CN111125843A - Industrial robot rigidity identification method based on digital image correlation technology - Google Patents
Industrial robot rigidity identification method based on digital image correlation technology Download PDFInfo
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Abstract
The invention discloses an industrial robot rigidity identification method based on a digital image correlation technology, which comprises the following steps: establishing an industrial robot rigidity identification test system based on a digital image correlation technique; carrying out a rigidity identification experiment in an industrial robot rigidity identification test system according to a preset mode, acquiring load data of the tail end of an industrial robot in the rigidity identification experiment and speckle pattern data of corresponding conditions, and extracting corresponding speckle pattern coordinate data from the speckle pattern data; calculating six-dimensional load data and six-dimensional deformation data of the industrial robot under the base coordinate system; and acquiring the joint stiffness value of the industrial robot according to the stiffness identification model based on the six-dimensional load data and the six-dimensional deformation data. The rigidity identification precision of the industrial robot is improved, so that the rigidity identification precision of the industrial robot is used for joint deformation compensation, and the rigidity identification method has important significance for improving the operation precision of the industrial robot and expanding the application field of the industrial robot.
Description
Technical Field
The invention relates to the technical field of rigidity identification of industrial robots, in particular to a rigidity identification method of an industrial robot based on a digital image correlation technology.
Background
The industrial robot has high flexibility, can realize complex pose transformation in space, and can execute different tasks such as grabbing, spraying, welding and the like by installing different actuators at the tail end of the robot. In addition, the main shaft is arranged at the tail end of the industrial robot, the main shaft can be used for machining, compared with the traditional machine tool, the machine tool has the advantages of high flexibility, large machining range and low cost, the complex part of the part can be easily machined through pose transformation, and the interference between the main shaft and the part is avoided. However, when the industrial robot is applied to machining, due to the characteristic that the industrial robot is connected with a plurality of connecting rods in series, the rigidity is far lower than that of a traditional numerical control machine, and the problems of insufficient precision of machined parts, high surface roughness and the like are caused by the trouble of frequently suffering from the flutter problem in the machining process.
At present, an industrial robot machining system is mainly used for chamfering, deburring, polishing and other processes of plastic and aluminum parts in the fields of aerospace, automobiles and the like, and is more suitable for the conditions that the hardness of a workpiece to be machined is lower, the removal amount in the cutting process is smaller and the machining precision requirement is not very high. In order to further improve the machining precision and expand the application of industrial robot machining in high-hardness material cutting, the rigidity characteristic and the kinematic modeling of the industrial robot need to be researched, and rigidity identification is an important step of the kinematic modeling.
The detection precision of the deformation of the tail end of the industrial robot is the key of the rigidity identification precision. The instrument that is applied to terminal deformation measurement of robot at present is mainly laser tracker, but laser tracker's measurement needs at the terminal installation target of robot, and the measuring accuracy can be influenced greatly to the installation accuracy of target internal reflection mirror to laser receives external environment's interference easily, and therefore laser tracker's measuring accuracy is very limited.
Disclosure of Invention
In order to overcome the defects of poor rigidity identification precision and complex operation in the prior art, the invention provides an industrial robot rigidity identification method based on a digital image correlation technology, which is used for identifying the joint rigidity of an industrial robot with high precision.
In order to solve the technical problem, the invention provides an industrial robot rigidity identification method based on a digital image correlation technology, which comprises the following steps:
establishing an industrial robot rigidity identification test system based on a digital image correlation technique;
carrying out a rigidity identification experiment in the rigidity identification test system of the industrial robot according to a preset mode, acquiring load data of the tail end of the industrial robot and speckle pattern data of corresponding conditions in the rigidity identification experiment, and extracting corresponding speckle pattern coordinate data from the speckle pattern data;
calculating six-dimensional load data and six-dimensional deformation data of the industrial robot under a base coordinate system according to the load data and the speckle pattern coordinate data;
and approximating the joint of the industrial robot to a linear torsion spring, constructing a rigidity identification model of the industrial robot, and acquiring the joint rigidity value of the industrial robot according to the rigidity identification model based on the six-dimensional load data and the six-dimensional deformation data.
