating a high-density discrete point set by using a point cloud sampling algorithm;

[0071] Step S103, according to the high-density discrete point set, using the inverse kinematics algorithm to calculate the target pose of the end effector of the robotic arm at each discrete point, and combining the displacement range and the dynamic constraint conditions to obtain a preliminary joint angle sequence and a preliminary trajectory sequence;

[0072] Step S104, according to the workpiece material parameters and the machining process parameters, matching the optimal laser working parameters from a pre-established laser processing parameter database, and generating a laser parameter change curve matching the preliminary trajectory sequence;

[0073] Step S105, synchronously map the laser parameter change curve and the preliminary joint angle sequence along the time axis, and use the spline interpolation algorithm to finely adjust the preliminary joint angle sequence to obtain a synchronized joint angle sequence;

[0074] Step S106, use the B-spline curve fitting algorithm to smooth the preliminary trajectory sequence, and combine it with the synchronized joint angle sequence to obtain a motion trajectory control instruction to control the operation of the robotic arm;

[0075] Step S107, the robotic arm moves according to the motion trajectory control instruction. When the processing environment parameters change, dynamically adjust the preset trajectory compensation coefficient, and perform online compensation on the trajectory instruction through the adaptive Kalman filtering algorithm to obtain a corrected motion control instruction.

[0076] In step S101, obtain the three-dimensional model data of the workpiece, the workpiece material parameters, the processing process parameters, obtain the displacement ranges of the joints of the robotic arm and the dynamic constraints, and obtain the processing environment parameters.

[0077] It should be noted that the three-dimensional model data is a geometric shape description of the workpiece, including dimensions, shapes, and surface information. It is the basis for path planning and determines the motion trajectories and processing ranges of the robotic arm and the laser. The workpiece material parameters include the physical properties (such as hardness, thermal conductivity, reflectivity) and chemical properties of the material. These parameters directly affect the laser processing effect, such as the penetration depth of the laser, the energy absorption rate, and the processing speed. The processing process parameters involve specific requirements during the processing, such as processing accuracy, surface roughness, and processing depth. These parameters determine the process path and parameter selection of laser processing. The displacement ranges of the joints refer to the maximum and minimum angle ranges that the joints of the robotic arm can move. This is a physical constraint for the motion planning of the robotic arm and determines the reachability and flexibility of the robotic arm. The dynamic constraints include the load capacity, acceleration, and speed limits of the robotic arm. These constraints affect the motion smoothness and processing accuracy of the robotic arm and are factors that must be considered in path planning. The processing environment parameters involve external conditions during the processing, such as temperature, humidity, and light intensity. These parameters will affect the performance of the laser and the processing effect. For example, temperature changes may cause thermal expansion of the workpiece, affecting the processing accuracy. At the same time, the environmental parameters may also affect the motion control of the robotic arm and the performance of the sensors.

[0078] In step S102, extract the irregular surfaces in the three-dimensional model data, and use the point cloud sampling algorithm to generate a high-density discrete point set, including:

[0079] For the irregular surface, use the uniform point cloud sampling algorithm to generate an initial discrete point set;

[0080] Determine whether the point set density of the initial discrete point set is lower than a preset density threshold. If so, process the initial discrete point set using a local encryption sampling algorithm. If not, do nothing and obtain enhanced discrete point set data.

[0081] Based on the enhanced discrete point set data, use the random forest algorithm to classify the irregular surface to obtain different feature regions, and obtain the geometric property distribution of the different feature regions.

[0082] Based on the geometric property distribution, determine the direction consistency data of the surface of the irregular surface through normal estimation.

[0083] Based on the direction consistency data, for the enhanced discrete point set, use the k-nearest neighbor algorithm to smooth the point set spacing to obtain a high-density discrete point set.

[0084] It should be noted that an irregular surface refers to a complex geometric shape that cannot be accurately described by a simple mathematical equation; the initial discrete point set refers to a discrete point set evenly distributed on the surface, which can reflect the geometric features and shape information of the surface; the preset density threshold is a key parameter that determines the accuracy of describing the irregular surface. If the preset density threshold is too high, it is difficult to accurately describe the irregular surface. If the preset density threshold is too low, excessive computing resources are consumed to describe the irregular surface. Exemplarily, the preset density threshold is 1000 points per square meter; the enhanced discrete point set data is obtained by encrypting the sampling on the basis of the initial discrete point set so that the point set density meets the preset density threshold, and contains more points to describe the irregular surface; the geometric property distribution refers to the spatial distribution of curvature, concavity and convexity, and normal vector direction on the irregular surface; the direction consistency data is the normal vector direction information at each point on the surface, which reflects the matching degree of the normal vector directions of adjacent points and is used to determine the local surface direction of each point.

[0085] As a feasible implementation, calculate the normal vector and gradient of each point of the three-dimensional model of the workpiece. By deriving the relationship between the gradient of the surface function and the integral of the normal vector field, establish and solve the Poisson equation to obtain the surface function of the three-dimensional model. The value of this function at the points inside the model is 1, and the value at the points outside is 0. According to the Poisson equation, use the Poisson algorithm to automatically generate a closed triangular mesh surface model and obtain sample triangular points, and use the segmentation threshold to automatically segment the reconstructed triangular mesh surface, and delete the unreasonable triangular patches on the non-entity, so as to obtain an irregular surface with smooth and correct edges.

[0086] As a feasible implementation, assume there is an irregular surface. Uniform point cloud sampling generates an initial discrete point set by setting a fixed spacing, such as taking a point every 5 centimeters.

[0087] It should be noted that the core of the local encryption sampling algorithm is to increase the sampling points in areas with low point set density to improve the data density and detail performance. The local encryption sampling algorithm is adopted to identify low-density areas and locally increase the sampling points.

[0088] For example, in a low-density area, the point set is supplemented by linear interpolation method, so that the spacing is reduced from 5 cm to 2 cm, and an enhanced discrete point set data is generated. The new point set increases from 400 to 600, and the detail performance is richer.

