Robot autonomous carving operation method and system for complex curved surface

Abstract

The invention discloses a robot autonomous carving operation method and system for a complex curved surface. The method comprises the steps that S1, a character pattern segmentation method is designed, and a letter segmentation information base is constructed; constructing a sub-skill primitive library; generating a plane reference trajectory by adopting a discrete dynamic motion primitive model; s2, a 3D camera is adopted to obtain point cloud of an interested complex curved surface, eye calibration outside the hand is conducted on the camera, the transformation relation from a camera coordinate system to a mechanical arm base coordinate system is obtained, and a curved surface reference track and a surface normal vector are obtained; s3, parameter identification and gravity compensation are conducted on the mechanical arm tail end carving tool based on the six-dimensional force sensor; and S4, a self-adaptive admittance controller based on the radial basis function neural network is adopted to generate an expected engraving track, so that the interaction force of the environment and the mechanical arm tracks the expected interaction force, and the engraving operation of the mechanical arm is realized. The robot can conduct carving operation on a complex curved surface, the carving precision is high, the carving effect is good, and practicability is high.

Description

Technical Field

[0001] The invention belongs to the technical field of robots, and in particular relates to a robot autonomous engraving operation method and system for complex curved surfaces. Background Art

[0002] In recent years, robotics technology has developed rapidly. Industrial robots are increasingly used in industrial manufacturing fields, such as engraving, welding, assembly, and grinding, due to their advantages such as reliable stability, high repeatability, and the ability to operate in high-risk environments. Robot engraving operations usually require teaching to perform new tasks, while traditional teaching technology has low programming efficiency, strong professionalism, and can only perform specific tasks. It cannot meet the customization and efficiency requirements of engraving operations. Therefore, robots must have more efficient skill generalization capabilities to quickly adapt to complex environments and different task requirements, thereby continuously improving their intelligence level.

[0003] Carving on complex surfaces requires ensuring the robot's carving accuracy and effect, and places high demands on the robot's force controller. In the patent, engraving control method, device, engraving equipment and storage medium CN116330260A, a traditional PID controller is used to correct the current trajectory of the engraving robot to achieve constant force engraving. There are also many documents that use classic fixed-parameter admittance controllers to achieve constant force control of robot engraving or polishing. However, when the engraving robot encounters different surface shapes and materials with different stiffness, it is necessary to manually adjust the PID gain or admittance parameters according to changes in the working environment, which not only affects the efficiency of the engraving operation, but also the controller has no adaptive parameter adjustment, which will greatly reduce the engraving accuracy on complex surfaces, resulting in poor engraving effects. Summary of the invention

[0004] The main purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art and to provide a robot autonomous engraving method and system for complex curved surfaces.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A robot autonomous engraving method for complex curved surfaces comprises the following steps:

[0007] S1. Design a glyph segmentation method for English letters and build a letter segmentation information database; construct a teaching sub-trajectory for each glyph category and build a sub-skill primitive library; use discrete dynamic motion primitive models DMPs to generate planar reference trajectories;

[0008] S2. Use a 3D camera to obtain point cloud information of the complex surface of interest, and calibrate the camera with the eye outside the hand to obtain the transformation relationship from the camera coordinate system to the robot base coordinate system. According to the transformation relationship, the surface point cloud in the robot base coordinate system is obtained. Based on the surface trajectory planning method of the wrapping projection algorithm, the generated plane reference trajectory is projected onto the surface to obtain the surface reference trajectory and surface normal vector;

[0009] S3, perform parameter identification on the end-carving tool of the robotic arm based on the six-dimensional force sensor, perform gravity compensation on the end-carving tool of the robotic arm, eliminate the influence of gravity of the end-carving tool, and obtain the interaction force between the external environment and the robotic arm;

[0010] S4. The robot arm adopts an adaptive admittance controller based on radial basis neural network RBFNN to generate the desired engraving trajectory, and at the same time makes the interaction force between the environment and the robot arm track the expected interaction force, so as to realize the smooth engraving operation of the robot arm.