Preferably, the step of establishing the industrial robot stiffness recognition test system based on the digital image correlation technology comprises the following steps:
setting the rigidity identification testing system of the industrial robot to comprise the industrial robot, a six-dimensional force sensor arranged at the tail end of the industrial robot, a load and a digital image device arranged corresponding to the industrial robot and the load;
and the digital image device comprises a speckle board fixed on the six-dimensional force sensor, an industrial camera with a camera shooting visual field parallel to the speckle board and a photographic lamp arranged corresponding to the industrial camera.
Preferably, the digital image device comprises not less than two industrial cameras, and the included angle of the optical axes of two adjacent industrial cameras is more than 15 degrees.
Preferably, according to a preset mode, a rigidity identification experiment is performed in the rigidity identification testing system of the industrial robot, and acquiring load data of the tail end of the industrial robot and speckle pattern data of corresponding conditions in the rigidity identification experiment comprises the following steps:
and when the preset pose in the preset pose set is sequentially taken as the pose to be detected and correspondingly collected, the industrial robot is positioned in the load data and the speckle pattern data of the pose to be detected in different load application directions.
Preferably, the collecting the load data and the speckle pattern data of the industrial robot in different load application directions in the to-be-detected pose comprises:
and when the preset load application directions in the preset load application direction set are sequentially taken as the load application directions to be detected, and the preset load application directions in the preset load application direction set are correspondingly collected as the load application directions to be detected, the industrial robot is positioned at the pose to be detected and belongs to the load data and the speckle pattern data of the condition of the load application directions to be detected.
Preferably, the collecting the load data and the speckle pattern data of the industrial robot in the to-be-detected pose and belonging to the to-be-detected load application direction condition comprises:
enabling the industrial robot to be located at the position to be detected, and acquiring a no-load speckle pattern when the tail end of the industrial robot is not loaded;
loading a load at the tail end of the industrial robot according to a preset load application direction, and acquiring load data of the tail end of the industrial robot and a loaded speckle pattern when the tail end of the industrial robot loads the load, wherein the no-load speckle pattern and the loaded speckle pattern form speckle pattern data;
and unloading the load at the tail end of the industrial robot, and restoring the industrial robot to the position to be detected.
Preferably, the preset pose set includes at least six preset poses, and the preset load application direction set includes at least three preset load application directions.
Preferably, extracting the corresponding speckle pattern coordinate data from the speckle pattern data comprises:
and extracting unloaded speckle pattern coordinate data from the unloaded speckle pattern, and extracting loaded speckle pattern coordinate data from the loaded speckle pattern, wherein the loaded speckle pattern coordinate data and the unloaded speckle pattern coordinate data form the speckle pattern coordinate data.
Preferably, the step of calculating six-dimensional load data and six-dimensional deformation data of the industrial robot in a base coordinate system according to the load data and the speckle pattern coordinate data comprises:
establishing a relation of coordinate data before and after the industrial robot loads the load according to the speckle pattern coordinate data;
according to the relation of coordinate data before and after the industrial robot loads the load, vector six-dimensional deformation data before and after the industrial robot loads the load is obtained by using a singular value decomposition method;
and converting the load data and the corresponding vector six-dimensional deformation data into a coordinate system to obtain six-dimensional load data and corresponding six-dimensional deformation data of the industrial robot under a base coordinate system.
Preferably, approximating the joint of the industrial robot as a linear torsion spring, the constructing of the stiffness recognition model of the industrial robot comprises:
establishing a Cartesian stiffness matrix of the industrial robot;
establishing a joint stiffness matrix of the industrial robot;
constructing a relational expression of the Cartesian stiffness matrix and the joint stiffness matrix:
and establishing a joint stiffness identification model of the industrial robot according to the Cartesian stiffness matrix and the relational expression of the joint stiffness matrix.