[0089] Furthermore, based on the enhanced discrete point set data, the random forest algorithm can be used to classify the geometric feature distribution of irregular surfaces. Through the integration of multiple decision trees, the random forest can effectively identify the feature areas on the surface.

[0090] For example, in a mountain model, the surface can be divided into flat areas, slope areas and peak areas. During implementation, features such as the local height difference and slope of the point set can be extracted as inputs, and the random forest judges the category of each point through a voting mechanism. Suppose among 50 points in a certain area, 40 are classified as slope areas, indicating that the geometric characteristics of this area tend to be inclined. Such classification results intuitively reflect the distribution law of the surface morphology and help to understand its spatial characteristics.

[0091] In a feasible embodiment, normal estimation is used to confirm the consistency of the surface direction, which can help to understand the local geometric characteristics of the surface. For each point, the points within its local neighborhood are selected. The selection of the neighborhood can be based on a fixed radius or a fixed number of nearest neighbor points. Exemplarily, each point and the surrounding 5 points are used as the neighborhood of this point. The direction consistency data of each point is obtained through normal estimation of the neighborhood.

[0092] It should be noted that for areas with the same direction consistency data, k-nearest neighbor adjusts the point positions to make the spacing more uniform by analyzing the distances between each point in the area and its neighboring points.

[0093] For example, in a slope area, the average distance between a certain point and its 5 neighboring points is 3 cm, but the maximum deviation reaches 1 cm. Through k-nearest neighbor smoothing, the point position can be adjusted to a deviation less than 0.5 cm. As a result, the point set may increase from 600 to 800, forming a high-density point cloud. The advantage of this processing is to eliminate noise points, improve the smoothness and consistency of the surface, and facilitate subsequent modeling or visualization.

[0094] In step S103, according to the high-density discrete point set, the inverse kinematics algorithm is adopted to calculate the target pose of the end effector of the robotic arm at each discrete point, and combined with the displacement range and the dynamic constraint conditions, a preliminary joint angle sequence and a preliminary trajectory sequence are obtained, including:

[0095] Obtain the discrete point set data, and use the inverse kinematics algorithm to determine the target pose of the end effector of the robotic arm at each discrete point. In view of the joint displacement range limit and dynamic constraints, obtain the original joint angle sequence;

[0096] Group the original joint angle sequence using the k-means clustering algorithm, and obtain the classified angle sequence according to the matching degree between each group of the original joint angle sequence and the dynamic constraints;

[0097] Adjust the angle values in the classified angle sequence that exceed the joint displacement range limit to the maximum value of the joint displacement range, and perform smoothing processing using the interpolation algorithm to obtain the optimized angle sequence;

[0098] Determine the direction consistency data of the end effector of the robotic arm at each discrete point of the optimized angle sequence through normal estimation, and the robotic arm combines the direction consistency data to obtain the motion trajectory parameters;

[0099] In view of the dynamic constraints, use the support vector machine algorithm to perform anomaly detection on the motion trajectory parameters. If abnormal trajectory points are detected, adjust the optimized angle sequence through local resampling to obtain the preliminary joint angle sequence and the preliminary trajectory sequence.

[0100] It should be noted that the target pose refers to the position and orientation that the end effector of the robotic arm is expected to reach in three-dimensional space. Specifically, it includes two parts: position and orientation. Position: the coordinates of the end effector in space (usually represented by x, y, z), Orientation: the rotation state of the end effector, represented by a rotation matrix, Euler angles, or quaternions;

[0101] The original joint angle sequence refers to the sequence of angle values of each joint of the robotic arm calculated by the inverse kinematics algorithm. These angle values describe the rotation angles of each joint that the robotic arm needs to pass through from the initial state to the target pose;

[0102] The classified angle sequence refers to the joint angle sequence re-divided according to the dynamic constraints and clustering results after clustering analysis of the original joint angle sequence. Specifically, the classified angle sequence divides the original data into multiple subsequences with similar characteristics, and each subsequence corresponds to a specific motion pattern or dynamic characteristic;

[0103] The direction consistency data reflects the normal vector directions of the end effector at different positions and has high consistency between adjacent points, helping to ensure that the end effector maintains the correct pose during movement;

[0104] The motion trajectory parameters refer to the detailed information describing the motion path of the end effector of the robotic arm in space, including parameters such as position, velocity, acceleration, and jerk (jerk).

[0105] The preliminary joint angle sequence refers to the joint angle sequence obtained after anomaly detection, smoothing, and local resampling adjustment during the motion planning of the robotic arm.

[0106] The preliminary trajectory sequence refers to the path sequence of the end effector of the robotic arm moving in space and the result after anomaly detection and smoothing adjustment.

[0107] As an implementation, inverse kinematics calculates the angles of each joint of the robotic arm from the end position and orientation of the robotic arm.

[0108] For example, assume a three-degree-of-freedom robotic arm operating on a point cloud on an irregular surface. The discrete point set contains 500 points, each with three-dimensional coordinates. When the end effector reaches a certain point (such as x = 10 cm, y = 5 cm, z = 2 cm), the joint angles may be 30 degrees, 45 degrees, and 60 degrees through inverse kinematics. Considering the joint displacement range limit (such as joint 1 limited to 0 - 90 degrees) and dynamic constraints (such as the speed not exceeding 2 m / s), the original joint angle sequence records the angle solutions for all points.

[0109] Specifically, the k-means clustering algorithm is used to group the original joint angle sequence. Exemplarily, assume that in the angle sequence of 500 points, the angles of some points change drastically due to the steepness of the surface. K-means can divide the sequence into 3 groups: small angle change in the flat area, moderate angle in the transition area, and large angle fluctuation in the steep area. Each group has a different degree of matching with the dynamic constraints. For example, the flat area has a low speed requirement and a high matching degree, while the steep area may exceed the speed limit and has a low matching degree. The classified angle sequence is thus formed, further reflecting the motion characteristics.

[0110] For example, if the angle at a point in the steep area is 95 degrees, exceeding the range of 0 - 90 degrees, it is adjusted to 90 degrees. The linear interpolation algorithm is used for smoothing processing to make the angles of adjacent points smoothly transition from 85 degrees to 90 degrees, avoiding sudden changes. The optimized angle sequence is thus generated, improving the smoothness.

