Industrial robot joint rigidity identification device and identification method

Abstract

The invention discloses an industrial robot joint rigidity identification device and method, solves the problem of how to improve the identification precision due to complex equipment in an existing robot rigidity identification method, and belongs to the technical field of robot rigidity identification. According to the industrial robot joint rigidity identification device, a monocular camera is installed at the tail end of an industrial robot, a load is connected to the tail end of the industrial robot through two pulleys in sequence through a rope, and the pulley located at the low point transmits force to a dynamometer; the identification method comprises the following steps: establishing a robot static stiffness model; screening a high-flexibility and low-rigidity measurement pose set {P1} of the robot according to a preset threshold value, and further selecting an optimal measurement pose set from {P1} in combination with the view field and the measurement precision of the monocular camera; under the optimal measurement pose, the identification device is used for measuring deformation, force and moment data of the tail end of the robot under the action of loading force, and the rigidity of each joint of the robot is identified by using the static rigidity model.

Description

Technical Field

[0001] The invention relates to an industrial robot joint stiffness identification device and an identification method, belonging to the technical field of industrial robot stiffness identification. Background Art

[0002] External forces during the processing cause irregular deviations in industrial robots, affecting the processing accuracy and surface quality of workpieces. The stiffness identification technology of industrial robots is constantly developing. Scholars use a variety of methods to model and identify the stiffness of industrial robots, and use this information to optimize the design of industrial robots and improve processing accuracy and performance.

[0003] During the stiffness identification process, the measurement technology of the end deformation significantly affects the accuracy of parameter identification. Currently, most industrial robots are equipped with vision systems to perform tasks such as detection and positioning. Compared with traditional measurement technologies such as laser trackers (LT), coordinate measuring machines (CMMs) and laser interferometers, vision-based identification methods are significantly superior due to their high cost-effectiveness, easy integration and no need for additional equipment installation. Although the CMM method is accurate, its small workspace limits its use. Although the laser tracker is technologically advanced, its expensive equipment cost and complex deployment process limit its widespread application. In addition, the laser tracker needs to measure multiple points in the same pose to calculate the torsional deformation, which prolongs the identification time. In contrast, camera technology and computing processing capabilities have made progress. Cameras can be seamlessly integrated into industrial robot systems as part of their perception and execution mechanisms without the need for external measurement equipment. Using this feature of the camera, the in-situ calibration of the joint stiffness of industrial robots can be achieved to ensure seamless deployment in industrial environments.

[0004] There are studies that use cameras to identify the stiffness of industrial robots. For example, a camera system is used to measure the stiffness of the joints of industrial robots, and the displacement of the end effector is calculated by monitoring the position changes of the markers. However, experiments show that this method is not accurate enough in capturing small elastic deformations, which indicates that despite the use of advanced vision systems, further optimization is needed to improve measurement accuracy. Summary of the invention

[0005] In order to solve the problem of how to improve the identification accuracy of existing robot stiffness identification methods due to the complexity of equipment, the present invention provides an industrial robot joint stiffness identification device and identification method based on vision and step-by-step posture optimization.

[0006] An industrial robot joint stiffness identification device of the present invention comprises a load, a rope, a dynamometer, a monocular camera, an ArUco marker and two pulleys;

[0007] The monocular camera is installed at the end of the industrial robot to measure the deformation of the end of the industrial robot before and after the loading force by photographing the ArUco marker;

[0008] The load is connected to the end of the industrial robot through a rope through a pulley at a high point and a pulley at a low point in sequence. The pulley at the low point transmits the force to the dynamometer, which measures the size and direction of the external load at the end of the industrial robot.

[0009] The present application also provides an identification method for an industrial robot joint stiffness identification device, the method comprising:

[0010] Establish static stiffness model of industrial robot;

[0011] According to the preset threshold, the measurement pose set {P1} of the industrial robot with high dexterity and low stiffness is screened, and the optimal measurement pose set {P2} is further selected from the screened measurement pose set {P1} by combining the field of view and measurement accuracy of the monocular camera;

[0012] Under the optimal measurement posture, the industrial robot joint stiffness identification device is used to measure the deformation, force and torque data of the industrial robot end under the action of the loading force. Based on the deformation, force and torque data corresponding to all optimal measurement postures, the stiffness of each joint of the industrial robot is identified using the static stiffness model of the industrial robot.