Compared with the prior art, one or more embodiments in the above scheme can have the following advantages or beneficial effects:
by applying the industrial robot rigidity identification method based on the digital image correlation technology provided by the embodiment of the invention, the digital image device is utilized to carry out high-precision identification on the deformation of the tail end of the robot, and the rigidity identification precision of the industrial robot is improved, so that the method is used for joint deformation compensation, and has important significance for improving the operation precision of the industrial robot and expanding the application field of the industrial robot.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic flow chart of an industrial robot stiffness identification method based on digital image correlation technology according to an embodiment of the invention;
fig. 2 is a process diagram of an industrial robot stiffness recognition method based on digital image correlation technology according to an embodiment of the invention;
FIG. 3 is a structural diagram of an industrial robot stiffness recognition test system in an industrial robot stiffness recognition method based on digital image correlation technology according to an embodiment of the invention;
fig. 4 shows a schematic diagram of a relevant coordinate system in an industrial robot stiffness identification method based on digital image correlation technology according to an embodiment of the present invention.
Detailed Description
The following detailed description of the embodiments of the present invention will be provided with reference to the drawings and examples, so that how to apply the technical means to solve the technical problems and achieve the technical effects can be fully understood and implemented. It should be noted that, as long as there is no conflict, the embodiments and the features of the embodiments of the present invention may be combined with each other, and the technical solutions formed are within the scope of the present invention.
The industrial robot machining system is mainly used for chamfering, deburring, polishing and other processes of plastic and aluminum parts in the fields of aerospace, automobiles and the like, and is more suitable for the conditions that the hardness of a workpiece to be machined is lower, the removal amount in the cutting process is smaller and the requirement on machining precision is not very high. In order to further improve the machining precision and expand the application of industrial robot machining in high-hardness material cutting, the rigidity characteristic and the kinematic modeling of the industrial robot need to be researched, and rigidity identification is an important step of the kinematic modeling. The detection precision of the deformation of the tail end of the industrial robot is the key of the rigidity identification precision. The instrument that is applied to terminal deformation measurement of robot at present is mainly laser tracker, but laser tracker's measurement needs at the terminal installation target of robot, and the measuring accuracy can be influenced greatly to the installation accuracy of target internal reflection mirror to laser receives external environment's interference easily, and therefore laser tracker's measuring accuracy is very limited.
Example one
In order to solve the technical problems in the prior art, the embodiment of the invention provides an industrial robot rigidity identification method based on a digital image correlation technology.
Fig. 1 is a schematic flow chart of an industrial robot stiffness identification method based on digital image correlation technology according to an embodiment of the invention; fig. 2 is a process schematic diagram of an industrial robot stiffness identification method based on digital image correlation technology according to an embodiment of the present invention, and referring to fig. 1 and fig. 2, the industrial robot stiffness identification method based on digital image correlation technology according to an embodiment of the present invention includes the following steps.
And S101, establishing an industrial robot rigidity identification testing system based on the digital image correlation technology.
Specifically, before the industrial robot rigidity identification method is carried out, an industrial robot rigidity identification test system needs to be constructed, and the industrial robot rigidity identification test system based on the digital image correlation technology is constructed in the embodiment of the invention. FIG. 3 is a structural diagram of an industrial robot stiffness recognition test system in an industrial robot stiffness recognition method based on digital image correlation technology according to an embodiment of the invention; referring to fig. 3, the specific setup of the stiffness recognition testing system for an industrial robot in the present embodiment includes: the system comprises an industrial robot, a six-dimensional force sensor arranged at the tail end of the industrial robot, a load and a digital image device arranged corresponding to the industrial robot and the load; it should be noted that the load can be linked with the six-dimensional force sensor, and specifically, the connection of the pulley and the rope connecting the two can be realized, specifically, the connection rope for suspending the load is connected to the six-dimensional force sensor by bypassing the pulley, and the position of the pulley can be changed, so that the direction of the stress at the tail end of the robot can be changed. The digital image device in the embodiment further comprises a speckle board fixed on the six-dimensional force sensor, an industrial camera with a shooting field parallel to the speckle board and a shooting lamp arranged corresponding to the industrial camera. Preferably, the digital image device comprises two industrial cameras, while the corresponding device comprises two photography lights. The camera lamp is used for providing a stable light source, and the included angle of the optical axes of two adjacent industrial cameras is larger than 15 degrees. It should be noted that the six-dimensional force sensor can acquire the force and moment applied to the end of the industrial robot.