[0111] For example, in the optimized angle sequence, the angle jump is reduced to less than 2 degrees. This processing ensures the continuous motion of the robotic arm, reduces vibration, and improves the execution accuracy. The direction consistency data of each point in the optimized angle sequence is determined through normal estimation. As an implementation, based on the coordinates of 5 neighboring points in the discrete point set, the normal vector of each point is estimated.

[0112] For example, the trajectory parameters can be set such that the end moves 1 cm in the normal direction while keeping the machining direction consistent. The advantage is to improve the geometric adaptability of the trajectory. For dynamic constraints, the support vector machine algorithm is used to detect anomalies in the motion trajectory parameters.

[0113] For example, assume that the speed of a certain point in the trajectory suddenly increases to 2.5 m / s, exceeding the 2 m / s constraint. The support vector machine marks it as an abnormal point through feature classification. The local resampling is used to adjust and optimize the angle sequence, such as adding sampling points near this point and recalculating the angles to reduce the speed to 1.8 m / s. The preliminary joint angle sequence and the preliminary trajectory sequence are thus generated.

[0114] In step S104, according to the workpiece material parameters and the machining process parameters, the optimal laser working parameters are matched from a pre-established laser processing parameter database, and a laser parameter variation curve matching the preliminary trajectory sequence is generated, including:

[0115] The k-nearest neighbor algorithm is used to retrieve the laser power, scanning speed, and focused spot parameters that match the workpiece material parameters and the machining process parameters from the pre-established laser processing parameter database to obtain a preliminary laser parameter set;

[0116] According to the preliminary laser parameter set, the interpolation algorithm is used to process the preliminary trajectory sequence to generate sequences of laser power and scanning speed changes corresponding to the trajectory points, obtaining an initial parameter variation curve;

[0117] The support vector machine algorithm is used to detect abnormal parameter points in the initial parameter variation curve that do not conform to the machining process parameters, and the parameter values are adjusted through local resampling to obtain a corrected parameter variation curve;

[0118] For the corrected parameter variation curve, the machining process parameter sequence is mapped to the discrete point set of the motion trajectory through grid division to obtain a spatially optimized data set;

[0119] The linear regression algorithm is used to smooth the spatially optimized data set to obtain the final laser parameter variation curve.

[0120] It should be noted that the laser power refers to the optical energy output by the laser per unit time; the scanning speed refers to the speed at which the laser beam moves on the workpiece surface. The scanning speed directly affects the effect of laser processing. A higher scanning speed can reduce the heat input and improve the processing efficiency, but too high a speed will lead to a decline in processing quality; the focused spot parameters include the spot size, spot shape, and spot quality, which describe the characteristics of the laser beam after focusing. In the embodiments of the present invention, the spot size is in micrometers (μm) and determines the accuracy and power density of laser processing. A smaller spot size can increase the power density and is suitable for fine processing, but too small a spot will cause an increase in the heat affected zone.

[0121] Exemplarily, when retrieving matching parameters from the laser processing parameter database using the k-nearest neighbor algorithm, a practical workpiece processing scenario is taken as an example. Suppose the workpiece material is aluminum alloy and the processing technology requires a surface roughness less than 1 micron. There are thousands of historical processing records stored in the database, including material parameters, thickness, laser power and other information. The k-nearest neighbor algorithm calculates the Euclidean distance between the current workpiece parameters and the records in the database, and finds 5 nearest neighbor data items as the preliminary laser parameter set.

[0122] It should be noted that the initial parameter variation curve refers to the function curve of the laser power and the scanning speed changing with time during the laser processing. The initial parameter variation curve is generated by interpolating the preliminary laser parameter set and the preliminary trajectory sequence, and is used to describe the dynamic changes of the laser power and the scanning speed during the laser processing. In a feasible implementation, when processing the preliminary trajectory sequence by the interpolation algorithm, the situation of the robotic arm processing along a curved path can be considered. Suppose the trajectory contains 100 discrete points, the preliminary parameter set gives a power of 300W and a speed of 1m / s, and the cubic spline interpolation algorithm is used to obtain the distance between points to generate the variation sequence.

[0123] For example, the starting power is 300W, a certain point in the middle is adjusted to 320W due to the increase in curvature, and the end drops to 280W. The speed smoothly transitions from 1m / s to 1.2m / s. The initial parameter variation curve is thus formed to adapt to the trajectory change.

[0124] It should be noted that the detection of abnormal parameter points usually uses the support vector machine (SVM) algorithm, especially the one-class support vector machine, which identifies abnormal parameter points that deviate from the normal processing parameter range by learning the distribution characteristics of normal data.

[0125] Specifically, when the support vector machine detects abnormal parameter points, suppose the speed at a certain point in the initial curve suddenly increases to 1.5m / s, exceeding the upper limit of 1.2m / s required by the process. After detecting the abnormal parameter point, the cubic spline interpolation is used to obtain the normal value to replace the abnormal parameter point in the initial parameter variation curve. The cubic spline interpolation fits the initial parameter variation curve through a piecewise polynomial function to generate a smooth parameter variation curve. The specific steps are as follows: extract the parameter values of the abnormal parameter and its adjacent points; calculate the new parameter value of the abnormal point using the cubic spline interpolation formula; replace the original parameter value of the abnormal point to generate the corrected parameter variation curve.

[0126] Preferably, when the mesh generation technology maps the machining process parameters, the trajectory discrete point set is divided into a 10x10 mesh. Assume that the power variation range within a certain mesh is from 290W to 310W. After mapping, a specific value is assigned to each point. For example, a point near the edge takes 295W. This spatially optimized data set reflects the correspondence between the trajectory and the parameters, facilitating subsequent processing.

[0127] In one embodiment, when the linear regression algorithm smooths the spatially optimized data set, assume that the power values of five points in a certain segment are 295W, 300W, 310W, 305W, and 298W respectively. After regression, they may be adjusted to 297W, 299W, 302W, 304W, and 300W. Finally, the laser parameter change curve is smoother, avoiding parameter mutations and improving machining consistency.