[0011] The present invention also includes a robot autonomous engraving operation system for complex curved surfaces, the system adopts the robot autonomous engraving operation method provided by the present invention, and the system includes a plane reference trajectory generation module, a surface reference trajectory generation and surface normal vector generation module, a parameter identification and gravity compensation module, and an adaptive admittance control module;

[0012] Generate a plane reference trajectory module, which is used to segment all English letters and build a letter segmentation information library containing all letter segmentation information; construct a teaching sub-trajectory for each letter category to build a sub-skill primitive library; use discrete dynamic motion primitive models DMPs to generate plane reference trajectories;

[0013] Generate surface reference trajectory and surface normal vector module, use 3D camera to obtain point cloud information of complex surface of interest, and calibrate the camera with eye outside hand to obtain the transformation relationship from camera coordinate system to robot base coordinate system, obtain surface point cloud in robot base coordinate system according to the transformation relationship, and project the generated plane reference trajectory onto the surface based on the surface trajectory planning method of wrapping projection algorithm to obtain surface reference trajectory and surface normal vector;

[0014] The parameter identification and gravity compensation module is used to identify the parameters of the engraving tool at the end of the robot arm based on the six-dimensional force sensor, perform gravity compensation on the engraving tool at the end of the robot arm, and obtain the interaction force between the external environment and the robot arm;

[0015] The adaptive admittance control module adopts an adaptive admittance controller based on radial basis function neural network (RBFNN) to generate the desired engraving trajectory, and at the same time makes the interaction force between the environment and the robot arm track the desired interaction force, thus realizing the smooth engraving operation of the robot arm.

[0016] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0017] 1. The present invention adopts a method of generating a plane reference trajectory using any given English letters. When different letters or reference trajectories of different positions and sizes need to be generated each time, the re-teaching and planning of the traditional teaching method is avoided, which greatly reduces the workload and operation difficulty. At the same time, the robot is given a more efficient skill generalization ability to adapt to the changing engraving operation requirements.

[0018] 2. The present invention adopts a method of generating surface reference trajectory and surface normal vector, projects the plane reference trajectory onto a complex surface, and expands the plane engraving operation to the curved surface, which greatly enriches the environment categories of robot engraving operations and adapts to complex engraving operation surfaces.

[0019] 3. The present invention adopts parameter identification and gravity compensation to obtain the identification parameters of the end engraving tool and the six-dimensional force sensor of the robotic arm, and eliminates the influence of the gravity of the end engraving tool, thereby obtaining the interaction force between the external environment and the robotic arm.

[0020] 4. The present invention adopts an adaptive admittance controller to complete the task of the robot to perform flexible engraving operations. At the same time, it not only ensures the interaction safety between the robot and the environment, but also obtains good engraving accuracy and engraving effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a flow chart of the method of the present invention;

[0022] Figure 2 It is a schematic diagram of the glyph segmentation method;

[0023] Figure 3 It is a flow chart of the surface trajectory planning method based on the wrapping projection algorithm;

[0024] Figure 4 is the damping parameter learning and convergence curve;

[0025] Figure 5 is the stiffness parameter learning and convergence curve;

[0026] Figure 6 It is the interaction force tracking expected interaction force curve between the environment and the robot;

[0027] Figure 7 It is a schematic diagram of the system of the present invention. DETAILED DESCRIPTION

[0028] The present invention is further described in detail below in conjunction with embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0029] Example

[0030] like Figure 1 As shown, the present invention provides a robot autonomous engraving method for complex curved surfaces, comprising the following steps:

[0031] S1. Design a glyph segmentation method for English letters and build a letter segmentation information library; construct a teaching sub-trajectory for each glyph category and build a sub-skill primitive library; use discrete dynamic motion primitive models DMPs to generate a plane reference trajectory; specifically include:

[0032] S11. For any letter, it can be composed of a specific combination of a finite number of sub-shapes, so all English letters are segmented; a letter segmentation information library containing all letter segmentation information is constructed, and the information library contains the glyph category and start and end points of each sub-track after each letter segmentation, and the constructed three-dimensional coordinates of the start and end points are all on the OXY plane of the robot base coordinate system; in this embodiment, Figure 2 As shown, any sub-shape after the letter segmentation is included in Figure 2 For a given letter to be engraved, the segmentation information of the given letter (including the glyph category and the starting and ending points of each sub-track after segmentation) is read from the letter segmentation information library.

[0033] S12, construct a teaching sub-track for each glyph category, the teaching sub-track describes the three-dimensional coordinate point set of the shape of the glyph category to which it belongs, all teaching sub-tracks constitute a sub-skill primitive library, and the three-dimensional coordinate point set of the constructed teaching sub-tracks is on the OXY plane of the robot base coordinate system. In this embodiment, a teaching sub-track is constructed for each of the nine categories, and these nine teaching sub-tracks constitute a sub-skill primitive library.