And S102, carrying out a rigidity identification experiment in the rigidity identification test system of the industrial robot according to a preset mode, acquiring load data of the tail end of the industrial robot in the rigidity identification experiment and speckle pattern data of corresponding conditions, and extracting corresponding speckle pattern coordinate data from the speckle pattern data.
In order to ensure the rigidity identification precision, the method can acquire the load data of the industrial robot in different poses and different directions and the speckle pattern data of the corresponding situation. Specifically, when the preset pose in the preset pose set is sequentially used as the pose to be detected and correspondingly collected as the pose to be detected, the industrial robot is located in load data and speckle pattern data of the pose to be detected in different load application directions. The preset pose set is set manually, and experimenters can set the poses of the industrial robots to be acquired as the preset poses according to actual conditions; and simultaneously setting all preset poses in the preset pose set to be different. In order to further ensure the rigidity identification accuracy, the preset pose set is set to include at least six preset poses. It should be noted that when selecting the pose, the industrial robot should avoid the singular pose, i.e., when the jacobian matrix is 0.
Further, when the preset pose in the preset pose set is sequentially used as the pose to be detected, the acquisition process of the load data and the speckle pattern data of the industrial robot in different load application directions of the pose to be detected comprises the following steps: and when the preset load application directions in the preset load application direction set are correspondingly collected as the load application directions to be detected, the industrial robot is positioned at the pose to be detected and belongs to the load data and the speckle pattern data of the condition of the load application directions to be detected. Similarly, it should be noted that the preset load application direction set is also set artificially, and an experimenter can set the preset load application directions to which the poses of the industrial robot to be acquired belong as the preset load application directions according to actual conditions; and simultaneously setting all the preset load applying directions in the preset load applying direction set to be different. In order to further ensure the rigidity identification precision, the preset load application direction set comprises at least three preset load application directions.
Furthermore, when the preset load application direction in the preset load application direction set is sequentially used as the load application direction to be detected, the process of acquiring the load data and the speckle pattern data of the industrial robot which is located at the position and posture to be detected and belongs to the condition of the load application direction to be detected comprises the following steps: enabling the industrial robot to be located at a pose to be detected, and acquiring a no-load speckle pattern when the tail end of the industrial robot is not loaded; loading a load at the tail end of the industrial robot according to a preset load application direction, and acquiring load data of the tail end of the industrial robot and a loaded speckle pattern when the tail end of the industrial robot loads the load, wherein the unloaded speckle pattern and the loaded speckle pattern form speckle pattern data; and unloading the load at the tail end of the industrial robot, and restoring the industrial robot to the position to be detected.
For a more clear description of step S102, a specific implementation of the above process is described as follows:
(1) the industrial robot is adjusted to one of a set of preset poses.
(2) When the tail end of the industrial robot is unloaded, the industrial camera is used for collecting the unloaded speckle pattern when the tail end of the industrial robot is unloaded, and the collected image is not less than 50.
(3) Loading a load at the tail end of the industrial robot according to a preset load application direction, and acquiring a loaded speckle pattern when the tail end of the industrial robot is loaded by an industrial camera, wherein the acquired image is not less than 50 images; and meanwhile, the load data of the tail end of the industrial robot is acquired through the six-dimensional force sensor.
(4) And unloading the load at the tail end of the industrial robot, and controlling the industrial robot to perform reciprocating motion with a certain amplitude after unloading to eliminate residual deformation.
(5) And (3) changing the preset load application direction of the industrial robot, repeating the substeps (1) to (4), and selecting not less than three different preset load application directions for each preset pose to ensure the identification precision.
(6) And (3) replacing the preset pose of the industrial robot, repeating the substeps (1) to (5), and selecting not less than six different preset poses for data acquisition in order to ensure the identification precision.
After collecting the load data of the end of the industrial robot and the speckle pattern data of the corresponding situation in the stiffness identification experiment, extracting the corresponding speckle pattern coordinate data from the speckle pattern data specifically comprises the following steps: and extracting the coordinate data of the unloaded speckle pattern from the unloaded speckle pattern, extracting the coordinate data of the loaded speckle pattern from the loaded speckle pattern, and forming the coordinate data of the speckle pattern by the coordinate data of the loaded speckle pattern and the coordinate data of the unloaded speckle pattern.