[0128] For example, for an aluminum alloy workpiece, when the power is finely adjusted from 300W to 302W and the speed is maintained at 1.1m / s, the surface roughness may be more uniform.

[0129] It can be understood that this smoothing process reduces the instantaneous impact of the laser on the material and prolongs the equipment life.

[0130] It should be noted that if the process requires an increase in the machining depth, the power can be adjusted to 320W through an extended scheme, and at the same time, the speed is reduced to 0.9m / s to maintain the heat input balance. This diversity design meets different requirements.

[0131] In one embodiment, after anomaly detection, the corrected curve may show that the power drops from 310W to 305W and the speed drops from 1.2m / s to 1m / s in a steep trajectory segment, matching the normal change. Multiple-sided verification shows that the machining accuracy is improved and the scrap rate is reduced after parameter adjustment.

[0132] For example, the flat segment has high consistency, and the steep segment has strong adaptability. The final curve takes into account both efficiency and quality.

[0133] In step S105, the laser parameter change curve and the preliminary joint angle sequence are synchronously mapped along the time axis, and the cubic spline interpolation algorithm is used to finely adjust the preliminary joint angle sequence to obtain a synchronous joint angle sequence, including:

[0134] The joint angle sequence is smoothed using the cubic spline interpolation algorithm to obtain a smoothed joint angle sequence;

[0135] The smoothed joint angle sequence is mapped into a motion speed sequence through time axis alignment, and the laser parameter change curve is mapped into a laser power change sequence;

[0136] The Kalman filter algorithm is used to filter the noise of the motion speed sequence to obtain a smoothed speed sequence;

[0137] The dynamic time warping algorithm is used to analyze the time deviation between the smoothed speed sequence and the laser power change sequence, and a calibrated synchronization sequence is obtained through calibration.

[0138] The direction consistency between the speed sequence in the calibrated synchronization sequence and the power change is judged through normal estimation, and a synchronized joint angle sequence is obtained.

[0139] Exemplarily, the cubic spline interpolation algorithm fits the data points between any two adjacent points of the joint angle sequence using a cubic polynomial to generate a smooth and continuous curve.

[0140] For example, assume that the robotic arm joint angle sequence contains 5 points: 10 degrees, 12 degrees, 15 degrees, 11 degrees, 13 degrees. Direct connection may result in sharp turns. After using cubic spline interpolation, a smooth curve is generated, and the angle values may become 10 degrees, 11.5 degrees, 14 degrees, 12.5 degrees, 13 degrees. This method ensures a natural transition of joint movement through piecewise polynomial fitting, avoiding jitter during the operation of the robotic arm. In a feasible implementation, by aligning the time axis, the angular difference between two adjacent points of the smoothed joint angle sequence is divided by the time interval between the two adjacent points to obtain the corresponding angular velocity, and the angular velocities of each point are combined in chronological order to obtain a motion speed sequence. Assume that the time interval is 0.1 second and the smoothed angle sequence is 10 degrees, 11.5 degrees, 14 degrees. The calculated speeds are 15 degrees / second and 25 degrees / second. Similarly, the laser parameter change curve is mapped to a laser power change sequence. For example, the initial power of 300W changes to 310W and 305W over time, which will not be elaborated here.

[0141] It should be noted that the Kalman filter is a recursive algorithm used to estimate the dynamic state of a system from noisy measurement data. When processing the motion speed sequence, the Kalman filter combines the prediction model of the system and the actual measurement data, continuously updates the estimated value of the speed, and uses the prior knowledge of the system (such as the motion model) and the statistical characteristics of the measurement data (such as the noise distribution) to calculate the optimal estimated value at each time step, thereby effectively filtering out the noise and obtaining a smoothed speed sequence. This method can provide accurate and smoothed speed estimation results in real time.

[0142] Exemplarily, the original speed sequence may be 15 degrees / second, 25 degrees / second, 20 degrees / second, but due to sensor noise, outliers such as 30 degrees / second are doped. The Kalman filter is smoothly adjusted to 15 degrees / second, 24 degrees / second, 21 degrees / second through the prediction and update steps. This processing reduces noise interference and ensures the reliability of speed data.

[0143] It should be noted that the dynamic time warping algorithm analyzes the time deviation between the smoothed velocity sequence and the laser power change sequence. First, it calculates the local distance between each point of the two sequences to form a distance matrix. Using dynamic programming techniques, it finds an optimal path in the distance matrix, which can minimize the cumulative distance between the two sequences. Through the optimal path, the two sequences can be aligned on the time axis to quantify the time deviation between them. Based on the optimal warping path, each point on the path can be converted into a time deviation sequence, representing the phase difference between the two sequences at different time points.

[0144] For example, the velocity sequence is 15 degrees per second and 24 degrees per second, and the power sequence is 300W and 310W, but their sampling times may be misaligned. Dynamic time warping adjusts the time axis by finding the best matching path to accurately correspond 24 degrees per second of velocity with 310W of power, generating a calibrated synchronization sequence.

[0145] Preferably, the normal estimation technique determines the direction consistency between the velocity sequence and the power change. Assuming that the machining trajectory is a curve, the velocity direction changes along the tangent, and the power needs to increase as the velocity increases.

[0146] In one embodiment, the velocity increases from 15 degrees per second to 24 degrees per second, and the power increases from 300W to 310W. The normal estimation confirms that their directions are consistent. If the power drops to 290W, it is mismatched and needs to be adjusted to a synchronized joint angle sequence. This consistency ensures the stability during the machining process.

[0147] Step S106, adopt the B-spline curve fitting algorithm to smooth the preliminary trajectory sequence, and combine it with the synchronized joint angle sequence to obtain a motion trajectory control instruction to control the operation of the robotic arm.

[0148] The B-spline curve fitting algorithm fits discrete trajectory points into a continuous and smooth curve by means of piecewise polynomial interpolation. It has local adjustability and high-order continuity, can effectively reduce the impact and jitter during the motion of the robotic arm, and improve the machining accuracy and efficiency. The B-spline curve fitting performs uniform parameterization on the preliminary trajectory sequence and generates a knot vector. According to the preliminary trajectory sequence and the knot vector, the control points of the B-spline curve are calculated by the least squares method, and the final smooth trajectory is generated through the De Boor algorithm. Then, combined with the synchronized joint angle sequence, a motion trajectory control instruction is generated to control the motion of the robotic arm and its end.

