Track generation method and device for deburring of casting blank and medium

Abstract

The invention discloses a track generation method and device for casting blank deburring and a medium, and relates to the technical field of grinding process.The track generation method mainly comprises the steps that according to FPFH feature descriptors of respective outer contours of an actual point cloud model and a standard point cloud model, key points matched with the standard point cloud model are searched through a KD tree, and the FPFH feature descriptors of the outer contours of the actual point cloud model and the standard point cloud model are obtained; performing multi-contour segmentation division of the actual point cloud model according to the distance between the key points; receiving polishing parameters input by the user based on the segmentation division results of the contours, and generating an initial track according to the polishing parameters; smoothing discrete trajectory points of the initial trajectory through B-spline interpolation, and generating a local obstacle avoidance path in combination with an industrial robot kinematics model and a fast expansion random tree star algorithm; outputting the final polishing track to a control terminal, and adjusting the track depth in real time according to the feedback of the force sensor. According to the method, regional differentiation track generation is supported, and the process adaptability and the personalized demand response capability of a complex contour are improved.

Description

Technical Field

[0001] The invention relates to the technical field of grinding technology, and in particular to a trajectory generation method, a device and a medium for deburring a casting blank. Background Art

[0002] In the field of industrial automation, casting deburring technology based on 3D vision has been gradually applied to actual production. The existing technology usually obtains casting point cloud data through 3D visual imaging, aligns it with the pre-stored standard 3D model, and directly generates a grinding trajectory based on the outer contour of the standard model. However, this method has significant defects: First, due to the limitations of the casting process or deviations in the production process, the actual shape of the casting blank often has local differences from the standard model. If the contour of the standard model is directly applied for grinding, it may cause excessive grinding depth, causing damage to the casting surface or even damage to the grinding device, affecting processing safety and finished product qualification rate. Secondly, the existing technology lacks adaptability to diversified needs. For example, different processing areas have personalized requirements for parameters such as chamfer depth and lateral expansion width. Some areas need to avoid the processed surface or use a specific feed trajectory (such as a quadratic function parameterized trajectory). However, the existing method only relies on the outer contour to generate a fixed trajectory, and cannot achieve segmented parameterized adjustment, resulting in limited application scenarios. In addition, the existing technology does not fully consider the dynamic registration accuracy of the actual point cloud and the standard model, and the interference of the mismatched point cloud may further reduce the reliability of trajectory generation. Therefore, there is an urgent need for a trajectory generation method that can combine the actual morphology of the casting, support segmented parameter configuration and has high robustness to improve the grinding accuracy and process flexibility. Summary of the invention

[0003] In view of the shortcomings of the prior art, the present invention proposes a trajectory generation method for deburring a casting blank, comprising the steps of: S1: Scan the casting blank with a 3D linear array camera to obtain the actual point cloud model and extract the pre-recorded standard point cloud model; S2: Based on the improved iterative closest point algorithm, the actual point cloud model and the standard point cloud model are aligned under the coordinate system registration adjustment; S3: Based on the FPFH feature descriptors of the outer contours of the actual point cloud model and the standard point cloud model, the key points matching the standard point cloud model are searched through the KD tree, and the multi-contour segmentation of the actual point cloud model is performed according to the distance between the key points; S4: receiving polishing parameters input by the user based on the segmentation results of each contour, and generating an initial trajectory according to the polishing parameters; S5: The initial trajectory is smoothed by B-spline interpolation, and the local obstacle avoidance path is generated by combining the industrial robot kinematic model and the fast expansion random tree star algorithm; S6: Output the final grinding track to the control terminal and adjust the track depth in real time according to the feedback from the force sensor.

[0004] Furthermore, in the step S2, the improved iterative closest point algorithm is specifically: S21: extract the FPFH feature descriptors of the outer contours of the actual point cloud model and the standard point cloud model and select the initial pairing points; S22: The mismatched points are eliminated through the random sampling consistency algorithm, and the actual point cloud model and the standard point cloud model are aligned with the coordinate system through the following formula: In the formula, is the rotation matrix, is the translation vector, is the total amount of 3D points in the point cloud model, The actual point cloud model Three-dimensional points, The first 3D points; S23: Iterative optimization of rotation matrix and translation vectors , until the error of the registration adjustment is less than the preset error threshold.