And S103, calculating six-dimensional load data and six-dimensional deformation data of the industrial robot in the base coordinate system according to the load data and the speckle pattern coordinate data.
Specifically, a relation of coordinate data before and after loading of the industrial robot is established according to the speckle pattern coordinate data; according to the relation of coordinate data before and after the industrial robot loads the load, vector six-dimensional deformation data before and after the industrial robot loads the load is obtained by using a singular value decomposition method; and (4) converting the load data and the corresponding vector six-dimensional deformation data into a coordinate system to obtain six-dimensional load data and corresponding six-dimensional deformation data of the industrial robot under the base coordinate system. Further, fig. 4 is a schematic diagram of a relevant coordinate system in an industrial robot stiffness identification method based on digital image correlation technology according to an embodiment of the present invention, and is referred to fig. 4, where a coordinate system {7} is a speckle board center coordinate system; the coordinate system {6} is an industrial robot end coordinate system; the coordinate system {0} is a base coordinate system.
Assuming that the series of point coordinates proposed by the industrial robot before loading the load is p1,p2,...,pnN is more than or equal to 3; a series of point coordinates which are provided after the industrial robot loads the load are { p }1',p2',...,pn' }, the relationship of coordinate data before and after the industrial robot loads the load can be calculated as follows:
pi'＝R*pi+t (1)
where R is a 3 × 3 rotation matrix and t is a 3 × 1 translation vector.
According to the formula (1), provided
Singular value decomposition of H, let H have the form: h ═ U Λ VT. Where U, V is an orthogonal matrix and Λ is a diagonal matrix, the R and t matrices can be solved from U and V:
R＝VUT(2)
t＝μB-R*μA(3)
then two series of R and t can be obtained with matching point coordinates, where,
from R and t, further vector six-dimensional deformation data Δ X of the industrial robot can be derived, wherein,
dx7＝t11
dy7＝t21
dz7＝t31
δx7＝atan2(r32/cos(δy),r33/cos(δy))
δz7＝atan2(r21/cos(δy),r11/cos(δy)) (6)
wherein d isx7、dy7、dz7、δy7、δx7、δz7Indicating that this element is depicted in the speckle plate center coordinate system {7} shown in fig. 4.
And converting the calculated deformation vector data into an industrial robot base coordinate system description mode to enable the deformation vector data to be consistent with the established industrial robot rigidity model coordinate system.
The vector six-dimensional deformation data delta X in the speckle plate central coordinate system {7} comprises a displacement part and a rotation part, and respectively comprises the following parts:
7d7＝[dx7,dy7,dz7]T
7δ7＝[δx7,δy7,δz7]T(7)
7d7and7δ7respectively, indicating that the displacement vector and the rotation vector act on the origin of the coordinate system 7, and are described in 7.
According to the method for describing the homogeneous coordinate of the pose of the industrial robot, a homogeneous transformation matrix of a coordinate system {7} relative to an end coordinate system {6} of the industrial robot is set as
Inverse matrix thereof
Is a homogeneous transformation matrix of the coordinate system 6 relative to the coordinate system 7,
the translation part is matrix of
Homogeneous transformation matrix of coordinate system {7} relative to base coordinate system {0}
Comprises the following steps:
homogeneous transformation matrix
Rotating part matrix of
The deformation vector acting on the center point of the end and described in the coordinate system {0} can be calculated as:
and converting the collected load data into a description mode of an industrial robot base coordinate system, so that the description mode is consistent with the established industrial robot rigidity model coordinate system.
The force vector measured by the force/moment sensor of the six-dimensional sensor comprises two parts of force and moment, namely6f6＝[fx6,fy6,fz6]T
6n6＝[nx6,ny6,nz6]T(10)
6f6And6n6representing force and moment vectors acting on a coordinate system6 origin and is described in 6.
The rotation transformation matrix of the coordinate system {6} relative to the coordinate system {0} is
The force vector acting on the center point of the end and described in the coordinate system {0} can be calculated as:
and S104, approximating the joint of the industrial robot to a linear torsion spring, constructing a rigidity identification model of the industrial robot, and acquiring the joint rigidity value of the industrial robot according to the rigidity identification model based on six-dimensional load data and six-dimensional deformation data.