[0149] Step S107, the robotic arm moves according to the motion trajectory control instruction. When the machining environment parameters change, the preset trajectory compensation coefficient is dynamically adjusted, and the trajectory instruction is online compensated through the adaptive Kalman filtering algorithm to obtain a corrected motion control instruction.

[0150] Monitor the changes of the machining environment parameters in real time, calculate the preliminary trajectory instruction adjustment amount using the preset initial compensation coefficient, and obtain the first trajectory adjustment sequence; according to the time distribution characteristics of the first trajectory adjustment sequence, dynamically adjust the compensation coefficient, combine with the change trend of the machining environment parameters, and use the adaptive Kalman filter algorithm to smooth and correct the first trajectory adjustment sequence to obtain the second trajectory adjustment sequence; according to the trajectory instruction distribution of the second trajectory adjustment sequence, suppress the high-frequency noise through the filter algorithm to obtain the third trajectory adjustment sequence; for the continuity of the control sequence of the third trajectory adjustment sequence, perform secondary correction on the deviation of the trajectory instruction through online compensation to obtain the corrected motion control instruction.

[0151] It should be noted that the trajectory instruction adjustment amount refers to the amount of adjustment of the motion trajectory instruction of the robotic arm or laser processing equipment according to the changes in the machining environment parameters monitored in real time. The trajectory instruction adjustment amount is used to compensate for the trajectory deviation caused by environmental changes (such as temperature and load changes) or system errors, so as to ensure the accuracy and stability of the machining process.

[0152] It should be noted that the initial compensation coefficient is a key parameter that determines the trajectory instruction adjustment amount. If it is set too large, the robotic arm and laser will be easily affected by the environment. If it is set too small, it will be difficult for the robotic arm and laser to make adaptive adjustments according to the environment. Monitoring the changes of the machining environment parameters in real time is the basis for ensuring the accuracy of the trajectory instruction adjustment.

[0153] Exemplarily, in a machining scenario, machining environment parameters such as temperature, humidity, or machine tool vibration will directly affect the accuracy of the tool path. Suppose a CNC machine tool is machining an aluminum part, and the environmental temperature rises from 25 degrees Celsius to 30 degrees Celsius. Thermal expansion may cause the tool to shift by 0.02 millimeters. Using the preset initial compensation coefficient, such as 0.8, to calculate the preliminary trajectory instruction adjustment amount can generate the first trajectory adjustment sequence. This sequence records the adjustment value at each moment, such as adjusting the tool position by 0.01 millimeters per second to cope with the impact of temperature changes. The advantage of this is that it can quickly respond to environmental changes and avoid the accumulation of machining errors. According to the time distribution characteristics of the first trajectory adjustment sequence, it is particularly important to dynamically adjust the compensation coefficient.

[0154] Specifically, if the sequence shows that the adjustment value fluctuates periodically in time, such as a large offset peak appears every 10 seconds, it may be caused by machine tool vibration. At this time, the compensation coefficient can be dynamically adjusted from 0.8 to 1.2, combined with the change trend of the machining environment parameters, such as the increase in vibration frequency, and the adaptive Kalman filter algorithm is used for smoothing correction.

[0155] It can be understood that adaptive Kalman filtering can effectively reduce the influence of random noise through prediction and update steps, generating a second trajectory adjustment sequence.

[0156] For example, in the corrected sequence, the fluctuation range of the adjustment value is reduced from ±0.01 mm to ±0.005 mm, improving the stability of the trajectory. The resulting technical effect is a significant improvement in machining accuracy. In a feasible implementation, according to the trajectory instruction distribution of the second trajectory adjustment sequence, a filtering algorithm is required to suppress high-frequency noise. Suppose there is noise with a frequency higher than 50 Hz in the sequence, which may be caused by high-frequency interference of the motor. At this time, a low-pass filter can be used to filter out the high-frequency components, obtaining a third trajectory adjustment sequence.

[0157] For example, the original 50 tiny jitters per second are smoothed into 5 stable adjustments per second, and the trajectory instruction distribution is more uniform. This processing can reduce the unnecessary oscillation of the machine tool, extend the equipment life, and ensure the surface finish of the machining. Regarding the continuity of the control sequence of the third trajectory adjustment sequence, online compensation is used to secondarily correct the trajectory instruction deviation.

[0158] Preferably, if the continuity analysis finds that the adjustment value of a certain trajectory instruction suddenly changes from 0.005 mm to 0.015 mm, which may be caused by sensor delay. Through online compensation, this mutation is detected in real time and smoothly transitioned. For example, the mutation value is adjusted to 0.008 mm, generating the final corrected motion control instruction.

[0159] In one embodiment, this method can control the machining error within ±0.003 mm.

[0160] For example, when machining a precision gear, this smooth correction can ensure the tooth profile consistency and avoid assembly failure caused by deviation. Its beneficial effect is to improve the reliability of the control instruction and the machining quality.

[0161] It should be noted that the implementation of each technical theme closely revolves around the core logic of the machining environment and trajectory adjustment.

[0162] For example, the combination of dynamic adjustment of the compensation coefficient and adaptive Kalman filtering not only smooths the trajectory but also adapts to the unpredictability of environmental changes; the combination of the filtering algorithm and online compensation forms a complete solution from noise suppression to continuity optimization. This multi-faceted supported design ensures the robustness and efficiency of the trajectory instruction adjustment, ultimately achieving the goal of high-precision machining.

[0163] Using high-precision encoders at the joints of the robotic arm and power sensors on the laser processing head, the actual rotation angles of each joint and the laser output parameters are collected in real time, and compared with the corrected motion control instructions. If the deviation exceeds the preset threshold, a closed-loop feedback control is triggered to generate a feedback control instruction for real-time adjustment of the robotic arm motion trajectory and laser parameters.