[0005] Furthermore, in the step S3, the key points of the standard point cloud model searched by the KD tree are expressed as follows: In the formula, The actual point cloud model Points The FPFH feature descriptor, The first Three-dimensional points, The first Points The FPFH feature descriptor, To find the corresponding point in all the standard point cloud candidate points Index with minimum Euclidean distance .

[0006] Furthermore, in the step S4, the grinding parameters include grinding depth , Horizontal expansion width The quadratic function feed angle of the chamfering required area ,in, For user input coefficients, Polish the track for the final touch.

[0007] Furthermore, the initial trajectory is expressed as the following formula: In the formula, is the normal offset trajectory, For horizontal expansion trajectory, is the reference trajectory obtained based on the standard point cloud model, N is the point cloud normal direction, and T is the edge tangent direction.

[0008] Furthermore, in the step S5, the smoothing of discrete trajectory points of the initial trajectory by B-spline interpolation is expressed as the following formula: In the formula, is a parameter variable, is the optimized initial trajectory, for The order is B-spline basis functions, is the control point in the initial trajectory that is dynamically adjusted according to the simulation results of the industrial robot kinematic model. is the total number of control points.

[0009] Furthermore, in the step S5, the generation of the local obstacle avoidance path by combining the industrial robot kinematic model and the RRT* algorithm is specifically as follows: Construct geometric bounding box models of robots, fixtures, and workpieces; Verify the feasibility of trajectory pose through inverse kinematics solution; If interference is detected, a fast expanding random tree star algorithm is used to generate a local obstacle avoidance path.

[0010] Furthermore, in the step S6, the real-time adjustment of the track depth according to the feedback of the force sensor is expressed as the following formula: In the formula, is the adjusted track depth, is the original track depth, is the proportionality coefficient, is the base pressure, is the real-time pressure of the force sensor.

[0011] The present invention also includes a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the trajectory generation method for deburring a casting blank are implemented.

[0012] Also included is a device for processing data, comprising: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the steps of the trajectory generation method for deburring a casting blank.

[0013] Compared with the prior art, the present invention has at least the following beneficial effects: (1) The present invention proposes a trajectory generation method for deburring casting blanks. It realizes key point matching and segmentation of the casting outer contour based on FPFH features and KD tree search. It supports differentiated trajectory generation for different areas (such as chamfers and burr concentration areas) in combination with the polishing parameters input by the user, significantly improving the process adaptability and personalized demand response capabilities of complex contours. (2) By combining the improved iterative closest point algorithm (ICP) with FPFH feature descriptor screening and random sampling consensus (RANSAC) mismatch elimination, the registration accuracy between the actual point cloud and the standard model is significantly improved, the coordinate system alignment error caused by casting deformation is effectively reduced, and excessive grinding or equipment collision caused by model deviation is avoided; (3) The initial trajectory is smoothed by discrete points through B-spline interpolation to reduce robot motion jitter; the local obstacle avoidance path is dynamically generated by combining the industrial robot kinematic model with the rapidly expanding random tree star algorithm to ensure real-time avoidance of the fixture and workpiece interference area during the grinding process, taking into account both processing efficiency and safety; (4) The trajectory depth is dynamically corrected based on the pressure difference feedback from the force sensor to achieve closed-loop control of the grinding force, effectively dealing with uneven burr thickness or undulations on the casting surface, and ensuring processing stability and finished product consistency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The figure is a step diagram of a trajectory generation method for deburring a casting blank. DETAILED DESCRIPTION

[0015] The following are specific embodiments of the present invention and the accompanying drawings to further describe the technical solution of the present invention, but the present invention is not limited to these embodiments.