[0013] Preferably, the method for screening the high dexterity and low stiffness measurement pose set {P1} of the industrial robot according to the preset threshold value is:

[0014] A workspace R1 where optional postures of the industrial robot are selected according to a preset threshold value of dexterity, and a workspace R6 where optional postures of the industrial robot are selected according to a preset threshold value of stiffness;

[0015] Establish a preliminary optimization model:

[0016]

[0017] The industrial robot is a six-degree-of-freedom industrial robot, and θ2, θ3, and θ5 are respectively the 2nd, 3rd, and 5th joint angles of six joints arranged upward from the base of the industrial robot.

[0018] Preferably, the dexterity of the industrial robot is expressed by the dexterity index k d Determination, dexterity index k d for:

[0019]

[0020] Among them, k(J N ) is the Jacobian matrix condition number obtained based on the Frobenius norm of the Jacobian matrix.

[0021] Preferably, the stiffness is calculated by the global stiffness coefficient K s Determination, global stiffness coefficient K s for:

[0022]

[0023] Among them, C fd is the force-linear displacement flexibility matrix.

[0024] As a preferred method, combining the field of view and measurement accuracy of the monocular camera, the method of further selecting the optimal measurement pose set {P2} from the screened pose set is:

[0025] Establish a visual error optimization model:

[0026]

[0027] Wherein, P is the coordinates of the four corner points of the ArUco marker measured by the monocular camera; Ф() is the field of view imaging model of the monocular camera; Ψ is the visual measurement index, and ε is the threshold of the set visual measurement index;

[0028] Based on the measured pose set {P1}, the visual error optimization model is solved to obtain the optimal measured pose set {P2}.

[0029] Preferably, the field of view and measurement accuracy of the monocular camera are determined by a visual measurement index Ψ, which is:

[0030]

[0031] Among them, Δx and Δy are the deformations mapped to the image coordinate system in the direction perpendicular to the optical axis when measuring with a monocular camera, Δz is the deformation mapped to the image coordinate system along the optical axis when measuring with a monocular camera, and ω1 and ω2 are weight factors used to balance the measurement accuracy in different directions.

[0032] Preferably, based on the kinematic model of the industrial robot, and through a virtual joint method, it is assumed that the connecting rod is completely rigid and the deformation of the end is caused by the deformation of the joint, so as to establish a static stiffness model of the industrial robot.

[0033] As a preferred embodiment, the static stiffness model of the industrial robot is:

[0034]

[0035] Where F represents the external generalized force vector on the end of the industrial robot; J is the kinematic Jacobian matrix of the industrial robot in the current position; X is the deformation vector of the end of the industrial robot under force F.

[0036] Preferably, J is obtained according to the kinematic equation of the industrial robot, and the kinematic equation of the industrial robot is:

[0037]

[0038] in, is the homogeneous transformation matrix of the first joint coordinate system {1} relative to the robot base coordinate system, is the homogeneous transformation matrix of the second joint coordinate system {2} relative to the first joint coordinate system {1}, is the homogeneous transformation matrix of the third joint coordinate system {3} relative to the second joint coordinate system {2}, is the homogeneous transformation matrix of the fourth joint coordinate system {4} relative to the third joint coordinate system {3}, is the homogeneous transformation matrix of the fifth joint coordinate system {5} relative to the fourth joint coordinate system {4}, is the homogeneous transformation matrix of the first joint coordinate system {6} relative to the fifth joint coordinate system {5}, is the homogeneous transformation matrix of the sixth joint coordinate system {6} relative to the robot base coordinate system.

[0039] The beneficial effects of the present invention are as follows: the present invention proposes a stiffness identification method of "eyes on hands", which not only utilizes the core advantages of the camera, but also breaks through the traditional concept and does not regard it as a substitute for external devices such as laser trackers. By directly integrating the camera into the end of the industrial robot, the posture changes of the end can be accurately captured to achieve accurate stiffness identification. Not only does it improve the identification accuracy, but it also simplifies the system architecture and is easy to deploy in actual industrial environments. The present invention uses predetermined performance indicators to optimize the posture of the industrial robot to ensure the accuracy and repeatability of joint stiffness identification. The present invention improves the convenience of stiffness identification, especially the experimental method has the advantage of being able to identify joint parameters in situ, and the deployed industrial robot does not need to disassemble the wrist for measurement, which significantly increases the operability and applicability, and provides reliable technical support for the precision optimization of industrial robots. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of the stiffness identification device of the present invention;