Assuming that the industrial robot includes 6 joints, the process of specifically constructing the stiffness recognition model of the industrial robot includes:
first, a cartesian stiffness matrix of the industrial robot is established.
Assuming that the force experienced by the robot tip is F,
the deformation data generated was Δ X, Δ X ═ 2 [, ]0dx6 0dy6 0dz6 0δx6 0δy6 0δz6]T. According to Hooke's law, there is a relationship F ═ KxΔ X, wherein KXIs a matrix of 6 x 6, which is the cartesian stiffness matrix of an industrial robot.
Secondly, a joint stiffness matrix of the industrial robot is established.
In the joint space, 6 joint bearing moments of the industrial robot are set to be tauiThen, in particular, τiIs τ ═ τ [ τ ]1τ2τ3τ4τ5τ6]TSimultaneously setting the generation rotation angle of 6 joints as delta thetaiThen, the specific Δ θiIs Δ θi＝[Δθ1Δθ2Δθ3Δθ4Δθ5Δθ6]T. Similarly, according to hooke's law, there is a relationship τ ═ KθΔ θ, where KθRepresenting the joint stiffness matrix of the industrial robot.
And establishing a relation between the Cartesian stiffness matrix and the joint stiffness matrix of the industrial robot.
According to the end stress F of the industrial robot and the deformation data Delta X thereof, and the torque tau borne by 6 jointsiAnd the corresponding resulting angle of rotation delta thetaiThe relationship between the cartesian stiffness matrix and the joint stiffness matrix can be derived:
KX＝J-TKθJ-1(12)
j is a 6 x 6 matrix, is a Jacobian matrix of the industrial robot, changes along with the change of the pose of the industrial robot, and can be solved through a vector product method.
And finally, establishing a joint stiffness identification model of the industrial robot.
Substituting equation (12) into equation F ═ KxΔ X, the results are transformed to give:
since the joint flexibility of the robot is the inverse of the joint stiffness, i.e.
Therefore, the joint flexibility matrix of the industrial robot is c ═ c1c2c3c4c5c6]T＝[1/k θ11/k θ21/k θ31/k θ41/k θ51/kθ6]T，
For equation (13), the six-dimensional vector c is decomposed to obtain the relationship: ac ═ Δ X, where,
the matrix A is a 6 × 6 matrix and can be calculated by a Jacobian matrix J and a force vector F. A c vector is calculated from a set of F, Δ X, J obtained from a set of experimental tests.
Based on six-dimensional load data and six-dimensional deformation data, the joint rigidity value of the industrial robot obtained according to the rigidity identification model comprises the following steps:
assuming that m sets of experiments are performed in the stiffness identification experiment at multiple poses, i.e., m sets of F, Δ X, J, A, the most accurate compliance vector c needs to be calculated according to the least squares method.
Let Aall＝[A1；A2；...；Am]，ΔXall＝[ΔX1；ΔX2；...；ΔXm]Then there is
Aallc＝ΔXall(15)
For equation (15), the unknowns are 6, the number of equations is 6m, and the sum of the squared errors can be expressed as:
c, which minimizes the value of error e (c), is:
from this, the most accurate flexibility matrix c, the joint stiffness value K of the industrial robot can be obtainedθComprises the following steps:
Kθ＝1./c (18)
according to the industrial robot rigidity identification method based on the digital image correlation technology, the digital image device is used for carrying out high-precision identification on the deformation of the tail end of the robot, and the rigidity identification precision of the industrial robot is improved, so that the method is used for joint deformation compensation, and has important significance for improving the operation precision of the industrial robot and expanding the application field of the industrial robot.
Although the embodiments of the present invention have been described above, the above description is only for the convenience of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. An industrial robot rigidity identification method based on digital image correlation technology comprises the following steps:
establishing an industrial robot rigidity identification test system based on a digital image correlation technique;
carrying out a rigidity identification experiment in the rigidity identification test system of the industrial robot according to a preset mode, acquiring load data of the tail end of the industrial robot and speckle pattern data of corresponding conditions in the rigidity identification experiment, and extracting corresponding speckle pattern coordinate data from the speckle pattern data;
calculating six-dimensional load data and six-dimensional deformation data of the industrial robot under a base coordinate system according to the load data and the speckle pattern coordinate data;
and approximating the joint of the industrial robot to a linear torsion spring, constructing a rigidity identification model of the industrial robot, and acquiring the joint rigidity value of the industrial robot according to the rigidity identification model based on the six-dimensional load data and the six-dimensional deformation data.