[0016] In order to more intuitively reflect the practical application effect of the technical solution of the present invention, the specific implementation method is described in detail in combination with typical industrial scenarios. This embodiment takes the automated deburring of the turbocharger housing blank as an example, and constructs a complete intelligent polishing system by integrating 3D visual perception, dynamic trajectory planning and real-time feedback control. In actual applications, the system first obtains the three-dimensional data of the blank surface through a high-precision 3D scanning device, and combines the improved point cloud registration method to accurately align the actual workpiece with the theoretical model; then, based on feature analysis and the user interaction interface, flexible segmentation and parameter configuration of the polishing area are achieved, and customized trajectories are generated for different contour features (such as curved surface transition areas, edge burr areas); finally, the robot kinematics optimization and real-time force control feedback are used to ensure the stability and consistency of the polishing process. The following will focus on the hardware architecture, core algorithm implementation and multi-module collaborative mechanism. The detailed description will focus on the point cloud registration accuracy improvement strategy, the segmented trajectory adaptive generation method, and the engineering application of dynamic obstacle avoidance and pressure compensation technology, so as to fully present the technical implementation path and innovative advantages of this solution in complex industrial scenarios. Figure 1 As shown, a trajectory generation method for deburring a casting blank proposed by the present invention mainly comprises the following steps: S1: Scan the casting blank with a 3D linear array camera to obtain the actual point cloud model and extract the pre-recorded standard point cloud model; S2: Based on the improved iterative closest point algorithm, the actual point cloud model and the standard point cloud model are aligned under the coordinate system registration adjustment; S3: Based on the FPFH feature descriptors of the outer contours of the actual point cloud model and the standard point cloud model, the key points matching the standard point cloud model are searched through the KD tree, and the multi-contour segmentation of the actual point cloud model is performed according to the distance between the key points; S4: receiving polishing parameters input by the user based on the segmentation results of each contour, and generating an initial trajectory according to the polishing parameters; S5: The initial trajectory is smoothed by B-spline interpolation, and the local obstacle avoidance path is generated by combining the industrial robot kinematic model and the fast expansion random tree star algorithm; S6: Output the final grinding track to the control terminal and adjust the track depth in real time according to the feedback from the force sensor.

[0017] During the scanning and data acquisition stage, the system completes the 3D data acquisition and preprocessing of the casting blank through an integrated hardware architecture. In the specific implementation, the turbocharger housing blank is transported from the feeding system to the fixed fixture of the positioner. The positioner adjusts the workpiece posture according to the preset program to ensure that the surface to be polished is vertically facing the scanning direction. The 3D linear array camera is mounted on the truss system and moves bidirectionally along the XY axis to scan the casting surface at multiple angles at a constant speed, covering key areas such as the wheel rim, pores and flange connection surface of the blank. During the scanning process, the linear array camera captures point cloud data at a sampling rate of 2000 frames per second, synchronously triggers the ring LED light source to eliminate metal reflection interference, and performs point cloud denoising in real time to preliminarily filter out abnormal discrete points caused by dust or vibration. After completing the scan, the system starts the point cloud preprocessing process, including ground point cloud segmentation, non-polishing area shielding and point cloud simplification.

[0018] Then, the system uses the improved iterative closest point algorithm (ICP) to align the actual point cloud model with the standard point cloud model with high precision. In the specific implementation, the FPFH (Fast Point Feature Histograms) feature descriptors of the outer contours of the actual point cloud model and the standard point cloud model are first extracted, and the initial pairing points are screened through feature matching. The FPFH feature descriptor can effectively characterize the local geometric characteristics of the point cloud (such as curvature and normal direction) and provide a reliable initial matching pair for subsequent registration. In order to improve the matching accuracy, the system introduces the random sampling consensus algorithm (RANSAC) to eliminate mismatched points and ensure the uniformity of the spatial distribution of the pairing points.

[0019] During the registration process, the system minimizes the rotation matrix and translation vectors The error function realizes the alignment of coordinate system. The specific formula is: in, is the total number of data points in the point cloud, The actual point cloud model Three-dimensional points, The first Through iterative optimization, the system gradually adjusts the rotation matrix and translation vectors , until the registration error is less than the preset error threshold. In each iteration, the system dynamically updates the matching point pairs and optimizes the registration results in combination with the local curvature constraint to avoid misalignment caused by similar local features.

[0020] To further improve the robustness of registration, the system also introduces a multi-scale registration strategy: First, in the coarse registration stage, that is, in step S2, a low-resolution point cloud is used to quickly align the overall pose to eliminate mismatched points. Then in the fine registration stage, that is, step S3, it switches to high-resolution point clouds to refine local feature alignment. The final output registration result will be displayed through a visual interface, and users can manually fine-tune the alignment accuracy of key areas to ensure the reliability of subsequent trajectory generation.