[0041] Figure 2 Schematic diagram of the kinematic model of the industrial robot of the present invention;

[0042] Figure 3 This is a flow chart of the two-step method for optimizing the posture of an industrial robot according to the present invention;

[0043] Figure 4 This is a schematic diagram of the distribution of the optimal posture area of ​​the industrial robot of the present invention;

[0044] Figure 5 This is a comparison diagram of experimental errors before and after the optimization of the posture of the industrial robot of the present invention, where 1-load, 2-pulley, 3-steel cable, 4-dynamometer, 5-monocular camera, 6-ArUco marker, 7-industrial robot. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0047] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.

[0048] Build an industrial robot joint stiffness identification device, such as Figure 1 As shown, a monocular camera is installed at the end of the industrial robot to form an "eye on hand" visual system. The internal and external parameters of the monocular camera are calibrated, and the conversion relationship between the camera coordinate system and other coordinate systems in the industrial robot system is established. The device for stiffness identification of the industrial robot of this embodiment includes a load 1, a pulley 2, a rope 3, a dynamometer 4, a monocular camera, and an ArUco marker 6. The load 1 is connected to the end of the industrial robot 7 through the rope 3 in turn through the pulley 2 at the high point and the pulley 2 at the low point. The pulley 2 at the low point transmits the force to the dynamometer 4. The dynamometer 4 measures the size and direction of the external load 1 at the end of the industrial robot. The rope 3 can be a steel cable; the monocular camera is installed at the end of the industrial robot, and is used to measure the deformation of the end of the industrial robot before and after the loading force by shooting the ArUco marker 6.

[0049] In the process of industrial robot stiffness identification, the deformation and force data of the end of the industrial robot need to be expressed in the robot base coordinate system, which involves the conversion between multiple coordinate systems. First, the acquisition of the deformation data of the end of the industrial robot depends on the Aruco marker map observed by the camera, and the transformation of the camera pose before and after the force is obtained by calculation. The pose of the camera in the calibration plate coordinate system is known, and this information can be used to perform coordinate transformation to estimate the deformation of the end of the industrial robot in the robot base coordinate system. The homogeneous transformation from the end to the base is as follows:

[0050]

[0051] in, It is the hand-eye matrix from the robot end coordinate system to the camera coordinate system, which has been calibrated in advance; It is the transformation matrix from the camera to the calibration plate coordinate system, obtained by the PnP algorithm; It is the transformation matrix from the calibration plate coordinate system to the robot base coordinate system, which is fixed and unchanged.

[0052] The deformation of the end in the base coordinate system before and after the force is applied can be calculated by the following formula

[0053]

[0054] in and Represent the homogeneous transformation matrices before and after the force is applied.

[0055] Similarly, the measured forces are three-dimensional forces in the dynamometer coordinate system. First, these forces are converted to the robot end coordinate system, and then further converted to the robot base coordinate system through kinematic relationships to ensure the accuracy of the data. The coordinate system is defined as follows:

[0056] {E}: Robot end coordinate system

[0057] {F}: Coordinate system of the force application point

[0058] {D}: Dynamometer coordinate system

[0059] {P}: Pulley coordinate system

[0060] First, the force F3 at the force application point is calculated. Assuming that the tension on the steel cable is uniform, the tension F1, F2 and F3 in the two directions of the steel cable are the same. The calculation formula is as follows:

[0061]

[0062] Where f is the tension of the cable; F x ,F y and F z are the three-axis forces measured respectively. F is the origin of the force application point coordinate system, O D is the origin of the dynamometer coordinate system, O P is the origin of the pulley coordinate system, O E The origin of the robot's end coordinate system

[0063] At this time, F3 is the force in the dynamometer coordinate system, which is converted to the robot end coordinate system using the force coordinate system transformation.

[0064]

[0065] in is the rotation transformation matrix from the dynamometer coordinate system to the robot end coordinate system.

[0066] Get torque Next, through the force coordinate system transformation, the force and torque in the robot end coordinate system are converted to the robot base coordinate system. By repeating the above operation for all industrial robot postures, the corresponding force and torque data in all postures are obtained, providing a basis for further stiffness identification.