[0164] Obtain the actual rotation angles of each joint and the laser output parameters collected in real time by the high-precision encoders and power sensors to get the original data sequence; compare the original data sequence with the corrected motion control instructions, and use a deviation calculation method to determine the deviation value sequence; judge whether the deviation value sequence exceeds the preset deviation threshold. If so, start the closed-loop feedback mechanism. If not, do nothing and generate a preliminary feedback control sequence; for the preliminary feedback control sequence, use a low-pass filter algorithm to suppress high-frequency interference to obtain a smoothed control sequence; combine the change trend of the actual rotation angle and adjust the smoothed control sequence through online compensation to obtain an optimized control sequence; for the matching situation between the optimized control sequence and the laser output parameters, use a proportional adjustment method to correct the optimized control sequence to obtain a feedback control instruction.

[0165] Exemplarily, collecting the actual rotation angles of each joint and the laser output parameters in real time by the high-precision encoders and power sensors to obtain the original data sequence is the basis of the entire control process.

[0166] It should be noted that the original data sequence is arranged from the actual rotation angles and the laser output parameters in the order of collection time.

[0167] Exemplarily, in a robotic arm processing device, the high-precision encoder can collect 1000 joint rotation angle data per second. For example, the rotation angle of joint 1 changes from 10 degrees to 10.2 degrees, while the power sensor synchronously records that the laser output power slightly decreases from 500 watts to 498 watts. These data form the original data sequence, reflecting the real-time state of the device operation.

[0168] It can be understood that this high-frequency collection can capture subtle changes and provide a reliable basis for subsequent deviation analysis. Comparing the original data sequence with the corrected motion control and using a deviation calculation method to determine the deviation value sequence is the key to judging whether the system needs adjustment.

[0169] Specifically, assume that the preset instruction requires the joint rotation angle to remain at 10 degrees, while the actual value is 10.2 degrees, with a deviation value of 0.2 degrees; the laser power is preset at 500 watts, the actual value is 498 watts, and the deviation is 2 watts. This sequence of deviation values intuitively reflects the situation where the system deviates from the expectation. In a feasible implementation, the sequence of deviation values is arranged in time, for example, recorded every 0.1 seconds, forming a dynamic trend, which helps with subsequent judgment. Judging whether the sequence of deviation values exceeds the preset deviation threshold determines whether to activate the closed-loop feedback mechanism.

[0170] Preferably, if the thresholds are set at 0.1 degree for rotation angle and 3 watts for power, among the above deviations of 0.2 degrees for rotation angle and 2 watts for power, the rotation angle exceeds the threshold while the power does not. At this time, the closed-loop feedback mechanism is activated to generate a preliminary feedback control sequence.

[0171] For example, the sequence may indicate that joint 1 needs to be adjusted back by 0.15 degrees to approach the target value. This mechanism ensures that the system responds promptly to significant deviations and improves stability. For the preliminary feedback control sequence, a low-pass filtering algorithm is used to suppress high-frequency interference, obtaining a smooth control sequence, which can effectively cope with the influence of noise.

[0172] In one embodiment, assume that there are tiny jitters 20 times per second in the sequence, which are caused by motor operation interference. The low-pass filtering can smooth the adjustment frequency to 5 times per second.

[0173] For example, the original abrupt adjustment of 0.15 degrees becomes a gradual decrease of 0.05 degrees. This smoothing process reduces the unnecessary jitter of the robotic arm and helps extend the equipment life. Combining with the changing trend of the actual rotation angle, the smooth control sequence is adjusted through online compensation to obtain an optimized control sequence, further improving the response accuracy.

[0174] Specifically, if the rotation angle continuously rises from 10.2 degrees to 10.3 degrees, indicating a trend offset, the online compensation can be dynamically adjusted according to the change rate.

[0175] For example, the adjustment value is increased from 0.05 degrees to 0.08 degrees to form an optimized sequence. This method adapts to the gradual change characteristics of the environment or load and ensures the flexibility of control. Regarding the matching situation between the optimized control sequence and the laser output parameters, a proportional adjustment method is used for correction to obtain the feedback control instruction, which is an important step to achieve coordinated control.

[0176] For example, if the laser power of 498 watts does not fully match the rotation angle adjustment, the proportional adjustment can fine-tune the power to 499 watts while keeping the rotation angle adjustment at 0.08 degrees.

[0177] It should be noted that this matching correction ensures the consistency between the laser and the robotic arm movement during the processing, effectively improving the processing quality.

[0178] In one embodiment, it is assumed that when processing a metal sheet, corner deviation and power fluctuation may cause uneven cutting. Through the above process, from the acquisition of raw data to the generation of feedback commands, the system can quickly adjust the joint positions and laser output.

[0179] For example, the corner smoothly returns from 10.2 degrees to 10 degrees, and the power stabilizes at 500 watts. This multi-link collaborative control method not only improves the processing accuracy but also enhances the adaptability of the equipment, providing a reliable guarantee for high-precision tasks.

[0180] Referring to Figure 2 , the present invention provides a path planning device for synchronized processing of a robotic arm and a laser, mainly including:

[0181] A data acquisition module, configured to acquire three-dimensional model data of a workpiece, workpiece material parameters, machining process parameters, acquire the displacement ranges and dynamic constraint conditions of each joint of the robotic arm, and acquire machining environment parameters;

[0182] A point cloud generation module, configured to extract the irregular surface in the three-dimensional model data and generate a high-density discrete point set by using a point cloud sampling algorithm;

[0183] A preliminary trajectory generation module, configured to calculate the target pose of the end effector of the robotic arm at each discrete point according to the high-density discrete point set by using an inverse kinematics algorithm, and combine the displacement range and the dynamic constraint conditions to obtain a preliminary joint angle sequence and a preliminary trajectory sequence;

[0184] A laser parameter matching module, configured to match the optimal laser working parameters from a pre-established laser processing parameter database according to the workpiece material parameters and the machining process parameters, and generate a laser parameter change curve matching the preliminary trajectory sequence;

[0185] A synchronization mapping module, configured to synchronously map the laser parameter change curve and the preliminary joint angle sequence along the time axis, and finely adjust the preliminary joint angle sequence by using a spline interpolation algorithm to obtain a synchronized joint angle sequence;

[0186] A trajectory smoothing module, configured to perform smoothing processing on the preliminary trajectory sequence by using a B-spline curve fitting algorithm, and combine the synchronized joint angle sequence to obtain a motion trajectory control instruction to control the operation of the robotic arm;

[0187] A dynamic adjustment module, configured to make the robotic arm move according to the motion trajectory control instruction, dynamically adjust a preset trajectory compensation coefficient when the machining environment parameters change, and perform online compensation on the trajectory instruction through an adaptive Kalman filtering algorithm to obtain a corrected motion control instruction.