[0021] Among them, the local feature alignment of high-resolution point cloud refinement is to achieve accurate matching of key points and contour segmentation through the KD tree (K-Dimensional Tree) search algorithm. In the specific implementation, the FPFH feature library of the actual point cloud model obtained previously is compared with the FPFH feature library pre-calculated by the standard point cloud model. By constructing the KD tree index structure of the standard point cloud model, the system quickly searches for the nearest neighbor key point corresponding to each point in the actual point cloud. The matching formula is:

[0022] Among them, in the formula, The actual point cloud model Points The FPFH feature descriptor, The first Three-dimensional points, The first Points The FPFH feature descriptor, To find the corresponding point in all the standard point cloud candidate points Index with minimum Euclidean distance .

[0023] After the matching is completed, the system performs multi-contour segmentation based on the spatial distance between key points. For a set of matching key points that are continuously distributed in the actual point cloud, if the distance between adjacent key points exceeds the preset distance threshold, it is determined to be a boundary point of the contour segmentation. At the same time, the system dynamically adjusts the segmentation radius, giving priority to preserving the local continuity of high curvature areas (such as edges and transition fillets) to avoid mis-segmentation caused by uneven point cloud density. The segmentation results are displayed through a visual interface, and users can manually adjust the segmentation boundaries or merge adjacent areas to ensure the rationality of the polishing area division.

[0024] Preferably, in order to improve the reliability of segmentation, the system introduces a two-way verification mechanism: reverse search for the nearest neighbors of the actual point cloud key points in the standard point cloud model, and remove point pairs with inconsistent two-way matching. Overall, this stage combines automated segmentation with manual verification to achieve refined processing of complex contours and provide highly reliable input data for subsequent trajectory generation.

[0025] For the final generated segmented contour diagram, each segment is marked with a unique identifier, and during the parameter setting process, the user is supported to independently set the grinding depth for different segments. , Horizontal expansion width And the quadratic function feed angle of the chamfering required area , where the coefficient For user input, It is the final grinding trajectory used to control the smoothness of the feed trajectory and the cutting angle in the chamfered area to avoid stress concentration or surface scratches caused by linear feed.

[0026] The system then dynamically adjusts the trajectory offset according to the parameters entered by the user. (generated from the standard point cloud outer contour), normal offset trajectory With horizontal expansion trajectory Calculated by the following formulas: Where N is the normal direction of the point cloud, and T is the tangent direction of the edge. For the chamfered area, the system uses the quadratic function The trajectory interpolation algorithm is integrated to generate a continuously changing feed angle, ensuring the smooth movement of the grinding head in the transition area of ​​the curved surface.

[0027] In addition, during the parameter input process, the system will automatically detect the rationality of the parameters through a built-in verification mechanism: grinding depth Limited to a preset safety threshold (e.g. 0.1–2.0 mm), the quadratic function coefficient The trajectory curvature continuity condition must be met (such as non-mutation of the second-order derivative). If conflicting parameters are detected (such as the lateral expansion width exceeds the fixture avoidance space), the system prompts the user to make corrections in real time. After the configuration is completed, the initial trajectory is previewed in the form of a three-dimensional path, which supports the user to manually fine-tune the key point position and generate parameterized initial trajectory data that adapts to multi-segment contour features, providing an input basis for subsequent optimization and obstacle avoidance planning.

[0028] Based on the obtained initial trajectory, the system smoothes the initial trajectory through the B-spline interpolation algorithm, and combines the kinematic constraints of the industrial robot with the Rapidly-exploring Random TreeStar algorithm (RRT*) to generate a local obstacle avoidance path. Traditional polishing trajectory planning methods usually rely on fixed path templates or simple linear interpolation, which are difficult to adapt to the smoothness requirements of complex contours and lack the ability to effectively avoid dynamic interference. In response to these problems, the present invention introduces the B-spline interpolation algorithm to optimize the initial trajectory. In the specific implementation, the system interpolates and reconstructs the discrete points of the initial trajectory based on the B-spline basis function, and its mathematical expression is: in, is a parameter variable, is the optimized initial trajectory, for The order is B-spline basis functions, is the control point in the initial trajectory that is dynamically adjusted according to the simulation results of the industrial robot kinematic model. is the total number of control points. The system optimizes the distribution of control points according to the robot kinematic model (such as joint acceleration and speed limit) to ensure that the interpolated trajectory meets the requirements of motion smoothness and accessibility, and avoids sudden changes in the trajectory through curvature continuity detection. Compared with traditional linear interpolation, B-spline interpolation can significantly reduce trajectory jitter and improve the motion stability of the grinding head on complex surfaces.