[0067] The industrial robot joint stiffness identification method of this embodiment includes:

[0068] Step 1: Establish the static stiffness model of the industrial robot:

[0069] This implementation selects the KUKA kr500 six-degree-of-freedom industrial robot as the research object, and uses the widely recognized Denavit-Hartenberg (DH) method to establish its kinematic model. This method describes the geometric relationship between joints and connecting rods in a systematic parameterized manner, providing an accurate mathematical model basis for subsequent stiffness analysis.

[0070] like Figure 2 As shown in the figure, a schematic diagram of the kinematic model of the KUKA kr500 industrial robot is shown, clearly marking the layout of each joint and link. The transformation of the industrial robot joint coordinate system {i-1} to the joint coordinate system {i} can be achieved using the homogeneous transformation matrix To express:

[0071]

[0072] Among them, Rot() represents rotation transformation; Trans() represents translation transformation.

[0073] z represents the rotation or translation around the z axis, x represents the rotation or translation around the x axis, and θ i is the connecting rod angle, d i Indicates the connecting rod distance, a i represents the connecting rod length, α i represents the connecting rod torsion angle;

[0074] The kinematic equations of the entire industrial robot can be constructed by continuous multiplication, and the complete transformation matrix from the robot base coordinate system to the robot end coordinate system is Given by:

[0075]

[0076] Among them, vector n, o, a represents the attitude vector, vector p represents the translation vector, n = [n x ,n y ,nz ],o=[o x ,o y ,o z ],a=[a x ,a y ,a z ],p=[p x ,p y ,p z ].

[0077] In the stiffness model of industrial robots, the connecting rods of industrial robots are usually made of rigid materials such as cast iron and high-strength steel, while the joints are transmission mechanisms such as motors and reducers. Under the action of external loads, the deformation of the joints is usually more significant than the deformation of the connecting rods. Based on this realistic consideration, in the static stiffness modeling, it is assumed that the connecting rods are completely rigid, and all the end deformations are caused by the deformation of the joints. The joint stiffness matrix K θ It can be expressed as:

[0078]

[0079] in represents the stiffness of joint i.

[0080] The static stiffness model of the industrial robot was established using the virtual joint method (VJM):

[0081] F=J -T K θ ·J -1 X

[0082] Among them, F represents the external generalized force vector of the end of the industrial robot; J is the industrial robot in the current position according to the transformation matrix The obtained kinematic Jacobian matrix; X is the deformation vector of the industrial robot end under force F.

[0083] The above formula involves the calculation of the inverse Jacobian matrix. When the industrial robot approaches a singularity, calculation errors will inevitably be introduced. In order to solve this problem, the static stiffness model of the industrial robot is deformed to obtain:

[0084]

[0085] Expanding it gives

[0086]

[0087] Among them: x1, x2 and x3 are the small displacements of the end; δ1, δ2 and δ3 are the small deflections of the end.

[0088] Step 2: According to the requirements of joint stiffness identification, the posture of the industrial robot is optimized through a two-step method. First, the measurement posture set {P1} with high dexterity and low stiffness is selected. Then, the optimal measurement posture set {P2} is further selected from the selected measurement posture set {P1};

[0089] In order to accurately evaluate and optimize the posture of industrial robots, a motion performance index, also known as the dexterity index, is used, defined as k d This is a scalar based on the Jacobian matrix condition number that quantifies the distance to singular points and is used to measure the performance of industrial robot systems in terms of force and motion transmission. The dexterity index is defined as follows

[0090]

[0091] Where k(J N ) is the Jacobian matrix condition number obtained based on the Frobenius norm of the Jacobian matrix, and its calculation formula is as follows:

[0092]

[0093] Among them, J N The subscript N indicates the normalization operation, and tr() indicates the trace of the matrix. N Defined as

[0094]

[0095] Among them I 3×3 , O 3×3 They are 3×3 unit matrix and 3×3 zero matrix respectively; is the characteristic length, which is defined as the normalized length that ensures that all elements of the Jacobian matrix have the same physical units.

[0096] By adding k d Maximize, that is, choose k d The value of k is close to 100%, which can ensure that the industrial robot is far away from the singularity point, thereby enhancing its motion flexibility and operation accuracy. d A posture with a higher value of k means that the industrial robot has the best motion performance in this posture and can effectively transmit motion and force, which is crucial for accurate joint stiffness identification. d When the value is low, it indicates that the posture of the industrial robot is close to the singularity point, and its motion performance is poor, which may lead to an increase in the error of joint stiffness identification.