[0188] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and supplements can be made, and these improvements and supplements should also be regarded as the protection scope of the present invention.

[0189] The embodiment of the present invention also provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a path planning program for synchronous processing of a robotic arm and a laser. When the processor executes the computer program, it implements the steps in the above-mentioned embodiments of the path planning method for synchronous processing of a robotic arm and a laser, such as Figure 1 step S101 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module in the above-mentioned device embodiments, such as a dynamic adjustment module.

[0190] Exemplarily, the computer program can be divided into one or more modules. The one or more modules are stored in the memory and executed by the processor to complete the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the electronic device.

[0191] The electronic device can be a computing device such as a desktop computer, a notebook, a palm computer, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above components are only examples of the electronic device and do not constitute a limitation to the electronic device. It may include more or fewer components than the above, or combine some components, or different components. For example, the electronic device may further include input / output devices, network access devices, a bus, etc.

[0192] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the electronic device and connects various parts of the entire electronic device through various interfaces and lines.

[0193] The memory can be used to store the computer programs and modules. By running or executing the computer programs and modules stored in the memory, and by invoking the data stored in the memory, the processor can implement various functions of the electronic device. The memory may mainly include a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0194] Among them, if the modules integrated in the electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0195] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationships between the modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0196] The specific embodiments described above further elaborate on the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. In particular, it is pointed out that for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A path planning method for synchronized machining of a robotic arm and a laser, characterized in that, The method includes: Obtaining the three-dimensional model data of the workpiece, the workpiece material parameters, the machining process parameters, obtaining the displacement ranges of the joints of the robotic arm and the dynamic constraint conditions, and obtaining the machining environment parameters; Extracting the irregular surfaces from the three-dimensional model data, and generating a high-density discrete point set by using a point cloud sampling algorithm; According to the high-density discrete point set, using an inverse kinematics algorithm to calculate the target pose of the end effector of the robotic arm at each discrete point, and combining the displacement range and the dynamic constraint conditions to obtain a preliminary joint angle sequence and a preliminary trajectory sequence; According to the workpiece material parameters and the machining process parameters, matching the optimal laser working parameters from a pre-established laser processing parameter database, and generating a laser parameter change curve that matches the preliminary trajectory sequence; Synchronously mapping the laser parameter change curve and the preliminary joint angle sequence along the time axis, and using a spline interpolation algorithm to finely adjust the preliminary joint angle sequence to obtain a synchronous joint angle sequence; Using a B-spline curve fitting algorithm to smooth the preliminary trajectory sequence, and combining the synchronous joint angle sequence to obtain a motion trajectory control instruction to control the operation of the robotic arm; The robotic arm moves according to the motion trajectory control instruction. When the machining environment parameters change, dynamically adjust the preset trajectory compensation coefficient, and perform online compensation on the trajectory instruction through an adaptive Kalman filtering algorithm to obtain a corrected motion control instruction; Among them, the step of using an inverse kinematics algorithm to calculate the target pose of the end effector of the robotic arm at each discrete point according to the high-density discrete point set, and combining the displacement range and the dynamic constraint conditions to obtain a preliminary joint angle sequence and a preliminary trajectory sequence includes: Obtaining the discrete point set data, using an inverse kinematics algorithm to determine the target pose of the end effector of the robotic arm at each discrete point, and obtaining an original joint angle sequence for the joint displacement range limit and the dynamic constraint conditions; Grouping the original joint angle sequence by using a k-means clustering algorithm, and obtaining a classified angle sequence according to the matching degree between each group of the original joint angle sequences and the dynamic constraint conditions; Adjusting the angle values in the classified angle sequence that exceed the joint displacement range limit to the maximum value of the joint displacement range, and performing smoothing processing by using an interpolation algorithm to obtain an optimized angle sequence; Determining the direction consistency data of the end effector of the robotic arm at each discrete point in the optimized angle sequence through normal estimation, and the robotic arm combines the direction consistency data to obtain motion trajectory parameters; For the dynamic constraint conditions, using a support vector machine algorithm to perform anomaly detection on the motion trajectory parameters. If an abnormal trajectory point is detected, adjust the optimized angle sequence through local resampling to obtain a preliminary joint angle sequence and a preliminary trajectory sequence.

2. The method according to claim 1, wherein The step of extracting the irregular surfaces from the three-dimensional model data and generating a high-density discrete point set by using a point cloud sampling algorithm includes: For the irregular surface, generating an initial discrete point set by using a uniform point cloud sampling algorithm; Determine whether the point set density of the initial discrete point set is lower than a preset density threshold. If so, process the initial discrete point set using a local encryption sampling algorithm. If not, do not perform any processing to obtain enhanced discrete point set data; According to the enhanced discrete point set data, use the random forest algorithm to classify the irregular surface to obtain different feature regions, and obtain the geometric property distribution of the different feature regions; According to the geometric property distribution, determine the direction consistency data of the surface of the irregular surface through normal estimation; According to the direction consistency data, for the enhanced discrete point set, use the k-nearest neighbor algorithm to smooth the point set spacing to obtain a high-density discrete point set.