[0034] According to the glyph category of the sub-trajectory of a given letter to be engraved read from the letter information library, the corresponding teaching sub-trajectory is extracted from the sub-skill primitive library for subsequent trajectory generalization.

[0035] S13, using discrete dynamic motion primitive models DMPs to generate a planar reference trajectory, specifically:

[0036] For a given letter to be engraved and a specified position and size, a discrete dynamic motion primitive model DMPs is used to generate a planar reference trajectory. The model is expressed as:

[0037]

[0038] Among them, x is the system state (such as position coordinates or joint angles), x0 and x g They represent the initial value and target value of the motion trajectory respectively; α and β represent the system stiffness and damping respectively, and α=β is usually set 2 / 4 to ensure the stability of the system and make x converge to xg ; τ is the time scaling constant, and by changing this parameter, the trajectory can be generalized in the time dimension; s is a phase variable that decays exponentially, and γ and μ are defined positive constants; the nonlinear term f(s) is specifically:

[0039]

[0040] ψ i (s) = exp(-h i (sc i ) 2 ), i=1,2,…N

[0041] Among them, ψ i (s) represents a set of basis functions with Gaussian distribution, ω i is the corresponding weight, N is the number of basis functions, c i and h i They are respectively i (s) center point and width value;

[0042] The glyph category and starting and ending points of each sub-trajectory after segmentation of the given letter to be engraved read in step S11 and the teaching sub-trajectory extracted in step S12 are input into the discrete DMPs model to generalize and generate a plane reference sub-trajectory. The plane reference trajectory of the given letter is obtained by splicing and combining all the reference sub-trajectories, and performing translation and scaling operations according to the specified position and size.

[0043] In this embodiment, the parameters of the discrete DMPs model can be selected as shown in Table 1 below.

[0044]

[0045] Table 1

[0046] S2. Use a 3D camera to obtain point cloud information of the complex surface of interest, and calibrate the camera with the eye outside the hand to obtain the transformation relationship from the camera coordinate system to the robot base coordinate system. According to the transformation relationship, the surface point cloud in the robot base coordinate system is obtained. Based on the surface trajectory planning method of the wrapping projection algorithm, the generated plane reference trajectory is projected onto the surface to obtain the surface reference trajectory and surface normal vector;

[0047] In this embodiment, it specifically includes:

[0048] S21. Select a high-precision RGBD depth camera and a 8×6×25mm chessboard calibration plate. Fix the camera directly above the engraving platform and shoot the platform from above. Fix the calibration plate directly below the camera. Use the robot hand-eye calibration method with eyes on hands to read the camera's internal parameters, take a frontal and clear chessboard picture, call the OpenCV library for processing, and obtain the external parameter matrix, that is, the transformation relationship from the calibration plate coordinate system to the camera coordinate system.

[0049] S22, control the robot end engraving tool to move to the origin of the calibration plate, a point on the X axis and a point on the Y axis respectively, and record the coordinates of these three points in the base coordinate system, and calculate the position and posture of the calibration plate coordinate system in the robot arm base coordinate system based on these three coordinates Then calculate the transformation relationship from the camera coordinate system to the base coordinate system:

[0050]

[0051] S23. Clamp the material with complex surface to be engraved with a bench vise, and then fix the bench vise on the engraving platform and directly below the camera. Enable the camera to shoot depth map function, obtain point cloud data of the complex surface of interest, and then convert it from the camera coordinate system to the base coordinate system to obtain the surface point cloud in the base coordinate system.

[0052] S24. Using a surface trajectory planning method based on a wrapping projection algorithm, the generated plane reference trajectory is projected onto the surface to obtain a surface reference trajectory and a surface normal vector.

[0053] like Figure 3 As shown, step S24 is specifically as follows:

[0054] The target surface point cloud after transformation is denoted as S, and the plane trajectory point cloud is denoted as C = {c1, c2, …, c m};

[0055] Select a starting reference projection point c0 in the plane and specify a reference projection vector pointing to the target surface. Project c0 along the reference vector Project on the target surface to get d0;

[0056] Search for k neighboring points of d0 in S, and use the least squares method to fit the tangent plane H0 and normal vector at d0 according to and Calculate the rotation matrix R0 of the two;

[0057] Then project the m plane trajectory points separately to a certain point c i , according to the preset projection step l, the line segment c0ci Divided into several segments, when c0c i ≤l, there is no intermediate point; when c0c i >l, the set of intermediate points is recorded as:

[0058] {c i,j |j=1,…,q i}

[0059]

[0060] When c0c i >l, firstly the first segment c0c i,1 Projection is performed, and the plane line segment c0c is transformed using the above rotation matrix R0 i,1 Transform to the tangent plane H0 and get the middle projection point Then search in S k neighboring points of , and fit a new tangent plane H i,1 and the normal vector And according to and Calculate the new rotation matrix R i,1 .Pick On the tangent plane H i,1 The projection point d on i,1 As c i,1 The projection point on the target surface; then iteratively calculate the projection point of all intermediate points and the normal vector at that point until c is obtained i Projection point d on the target surface i and the normal vector at that point

[0061] When c0c i ≤l, directly to c0c i Projection point d is obtained by the above method i and the normal vector at that point

[0062] Take off a little c i+1 Repeat the above operation and finally get the surface trajectory point set D = {d1, d2, ..., d m} and the corresponding normal vector set

[0063] S3, perform parameter identification on the engraving tool at the end of the robotic arm based on the six-dimensional force sensor, perform gravity compensation on the engraving tool mounted on the end of the robotic arm, eliminate the influence of gravity of the end engraving tool, and obtain the interaction force between the external environment and the robotic arm;

[0064] In this embodiment, a six-axis Elite robot and an ATI-MINI45 six-dimensional force sensor are used. The force sensor is installed at the end of the robot, and then the engraving tool is installed on the force sensor. The engraving tool includes a flange, an engraving motor, and an engraving tool head. Ensure that the Z axis at the end of the robot, the Z axis of the force sensor, and the center axis of the engraving tool are aligned. Step S3 specifically includes:

[0065] S31, change the posture of the engraving tool at the end of the robot arm, and obtain N (N≥3) groups of α under different postures i ,β i ,γ i Six-axis force sensor reading F xi, F yi ,F zi ,M xi ,M yi ,M zi , i=1,2,…,N, the force and torque have the following relationship:

[0066]

[0067] Among them, F x0 ,F y0 ,F z0 ,M x0 ,M y0 ,M z0 is the sensor zero value, F x ,F y ,F z ,M x ,M y ,M z is the sensor measurement value. After simplification, the least square method can be used to obtain the centroid coordinates (cx, cy, cz) and three constant values ​​k1, k2, k3.

[0068] The world coordinate system is O0X0Y0Z0, the base coordinate system is O1X1Y1Z1, and it is assumed that the robot arm has an installation inclination angle, that is, O0X0Y0Z0 is first rotated around the X axis by angle U, and then around the Y axis by angle V to obtain O1X1Y1Z1. The force sensor coordinate system is O2X2Y2Z2, which can be obtained by rotating the base coordinate system O1X1Y1Z1 around the Z1 axis by angle α, around the Y2 axis by angle β, and around the X2 axis by angle γ. According to the above N groups of posture data, the load gravity, force sensor force and zero point have the following relationship:

[0069]

[0070] Where G is the load gravity and I is the 3×3 unit matrix. After simplification, the force zero point F of the force sensor can be obtained by the least squares method. x0 ,F y0 ,F z0 and three constant values ​​Lx ,L y ,L z .

[0071] According to the above results, calculate the end engraving tool gravity G, installation inclination angle U, V and sensor torque zero point M x0 ,M y0 ,M z0 , completing the entire parameter identification process.

[0072] In this embodiment, N (N=17) groups of six-axis force sensor readings in different postures are shown in Table 2 below.

[0073]

[0074]

[0075] Table 2

[0076] S32, performing gravity compensation on the engraving tool at the end of the robot arm, specifically:

[0077] Get the current robot end posture matrix and force sensor reading F x ,F y ,F z ,M x ,M y ,M z , according to the identification parameters obtained in step S31, the real-time gravity compensation of the end engraving tool is realized, and the interaction force and torque between the external environment and the robotic arm are calculated:

[0078]

[0079] Among them, F ex 、F ey 、F ez are the interaction forces of the sensor X, Y, and Z axes, M ex 、M ex 、M ez are the interaction torques of the sensor X, Y, and Z axes, G x , G y , G z They are the end engraving tool gravity of the sensor X, Y, and Z axes respectively.