[0097] In the stiffness identification experiment of industrial robots, the stiffness of the area where the force and deformation of the industrial robot are large is low, which makes it easy to observe and measure the deformation of the end. In order to more accurately describe the relationship between the force and deformation of the end of the industrial robot, the flexibility matrix is ​​used instead of directly relying on the inverse of the Jacobian matrix. Because when the posture of the industrial robot is close to the singular point, the Jacobian matrix may be close to singularity, and its inverse is difficult to solve.

[0098] The relationship between the end deformation and the force can be expressed by the following equation:

[0099]

[0100] Where: f and m are the force vector and torque vector at the end of the industrial robot; C fd and C nδ are the force-linear displacement flexibility and moment-linear displacement flexibility matrices respectively; C fδ and C nδ They represent the force-angular displacement flexibility and torque-angular displacement flexibility matrices respectively; x is the movement deformation vector of the industrial robot end; δ is the rotation deformation vector of the industrial robot end.

[0101] Among them, the linear displacement deformation generated by the end of the industrial robot is much greater than the angular displacement deformation, that is, the industrial robot has a higher anti-angular displacement stiffness and a lower anti-linear displacement stiffness. Therefore, the simplified model is as follows:

[0102] x=C fd ·f

[0103] Assuming that the end linear displacement of the industrial robot is a unit vector, that is, x = 1, then:

[0104]

[0105] It can be described as an ellipsoid. The ellipsoid has three directions, and the length of the main axis of the ellipsoid is equal to the matrix corresponding to the three directions The square roots of the eigenvalues ​​λ1, λ2, λ3.

[0106] The stiffness value of the Cartesian flexibility ellipsoid of an industrial robot in the main direction reflects the optimal and worst stiffness of the industrial robot in a specific posture. The volume V of the flexibility ellipsoid is generally used as a scalar criterion for the stiffness performance of an industrial robot:

[0107]

[0108] det() represents the product of matrix eigenvalues;

[0109] Therefore, the global stiffness coefficient K s It can be defined as:

[0110]

[0111] In order to maximize the measurement accuracy in the process of industrial robot joint stiffness identification. First, the expected deformation model of the industrial robot end under external force is defined. This model predicts the displacement vector of the industrial robot end under different forces in different postures based on the known ideal stiffness model. Considering that the displacement perpendicular to the optical axis usually has higher measurement accuracy while the displacement along the optical axis is lower, the measurement accuracy index is used as the optimization target, so that the relative displacement of the target in the direction perpendicular to the optical axis caused by the deformation of the industrial robot end is as large as possible, thereby increasing the measurement accuracy. The specific optimization target formula is:

[0112]

[0113] Among them, Δx and Δy are the deformation of ArUco in the direction perpendicular to the optical axis mapped to the image coordinate system; Δz is the deformation of ArUco along the optical axis mapped to the image coordinate system; ω1 and ω2 are weight factors used to balance the measurement accuracy in different directions. This setting allows the selection of an industrial robot posture that makes the force deformation of the end of the industrial robot mainly concentrated in the direction perpendicular to the optical axis while ensuring the measurement accuracy.

[0114] Based on the dexterity, stiffness and visual measurement indicators mentioned above, this embodiment proposes a hierarchical multi-index industrial robot posture optimization method. First, based on the dexterity and stiffness indicators of the industrial robot, the posture of the industrial robot is preliminarily optimized. The posture set obtained after optimization is {P1}, and the preliminary optimization model is as follows:

[0115]

[0116] Among them, θ i is the rotation angle of joint i, θ2, θ3, θ5 are the 2nd, 3rd, and 5th joint angles of the six joints sorted upward from the base of the industrial robot, respectively; the workspace R1 where the optional postures of the industrial robot are located is selected according to the preset threshold of dexterity, and the workspace R6 where the optional postures of the industrial robot are located is selected according to the preset threshold of stiffness, as shown in Figure 4 As shown;

[0117] Then, the poses in the measured pose set {P1} are analyzed and considered based on the camera field of view and camera measurement accuracy indicators. This link is a decisive step in the process of optimizing the pose of the industrial robot. It involves the precise alignment between the industrial robot's visual system and the actual operation target, which is a key factor affecting the operation accuracy. n1 represents the number of poses in the measured pose set {P1}. A set of industrial robot joint angles is generated in a predefined way. Each pose is initially selected by its dexterity index k d and comprehensive stiffness performance, only when the dexterity index k of the posture isd >0.35 and comprehensive stiffness performance K s Only when it is <3500N / mm can it enter the next stage, which ensures that the flexibility of the selected posture set {P1} is basically qualified and the structural rigidity performance is poor.