3. The method according to claim 1, wherein The step of matching the optimal laser working parameters from the pre-established laser processing parameter database according to the workpiece material parameters and the processing technology parameters to generate a laser parameter change curve matching the preliminary trajectory sequence includes: Use the k-nearest neighbor algorithm to retrieve the laser power, scanning speed, and focused spot parameters matching the workpiece material parameters and the processing technology parameters from the pre-established laser processing parameter database to obtain a preliminary laser parameter set; According to the preliminary laser parameter set, use the interpolation algorithm to process the preliminary trajectory sequence to generate a laser power and scanning speed change sequence corresponding to the trajectory points to obtain an initial parameter change curve; Use the support vector machine algorithm to detect the abnormal parameter points in the initial parameter change curve that do not match the processing technology parameters, and adjust the parameter values through local resampling to obtain a corrected parameter change curve; Map the processing technology parameter sequence to the discrete point set of the motion trajectory through grid division for the corrected parameter change curve to obtain a spatially optimized data set; Use the linear regression algorithm to smooth the spatially optimized data set to obtain the final laser parameter change curve.

4. The method according to claim 1, wherein The step of synchronously mapping the laser parameter change curve and the preliminary joint angle sequence along the time axis and using the spline interpolation algorithm to finely adjust the preliminary joint angle sequence to obtain a synchronized joint angle sequence includes: Use the cubic spline interpolation algorithm to smooth the joint angle sequence to obtain a smoothed joint angle sequence; Map the smoothed joint angle sequence to a motion speed sequence through time axis alignment, and map the laser parameter change curve to a laser power change sequence; Use the Kalman filter algorithm to filter the noise of the motion speed sequence to obtain a smoothed speed sequence; Use the dynamic time warping algorithm to analyze the time deviation between the smoothed speed sequence and the laser power change sequence, and calibrate to obtain a calibrated synchronization sequence; Judge the direction consistency between the speed sequence and the power change in the calibrated synchronization sequence through normal estimation to obtain a synchronized joint angle sequence.

5. The method according to claim 1, wherein The robotic arm moves according to the motion trajectory control instruction. When the processing environment parameters change, dynamically adjust the preset trajectory compensation coefficient, and perform online compensation on the trajectory instruction through the adaptive Kalman filter algorithm to obtain a corrected motion control instruction, including: Monitor the changes of the machining environment parameters in real time, calculate the preliminary trajectory instruction adjustment amount using the preset initial compensation coefficient, and obtain the first trajectory adjustment sequence; According to the time distribution characteristics of the first trajectory adjustment sequence, dynamically adjust the compensation coefficient, combine with the change trend of the machining environment parameters, and use the adaptive Kalman filter algorithm to smooth and correct the first trajectory adjustment sequence to obtain the second trajectory adjustment sequence; According to the trajectory instruction distribution of the second trajectory adjustment sequence, suppress the high-frequency noise through a filtering algorithm to obtain the third trajectory adjustment sequence; For the continuity of the control sequence of the third trajectory adjustment sequence, perform a secondary correction on the deviation of the trajectory instruction through online compensation to obtain the corrected motion control instruction.

6. The method according to claim 5, wherein It further includes: Utilize the high-precision encoder at the robotic arm joint and the power sensor on the laser processing head to collect the actual rotation angle of each joint and the laser output parameters in real time, and compare them with the corrected motion control instruction. If the deviation exceeds the preset threshold, trigger the closed-loop feedback control, generate the feedback control instruction, and perform real-time adjustment on the robotic arm motion trajectory and laser parameters, specifically including: Obtain the actual rotation angle of each joint and the laser output parameters collected in real time through the high-precision encoder and the power sensor to obtain the original data sequence; Compare the original data sequence with the corrected motion control instruction, and use the deviation calculation method to determine the deviation value sequence; Judge whether the deviation value sequence exceeds the preset deviation threshold. If so, start the closed-loop feedback mechanism. If not, do not perform any processing and generate the preliminary feedback control sequence; For the preliminary feedback control sequence, suppress the high-frequency interference through a low-pass filtering algorithm to obtain the smooth control sequence; Combine with the change trend of the actual rotation angle, and adjust the smooth control sequence through online compensation to obtain the optimized control sequence; For the matching situation between the optimized control sequence and the laser output parameters, correct the optimized control sequence using the proportional adjustment method to obtain the feedback control instruction.

7. A path planning device for synchronized machining of a robotic arm and a laser, characterized in that, For implementing the path planning method for synchronous machining of a robotic arm and a laser as described in any one of claims 1 to 6, the device includes: A data acquisition module, used to acquire the three-dimensional model data of the workpiece, the workpiece material parameters, the machining process parameters, acquire the displacement range and dynamic constraint conditions of each joint of the robotic arm, and acquire the machining environment parameters; A point cloud generation module, used to extract the irregular surface in the three-dimensional model data and generate a high-density discrete point set using the point cloud sampling algorithm; A preliminary trajectory generation module, used to calculate the target pose of the end effector of the robotic arm at each discrete point according to the high-density discrete point set using the inverse kinematics algorithm, and combine the displacement range and the dynamic constraint conditions to obtain the preliminary joint angle sequence and the preliminary trajectory sequence; A laser parameter matching module, used to match the optimal laser working parameters from the pre-established laser processing parameter database according to the workpiece material parameters and the machining process parameters, and generate a laser parameter change curve matching the preliminary trajectory sequence; A synchronization mapping module, configured to synchronously map the laser parameter variation curve and the preliminary joint angle sequence along the time axis, and use a spline interpolation algorithm to finely adjust the preliminary joint angle sequence to obtain a synchronized joint angle sequence; A trajectory smoothing module, configured to use a B-spline curve fitting algorithm to smooth the preliminary trajectory sequence, and combine the synchronized joint angle sequence to obtain a motion trajectory control instruction for controlling the operation of the robotic arm; A dynamic adjustment module, configured to enable the robotic arm to move according to the motion trajectory control instruction. When the processing environment parameters change, dynamically adjust a preset trajectory compensation coefficient, and perform online compensation on the trajectory instruction through an adaptive Kalman filtering algorithm to obtain a corrected motion control instruction.

8. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a path planning method for synchronized machining of a robotic arm and a laser as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein when the computer program runs, it controls the device where the computer-readable storage medium is located to execute a path planning method for synchronized machining of a robotic arm and a laser as described in any one of claims 1 to 6.

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