[0029] Furthermore, in order to deal with the interference risk in complex tooling environments, the system constructs a geometric bounding box model of the robot, fixture and workpiece, and verifies the feasibility of the trajectory pose based on the inverse kinematic solution. If potential interference is detected (such as a collision between the grinding head and the fixture), the system triggers the RRT* (Rapidly Expanding Random Tree Star) algorithm to generate a local obstacle avoidance path. The RRT algorithm is a path planning method based on random sampling. It gradually optimizes the path quality by randomly generating nodes in the configuration space and constructing a tree-like path network. Compared with the traditional RRT algorithm, RRT* introduces a rewiring mechanism that can continuously optimize the path cost (such as path length, energy consumption) during the expansion process, thereby generating a more efficient obstacle avoidance path. In specific implementation, the system takes the minimum energy consumption as the optimization goal, selects the optimal detour path, and smoothly connects the obstacle avoidance path with the original trajectory through a cubic spline curve to ensure that there is no jitter in the transition area.

[0030] Considering that traditional grinding systems often rely on rigid execution of preset trajectories, it is difficult to cope with pressure fluctuations caused by uneven thickness of casting burrs or surface deformation, which can easily lead to over-grinding or under-grinding defects. In view of this, after the optimized grinding trajectory is transmitted to the execution terminal through the industrial robot control interface, the system will parse the trajectory data based on the robot kinematic model, convert it into servo motor instructions for each joint, and synchronously plan the posture adjustment in the tool coordinate system (such as grinding head inclination compensation). During execution, the floating spindle at the end of the robot is equipped with a high-frequency force sensor (sampling rate ≥ 1kHz) to monitor the normal contact pressure in real time. The system dynamically corrects the track depth based on the pressure difference feedback from the force sensor. The core formula is: in, is the adjusted track depth, is the original track depth, is the scaling factor (dynamically calibrated by an adaptive algorithm, such as gradient descent optimization based on historical pressure data), is the reference pressure (set according to material hardness and process requirements). Continue to be below When the system determines that the burr thickness exceeds the expected value, press Gain gradually increases the grinding depth , otherwise the depth is reduced to avoid overcutting. During the adjustment process, the depth correction amount is smoothed by a low-pass filter to prevent trajectory jitter caused by instantaneous pressure fluctuations.

[0031] In the preferred embodiment, anomaly detection and fault-tolerant processing are also introduced. If the pressure deviation continues to exceed the threshold, an emergency pause is triggered, and the 3D visual re-inspection module is started to rescan the point cloud data of the abnormal area and update the local trajectory. At the same time, the system records the pressure-time curve and depth correction log to provide data support for process optimization.

[0032] The present invention also includes a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the trajectory generation method for deburring a casting blank are implemented.

[0033] Also included is a device for processing data, comprising: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the steps of the trajectory generation method for deburring a casting blank.

[0034] In summary, the trajectory generation method for deburring casting blanks proposed in the present invention realizes key point matching and segmentation of the casting outer contour based on FPFH features and KD tree search, and supports differentiated trajectory generation for different areas (such as chamfers and burr concentration areas) combined with the polishing parameters input by the user, which significantly improves the process adaptability of complex contours and the ability to respond to personalized needs.

[0035] Through the improved iterative closest point algorithm (ICP) combined with FPFH feature descriptor screening and random sampling consistency (RANSAC) mismatch elimination, the registration accuracy of the actual point cloud and the standard model is significantly improved, the coordinate system alignment error caused by casting deformation is effectively reduced, and excessive grinding or equipment collision caused by model deviation is avoided.

[0036] The initial trajectory is smoothed by B-spline interpolation to reduce robot motion jitter; the kinematic model of the industrial robot is combined with the rapidly expanding random tree star algorithm to dynamically generate a local obstacle avoidance path to ensure real-time avoidance of the fixture and workpiece interference area during the grinding process, taking into account both processing efficiency and safety. At the same time, the trajectory depth is dynamically corrected based on the pressure difference feedback from the force sensor to achieve closed-loop control of the grinding force, effectively dealing with uneven burr thickness or undulating casting surface, and ensuring processing stability and finished product consistency.