[0080] S4. The robot arm adopts an adaptive admittance controller based on radial basis neural network (RBFNN) to generate the desired engraving trajectory, and at the same time make the interaction force between the environment and the robot arm track the desired interaction force, so as to realize the smooth engraving operation of the robot arm;

[0081] In this embodiment, the robot arm adopts an adaptive admittance controller based on RBFNN, which is expressed as:

[0082]

[0083] Among them, B and K are damping parameters and stiffness parameters respectively, and F d represents the expected interaction force between the environment and the robot, F e Represents the actual interaction force between the environment and the robot, e = ΔX e It represents the compensation amount of the robot arm’s posture error caused by the force of the environment on the robot arm. Then it represents the rate of change of the posture error compensation, X e =X d -X r represents the pose error, X r represents the reference pose, i.e., the surface reference trajectory of step S24, X d represents the desired pose generated.

[0084] By optimizing the cost function Adaptively adjust the damping and stiffness parameters so that the actual interaction force between the environment and the robot arm tracks the expected interaction force; the damping and stiffness parameters are specifically expressed as:

[0085] B=w B T h(z)+ξ B

[0086] K=w K T h(z)+ξ K

[0087] Where z = F d -F e is the RBFNN input, w B ,w K represents the weight vector, n is the number of radial basis functions, ξ B ,ξ K represents the approximate error of the neural network, and h(z) represents the vector composed of Gaussian functions;

[0088] Use the gradient descent method to get the weight update rate of the cost function J

[0089]

[0090] in, Denote the weighted estimates of damping and stiffness, η B ,η K denote the learning rates of damping and stiffness, respectively, and are defined as positive constants;

[0091] Calculate the damping and stiffness parameters of the adaptive admittance controller, and the weight w after iterative trainingB ,w K The learning is obtained, and finally the damping and stiffness parameters B and K converge. In this embodiment, the initial weight is set to w B =w K =0, weight learning rate η B =1,η K =100, the initial impedance parameters are set to B = 10N·s / mm, K = 400N / mm. The desired interaction force is selected as a constant value F d =5N. Figure 4 and Figure 5 As shown, after iterative training, the weight W B ,W K After learning, the damping and stiffness parameters B and K converge to appropriate values.

[0092] The surface reference trajectory obtained in step S24 and the interaction force between the external environment and the robot arm in step S32 are fed back to the RBFNN-based adaptive admittance controller to generate the desired engraving trajectory; the end pose trajectory is converted to the joint space through the robot inverse kinematics, and the robot arm control is realized through the joint controller of the robot arm, such as Figure 6 As shown, the interaction force between the environment and the robot arm is made to track the expected interaction force, thereby achieving smooth engraving operation of the robot arm.

[0093] In another embodiment, a robot autonomous engraving system for complex curved surfaces is provided. The system adopts the robot autonomous engraving method of the above embodiment. Figure 7 As shown, the system includes a module for generating a plane reference trajectory, a module for generating a curved surface reference trajectory and a surface normal vector, a module for parameter identification and gravity compensation, and a module for adaptive admittance control;

[0094] Generate a plane reference trajectory module, which is used to segment all English letters and build a letter segmentation information library containing all letter segmentation information; construct a teaching sub-trajectory for each letter category to build a sub-skill primitive library; use discrete dynamic motion primitive models DMPs to generate plane reference trajectories;

[0095] Generate surface reference trajectory and surface normal vector module, use 3D camera to obtain point cloud information of complex surface of interest, and calibrate the camera with eye outside hand to obtain the transformation relationship from camera coordinate system to robot base coordinate system, obtain surface point cloud in robot base coordinate system according to the transformation relationship, and project the generated plane reference trajectory onto the surface based on the surface trajectory planning method of wrapping projection algorithm to obtain surface reference trajectory and surface normal vector;

[0096] The parameter identification and gravity compensation module is used to identify the parameters of the engraving tool at the end of the robot arm based on the six-dimensional force sensor, perform gravity compensation on the engraving tool at the end of the robot arm, eliminate the influence of gravity of the engraving tool at the end of the robot arm, and obtain the interaction force between the external environment and the robot arm;

[0097] The adaptive admittance control module adopts an adaptive admittance controller based on radial basis function neural network (RBFNN) to generate the desired engraving trajectory, and at the same time makes the interaction force between the environment and the robot arm track the desired interaction force, thus realizing the smooth engraving operation of the robot arm.