[0118] By further screening the pose set {P1}, n2 poses with the smallest error are obtained to form the final optimal measurement pose set {P2}. This set combines the operational dexterity, structural rigidity and accuracy of the visual system of the industrial robot, thus providing the most theoretically reasonable pose configuration for the industrial robot stiffness identification. The visual error optimization model for the specific optimization problem is as follows:

[0119]

[0120] Among them, P is the coordinates of the four corner points of the ArUco marker measured by the monocular camera; Ф() is the field of view imaging model of the monocular camera; ψ is the visual measurement index, and ε is the threshold of the set visual measurement index. Based on the measured pose set {P1}, the visual error optimization model is solved to obtain the optimal measured pose set {P2}.

[0121] Step 3: Based on the optimal measurement pose set {P2}, the deformation, force and torque data of the industrial robot end under the action of the loading force are measured using the industrial robot joint stiffness identification device and the coordinate system transformation relationship. Based on the deformation, force and torque data corresponding to all the optimal measurement poses, the stiffness of each joint of the industrial robot is identified using the static stiffness model of the industrial robot. In the unloaded state and the state of applying a known external force in the same pose, the position of the industrial robot end is identified using a monocular camera according to the ArUco map that fully covers the kinematic model of the industrial robot, and the absolute position model of the industrial robot end under the corresponding pose is constructed. The magnitude and direction of the force / torque captured by the dynamometer are simultaneously recorded, and the stiffness of each joint of the industrial robot is then identified.

[0122] Step 3 of this implementation method includes the following key links:

[0123] In the unloaded state, a monocular camera is used to capture the data of each posture of the industrial robot. After applying a known external force at the same posture, the data is recorded again to capture the posture change of the industrial robot after loading. At the same time, the deformation of the end of the industrial robot under the action of the loading force and the magnitude and direction of the force / torque captured by the dynamometer are synchronously recorded. The performance of the industrial robot under actual working conditions is evaluated by calculating the posture error caused by the loading force. Afterwards, the stiffness of each joint of the industrial robot is identified based on the stiffness model of the industrial robot. Finally, by adding a set of industrial robot postures and combining the measured force and deformation data, the deformation predicted by the theory is calculated using the identified joint stiffness matrix and Jacobian matrix, and compared with the actual deformation measured by the laser tracker to verify the effectiveness of the experimental method.

[0124] like Figure 5 As shown in the figure, the joint stiffness parameters identified by the industrial robot joint stiffness identification method based on vision and step-by-step posture optimization accurately predicted the deformation of the industrial robot end effector under external force, with an error range of 4.27% to 13.79%. In contrast, when no posture optimization was performed, the error was significantly higher, exceeding 37.36% in some cases.

[0125] In summary, the present invention successfully proposes a method for identifying the joint stiffness of industrial robots based on vision and step-by-step posture optimization based on the dexterity, comprehensive stiffness and visual measurement indicators mentioned above; the required force and torque data are obtained without disassembling the industrial robots on the existing production line, which significantly improves the convenience of operation. The system is not only easy to operate and cost-effective, but also has good adaptability and scalability, and can meet the needs of various high-precision control tasks. Through the implementation of the present invention, it can provide strong technical support for various industrial applications, especially in the fields of aerospace, automobile manufacturing and precision machining, and has broad application prospects and significant economic and social value.

[0126] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. It should therefore be understood that many modifications may be made to the exemplary embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in a manner different from that described in the original claims. It should also be understood that features described in conjunction with individual embodiments may be used in other described embodiments.

Claims

1. An industrial robot joint stiffness identification device, characterized in that: Includes load, rope, dynamometer, monocular camera, ArUco marker and two pulleys; The monocular camera is installed at the end of the industrial robot to measure the deformation of the end of the industrial robot before and after the loading force by photographing the ArUco marker; The load is connected to the end of the industrial robot through a rope through a pulley at a high point and a pulley at a low point in sequence. The pulley at the low point transmits the force to the dynamometer, which measures the size and direction of the external load at the end of the industrial robot.