[0037] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0038] In addition, in the present invention, descriptions such as "first", "second", "one", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0039] In the present invention, unless otherwise clearly specified and limited, the terms "connection", "fixation", etc. should be understood in a broad sense. For example, "fixation" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0040] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

Claims

1. A trajectory generation method for deburring a casting blank, characterized in that: Includes steps: S1: Scan the casting blank with a 3D linear array camera to obtain the actual point cloud model and extract the pre-recorded standard point cloud model; S2: Based on the improved iterative closest point algorithm, the actual point cloud model and the standard point cloud model are aligned under the coordinate system registration adjustment; S3: Based on the FPFH feature descriptors of the outer contours of the actual point cloud model and the standard point cloud model, the key points matching the standard point cloud model are searched through the KD tree, and the multi-contour segmentation of the actual point cloud model is performed according to the distance between the key points; S4: receiving polishing parameters input by the user based on the segmentation results of each contour, and generating an initial trajectory according to the polishing parameters; S5: The initial trajectory is smoothed by B-spline interpolation, and the local obstacle avoidance path is generated by combining the industrial robot kinematic model and the fast expansion random tree star algorithm; S6: Output the final grinding track to the control terminal and adjust the track depth in real time according to the feedback from the force sensor.

2. A trajectory generation method for deburring a casting blank according to claim 1, characterized in that: In the step S2, the improved iterative closest point algorithm is specifically: S21: extract the FPFH feature descriptors of the outer contours of the actual point cloud model and the standard point cloud model and select the initial pairing points; S22: The mismatched points are eliminated through the random sampling consistency algorithm, and the actual point cloud model and the standard point cloud model are aligned with the coordinate system through the following formula: In the formula, is the rotation matrix, is the translation vector, is the total amount of 3D points in the point cloud model, The actual point cloud model Three-dimensional points, The first 3D points; S23: Iterative optimization of rotation matrix and translation vectors , until the error of the registration adjustment is less than the preset error threshold.

3. A trajectory generation method for deburring a casting blank as claimed in claim 2, characterized in that: In the step S3, the key points of the standard point cloud model searched by the KD tree are expressed as follows: In the formula, The actual point cloud model Points The FPFH feature descriptor, The first Three-dimensional points, The first Points The FPFH feature descriptor, To find the corresponding point in all the standard point cloud candidate points Index with minimum Euclidean distance .

4. A trajectory generation method for deburring a casting blank according to claim 1, characterized in that: In the step S4, the grinding parameters include grinding depth , Horizontal expansion width The quadratic function feed angle of the chamfering required area ,in, For user input coefficients, Polish the track for the final touch.

5. A trajectory generation method for deburring a casting blank as claimed in claim 4, characterized in that: The initial trajectory is expressed as the following formula: In the formula, is the normal offset trajectory, For horizontal expansion trajectory, is the reference trajectory obtained based on the standard point cloud model, N is the point cloud normal direction, and T is the edge tangent direction.

6. A trajectory generation method for deburring a casting blank according to claim 1, characterized in that: In the step S5, the smoothing of discrete trajectory points of the initial trajectory by B-spline interpolation is expressed as the following formula: In the formula, is a parameter variable, is the optimized initial trajectory, for The order is B-spline basis functions, is the control point in the initial trajectory that is dynamically adjusted according to the simulation results of the industrial robot kinematic model. is the total number of control points.

7. A trajectory generation method for deburring a casting blank according to claim 1, characterized in that: In the step S5, the generation of the local obstacle avoidance path by combining the industrial robot kinematics model and the RRT* algorithm is specifically as follows: Construct geometric bounding box models of robots, fixtures, and workpieces; Verify the feasibility of trajectory pose through inverse kinematics solution; If interference is detected, a fast expanding random tree star algorithm is used to generate a local obstacle avoidance path.

8. A trajectory generation method for deburring a casting blank as claimed in claim 1, characterized in that: In the step S6, the real-time adjustment of the track depth according to the feedback of the force sensor is expressed as the following formula: In the formula, is the adjusted track depth, is the original track depth, is the proportionality coefficient, is the base pressure, is the real-time pressure of the force sensor.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of a trajectory generation method for deburring a casting blank as described in any one of claims 1 to 8 are implemented.

10. A device for processing data, characterized in that: include: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the steps of a trajectory generation method for deburring a casting blank as described in any one of claims 1 to 8.

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