[0098] The present invention provides a method and system for autonomous robot engraving operations on complex surfaces. When it is necessary to generate reference trajectories of different letters or different positions and sizes each time, the re-teaching and planning of the traditional teaching method are avoided, which greatly reduces the workload and operation difficulty, and at the same time gives the robot a more efficient skill generalization ability to adapt to the changing engraving operation requirements; the present invention projects a plane reference trajectory onto a complex curved surface, expands the plane engraving operation to the curved surface, greatly enriches the environment category of the robot engraving operation, and adapts to complex engraving operation surfaces; the present invention obtains the identification parameters of the end load of the robot arm and the six-dimensional force sensor, and eliminates the influence of the gravity of the end tool, thereby obtaining the interaction force between the external environment and the robot arm; the present invention completes the task of the robot performing a compliant engraving operation, and at the same time not only ensures the interactive safety of the robot and the environment, but also obtains good engraving accuracy and engraving effect.

[0099] It should also be noted that in this specification, terms such as "comprises", "includes" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of more restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0100] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A robot autonomous engraving method for complex surfaces. It is characterized in that The following steps are involved: S1. Design a glyph segmentation method for English letters and build a letter segmentation information database; construct a teaching sub-trajectory for each glyph category and build a sub-skill primitive library; use discrete dynamic motion primitive models DMPs to generate planar reference trajectories; S2. Use a 3D camera to obtain point cloud information of the complex surface of interest, and calibrate the camera with the eye outside the hand to obtain the transformation relationship from the camera coordinate system to the robot base coordinate system. According to the transformation relationship, the surface point cloud in the robot base coordinate system is obtained. Based on the surface trajectory planning method of the wrapping projection algorithm, the generated plane reference trajectory is projected onto the surface to obtain the surface reference trajectory and surface normal vector; S3, perform parameter identification on the end-carving tool of the robotic arm based on the six-dimensional force sensor, perform gravity compensation on the end-carving tool of the robotic arm, eliminate the influence of gravity of the end-carving tool, and obtain the interaction force between the external environment and the robotic arm; S4. The robot arm adopts an adaptive admittance controller based on radial basis neural network RBFNN to generate the desired engraving trajectory, and at the same time makes the interaction force between the environment and the robot arm track the expected interaction force, so as to realize the smooth engraving operation of the robot arm.

2. A robot autonomous engraving method for complex curved surfaces according to claim 1, It is characterized in that In step S1, a method for segmenting the English letters is designed and a letter segmentation information library is constructed, specifically: Perform glyph segmentation on all English letters and build a letter segmentation information library containing all letter segmentation information. The information library contains the glyph category and start and end points of each sub-track after each letter segmentation; for a given letter to be engraved, read the segmentation information of the given letter from the letter segmentation information library.

3. A robot autonomous engraving method for complex curved surfaces according to claim 2, It is characterized in that In step S1, a teaching sub-track is constructed for each glyph category to form a sub-skill primitive library, specifically: A teaching sub-track is constructed for each glyph category, the teaching sub-track describes the three-dimensional coordinate point set of the shape of the glyph category to which it belongs, and all teaching sub-tracks constitute a sub-skill primitive library; According to the glyph category of the sub-track of a given letter to be engraved read from the letter information library, the corresponding teaching sub-track is extracted from the sub-skill primitive library.

4. The method for autonomous robot engraving of complex curved surfaces according to claim 3, It is characterized in that In step S1, a discrete dynamic motion primitive model DMPs is used to generate a planar reference trajectory, specifically: For a given letter to be engraved and a specified position and size, a discrete dynamic motion primitive model DMPs is used to generate a planar reference trajectory. The model is expressed as: Among them, x is the system state, x 0 and x g They represent the initial value and target value of the motion trajectory respectively; α and β represent the system stiffness and damping respectively; τ is the time scaling constant; s is a phase variable that decays exponentially, γ and μ are defined positive constants; the nonlinear term f(s) is specifically: ψ i (s)=exp(-h i (s-c i ) 2 ),i=1,2,…N Among them, ψ i (s) represents a set of basis functions with Gaussian distribution, ω i is the corresponding weight, N is the number of basis functions, x i and h i They are respectively i (s) center point and width value; The glyph category and starting and ending points of each sub-trajectory after segmentation of the given letter to be engraved and the extracted teaching sub-trajectory are input into the discrete DMPs model to generalize and generate a plane reference sub-trajectory. The plane reference trajectory of the given letter is obtained by splicing and combining all the reference sub-trajectories, and performing translation and scaling operations according to the specified position and size.