2. The identification method of the industrial robot joint stiffness identification device according to claim 1, characterized in that: The method comprises: Establish static stiffness model of industrial robot; According to the preset threshold, the measurement pose set {P1} of the industrial robot with high dexterity and low stiffness is screened, and the optimal measurement pose set {P2} is further selected from the screened measurement pose set {P1} by combining the field of view and measurement accuracy of the monocular camera; Under the optimal measurement posture, the industrial robot joint stiffness identification device is used to measure the deformation, force and torque data of the industrial robot end under the action of the loading force. Based on the deformation, force and torque data corresponding to all optimal measurement postures, the stiffness of each joint of the industrial robot is identified using the static stiffness model of the industrial robot.

3. The method for identifying joint stiffness of an industrial robot according to claim 2, characterized in that: The method for screening the high dexterity and low stiffness measurement pose set {P1} of the industrial robot according to the preset threshold is: A workspace R1 where optional postures of the industrial robot are selected according to a preset threshold value of dexterity, and a workspace R6 where optional postures of the industrial robot are selected according to a preset threshold value of stiffness; Establish a preliminary optimization model: The industrial robot is a six-degree-of-freedom industrial robot, and θ2, θ3, and θ5 are respectively the 2nd, 3rd, and 5th joint angles of six joints arranged upward from the base of the industrial robot.

4. The method for identifying joint stiffness of an industrial robot according to claim 2 or 3, characterized in that: The dexterity of industrial robots is measured by the dexterity index k d Determination, dexterity index k d for: Among them, k(J N ) is the Jacobian matrix condition number obtained based on the Frobenius norm of the Jacobian matrix.

5. The method for identifying joint stiffness of an industrial robot according to claim 2 or 3, characterized in that: The stiffness is determined by the global stiffness coefficient K s Determination, global stiffness coefficient K s for: Among them, C fd is the force-linear displacement flexibility matrix.

6. The method for identifying joint stiffness of an industrial robot according to claim 2, characterized in that: Combining the field of view of the monocular camera and the measurement accuracy, the method for further selecting the optimal measurement pose set {P2} from the screened pose set is: Establish a visual error optimization model: Wherein, P is the coordinates of the four corner points of the ArUco marker measured by the monocular camera; Ф() is the field of view imaging model of the monocular camera; Ψ is the visual measurement index, and ε is the threshold of the set visual measurement index; Based on the measured pose set {P1}, the visual error optimization model is solved to obtain the optimal measured pose set {P2}.

7. The method for identifying joint stiffness of an industrial robot according to claim 2 or 6, characterized in that: The field of view and measurement accuracy of the monocular camera are determined by the visual measurement index Ψ, which is: Among them, Δx and Δy are the deformations mapped to the image coordinate system in the direction perpendicular to the optical axis when measuring with a monocular camera, Δz is the deformation mapped to the image coordinate system along the optical axis when measuring with a monocular camera, and ω1 and ω2 are weight factors used to balance the measurement accuracy in different directions.

8. The method for identifying joint stiffness of an industrial robot according to claim 2, characterized in that: Based on the kinematic model of the industrial robot and using the virtual joint method, the static stiffness model of the industrial robot is established, assuming that the connecting rod is completely rigid and the end deformation is caused by the deformation of the joint.

9. The method for identifying joint stiffness of an industrial robot according to claim 8, characterized in that: The static stiffness model of the industrial robot is: Where F represents the external generalized force vector on the end of the industrial robot; J is the kinematic Jacobian matrix of the industrial robot in the current position; X is the deformation vector of the end of the industrial robot under force F.

10. The method for identifying joint stiffness of an industrial robot according to claim 9, characterized in that: J is obtained based on the kinematic equation of the industrial robot, which is: in, is the homogeneous transformation matrix of the first joint coordinate system {1} relative to the robot base coordinate system, is the homogeneous transformation matrix of the second joint coordinate system {2} relative to the first joint coordinate system {1}, is the homogeneous transformation matrix of the third joint coordinate system {3} relative to the second joint coordinate system {2}, is the homogeneous transformation matrix of the fourth joint coordinate system {4} relative to the third joint coordinate system {3}, is the homogeneous transformation matrix of the fifth joint coordinate system {5} relative to the fourth joint coordinate system {4}, is the homogeneous transformation matrix of the first joint coordinate system {6} relative to the fifth joint coordinate system {5}, is the homogeneous transformation matrix of the sixth joint coordinate system {6} relative to the robot base coordinate system.

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