5. The method for autonomous robot engraving of complex curved surfaces according to claim 1, It is characterized in that Step S2 specifically includes: S21. Use the robot camera installation method of eye on hand and the robot hand-eye calibration method to calibrate the camera's external parameters and obtain the external parameter matrix, that is, the transformation relationship from the calibration plate coordinate system to the camera coordinate system. S22, according to the position and posture of the calibration plate coordinate system in the robot base coordinate system Calculate the transformation relationship from the camera coordinate system to the robot base coordinate system: S23, converting the point cloud data of the complex surface of interest acquired from the 3D camera from the camera coordinate system to the robot base coordinate system to obtain the surface point cloud in the robot base coordinate system; S24, using a surface trajectory planning method based on a wrapping projection algorithm, projecting the generated plane reference trajectory onto the surface to obtain the surface reference trajectory and the surface normal vector; the method is specifically as follows: The target surface point cloud after transformation is denoted as S, and the plane trajectory point cloud is denoted as C = {c 1 , c 2 , ..., c m }; Choose any starting reference projection point c in the plane 0 , specifies a reference projection vector pointing in the direction of the target surface c 0 Along the reference projection vector Projecting onto the target surface yields d 0 ; Search for d in S 0 k neighboring points of , and use the least squares method to fit d 0 The tangent plane H 0 and the normal vector according to and Calculate the rotation matrix R of the two 0 ; Project the m plane trajectory points separately to a certain point c i , according to the preset projection step l, the line segment c 0 c i Divided into several segments, when c 0 c i When ≤l, there is no middle point; when c 0 c i >l, the set of intermediate points is recorded as: {c i，j |j=1,...,q i } When c 0 c i >l, firstly, the first segment c o c i，1 For projection, use the rotation matrix R 0 The plane line segment c 0 c i，1 Transform to tangent plane H 0 On, get the middle projection point Search in S k neighboring points of , and fit a new tangent plane H i，1 and the normal vector And according to and Calculate the new rotation matrix R i，1 ;Pick On the tangent plane H i，1 The projection point d on i，1 As c i，1 The projection point on the target surface; then iteratively calculate the projection point of all intermediate points and the normal vector at that point until c is obtained i Projection point d on the target surface i and the normal vector at that point When c 0 c i ≤l, directly to c 0 c i Projection point d is obtained by the above method i and the normal vector at that point Take off a little c i+1 Repeat the above operation and finally get the surface trajectory point set D = {d 1 , d 2 , ..., d m } and the corresponding normal vector set 6. The method for autonomous robot engraving of complex curved surfaces according to claim 5, It is characterized in that In step S3, the parameters of the engraving tool at the end of the robot arm based on the six-dimensional force sensor are identified, specifically: Change the posture of the engraving tool at the end of the robot arm to obtain N groups of α under different postures i , β i , γ i Six-axis force sensor reading F xi , F yi , F zi , M xi , M yi , M zi , i=1, 2, ..., N, N≥3, the relationship between force and moment is: Among them, F x0 , F y0 , F z0 , M x0 , M y0 , M z0 is the sensor zero value, F x , F y , F z , M x , M y , M z is the sensor measurement value; after simplification, the least square method is used to obtain the centroid coordinates (cx, cy, cz) of the end engraving tool and the three constant values ​​k 1 , k 2 , k 3 ; The world coordinate system is denoted as O 0 X 0 Y 0 Z 0 , the base coordinate system is O 1 X 1 Y 1 Z 1 , assuming that the robot arm has an installation inclination angle, that is, O 0 X 0 Y 0 Z 0 First rotate angle U around the X axis, then rotate angle V around the Y axis to get O 1 X 1 Y 1 Z 1 ; The force sensor coordinate system is O 2 X 2 Y 2 Z 2 From the base coordinate system O 1 X 1 Y 1 Z 1 Around z 1 Axis rotation angle α, around Y 2 Axis rotation angle β, around X 2 The axis is rotated by an angle γ to obtain; According to N sets of posture data, the relationship between load gravity, force sensor force and zero point is: Where G is the load gravity, I is the 3×3 identity matrix, and L x , L y , L z is a constant; after simplification, the force zero point F of the force sensor is obtained by the least square method x0 , F y0 , F z0 and three constant values ​​L x , L y , L z ; According to the above results, calculate the end engraving tool gravity G, installation inclination angle U, V and sensor torque zero point M x0 , M y0 , M z0 , completing the parameter identification process.

7. The method for autonomous robot engraving of complex curved surfaces according to claim 6, It is characterized in that In step S3, gravity compensation is performed on the engraving tool at the end of the robot arm, specifically: Get the current robot end posture matrix and force sensor reading F x , F y , F z , M x , M y , M z , according to the identification parameters obtained in the parameter identification step, the real-time gravity compensation of the end engraving tool is realized, and the interaction force and torque between the external environment and the robotic arm are calculated: Among them, F ex 、F ey 、F ez are the interaction forces of the sensor X, Y, and Z axes, M ex 、M ex 、M ez are the interaction torques of the sensor X, Y, and Z axes, G x , G y , G z They are the end tool gravity of the sensor X, Y, and X axes respectively.

8. The method for autonomous robot engraving of complex curved surfaces according to claim 7, It is characterized in that Step S4 is specifically as follows: The surface reference trajectory obtained in step S24 and the interaction force between the external environment and the robotic arm are fed back to the adaptive admittance controller based on the radial basis function neural network RBFNN to generate the desired engraving trajectory; the end posture trajectory is converted to the joint space through the robot inverse kinematics, and the robotic arm is controlled through the joint controller of the robotic arm, so that the interaction force between the environment and the robotic arm tracks the expected interaction force, thereby realizing the flexible engraving operation of the robotic arm.

9. The method for autonomous robot engraving of complex curved surfaces according to claim 5, It is characterized in that The adaptive admittance controller based on radial basis neural network RBFNN is expressed as: Among them, B and K are damping parameters and stiffness parameters respectively, and F d represents the expected interaction force between the environment and the robot, F e Represents the actual interaction force between the environment and the robot, e = ΔX e It represents the compensation amount of the robot arm’s posture error caused by the force of the environment on the robot arm. Then it represents the rate of change of the posture error compensation, X e =X d -X r represents the pose error, X r represents the reference pose, i.e., the surface reference trajectory of step S24, X d represents the desired pose generated; By optimizing the cost function Adaptively adjust the damping and stiffness parameters so that the actual interaction force between the environment and the robot arm tracks the expected interaction force; the damping and stiffness parameters are specifically expressed as: B=w B T h(z)+ξ B F=w K T h(z)+ξ K Where z = F d -F e is the RBFNN input, w B , w K represents the weight vector, n is the number of radial basis functions, ξ B ,ξ K represents the approximate error of the neural network, and h(z) represents the vector composed of Gaussian functions; Use the gradient descent method to get the weight update rate of the cost function J in, Represent the weight estimates of damping and stiffness, ηB, ηK represent the learning rates of damping and stiffness, respectively, and are defined as positive constants; Calculate the damping and stiffness parameters of the adaptive admittance controller, and the weight W after iterative training B , w K After learning, the damping and stiffness parameters B and K finally converge.

10. A robot autonomous engraving system for complex surfaces. It is characterized in that The system adopts the robot autonomous engraving operation method according to any one of claims 1 to 9, and the system includes a plane reference trajectory generation module, a surface reference trajectory and surface normal vector generation module, a parameter identification and gravity compensation module, and an adaptive admittance control module; Generate a plane reference trajectory module, which is used to segment all English letters and build a letter segmentation information library containing all letter segmentation information; construct a teaching sub-trajectory for each letter category to build a sub-skill primitive library; use discrete dynamic motion primitive models DMPs to generate plane reference trajectories; Generate surface reference trajectory and surface normal vector module, use 3D camera to obtain point cloud information of complex surface of interest, and calibrate the camera with eye outside hand to obtain the transformation relationship from camera coordinate system to robot base coordinate system, obtain surface point cloud in robot base coordinate system according to the transformation relationship, and project the generated plane reference trajectory onto the surface based on the surface trajectory planning method of wrapping projection algorithm to obtain surface reference trajectory and surface normal vector; The parameter identification and gravity compensation module is used to identify the parameters of the engraving tool at the end of the robot arm based on the six-dimensional force sensor, perform gravity compensation on the engraving tool at the end of the robot arm, and obtain the interaction force between the external environment and the robot arm; The adaptive admittance control module adopts an adaptive admittance controller based on radial basis function neural network (RBFNN) to generate the desired engraving trajectory, and at the same time makes the interaction force between the environment and the robot arm track the desired interaction force, thus realizing the smooth engraving operation of the robot arm.

Images