2. The method of claim 1, wherein the step of establishing an industrial robot stiffness recognition test system based on digital image correlation technique comprises:
setting the rigidity identification testing system of the industrial robot to comprise the industrial robot, a six-dimensional force sensor arranged at the tail end of the industrial robot, a load and a digital image device arranged corresponding to the industrial robot and the load;
and the digital image device comprises a speckle board fixed on the six-dimensional force sensor, an industrial camera with a camera shooting visual field parallel to the speckle board and a photographic lamp arranged corresponding to the industrial camera.
3. The method of claim 2, wherein the digital image device comprises no less than two industrial cameras, and an included angle between optical axes of two adjacent industrial cameras is greater than 15 °.
4. The method of claim 1, wherein a rigidity identification test is performed in the rigidity identification test system of the industrial robot according to a preset mode, and the collecting of the load data of the end of the industrial robot and the speckle pattern data of the corresponding situation in the rigidity identification test comprises:
and when the preset pose in the preset pose set is sequentially taken as the pose to be detected and correspondingly collected, the industrial robot is positioned in the load data and the speckle pattern data of the pose to be detected in different load application directions.
5. The method according to claim 4, wherein acquiring load data and speckle pattern data of the industrial robot in different load application directions in the posture to be inspected comprises:
and when the preset load application directions in the preset load application direction set are sequentially taken as the load application directions to be detected, and the preset load application directions in the preset load application direction set are correspondingly collected as the load application directions to be detected, the industrial robot is positioned at the pose to be detected and belongs to the load data and the speckle pattern data of the condition of the load application directions to be detected.
6. The method of claim 5, wherein collecting load data and speckle pattern data of the industrial robot in the to-be-detected pose and pertaining to the to-be-detected load application direction condition comprises:
enabling the industrial robot to be located at the position to be detected, and acquiring a no-load speckle pattern when the tail end of the industrial robot is not loaded;
loading a load at the tail end of the industrial robot according to a preset load application direction, and acquiring load data of the tail end of the industrial robot and a loaded speckle pattern when the tail end of the industrial robot loads the load, wherein the no-load speckle pattern and the loaded speckle pattern form speckle pattern data;
and unloading the load at the tail end of the industrial robot, and restoring the industrial robot to the position to be detected.
7. The method according to claim 6, wherein the set of preset poses includes at least six preset poses, and the set of preset load application directions includes at least three preset load application directions.
8. The method of claim 6, wherein extracting corresponding speckle pattern coordinate data from the speckle pattern data comprises:
and extracting unloaded speckle pattern coordinate data from the unloaded speckle pattern, and extracting loaded speckle pattern coordinate data from the loaded speckle pattern, wherein the loaded speckle pattern coordinate data and the unloaded speckle pattern coordinate data form the speckle pattern coordinate data.
9. The method of claim 1, wherein the step of calculating six-dimensional load data and six-dimensional deformation data of the industrial robot in a base coordinate system from the load data and speckle pattern coordinate data comprises:
establishing a relation of coordinate data before and after the industrial robot loads the load according to the speckle pattern coordinate data;
according to the relation of coordinate data before and after the industrial robot loads the load, vector six-dimensional deformation data before and after the industrial robot loads the load is obtained by using a singular value decomposition method;
and converting the load data and the corresponding vector six-dimensional deformation data into a coordinate system to obtain six-dimensional load data and corresponding six-dimensional deformation data of the industrial robot under a base coordinate system.
10. The method of claim 1, wherein approximating the joint of the industrial robot as a linear torsion spring, constructing a stiffness-recognition model of the industrial robot comprises:
establishing a Cartesian stiffness matrix of the industrial robot;
establishing a joint stiffness matrix of the industrial robot;
constructing a relational expression of the Cartesian stiffness matrix and the joint stiffness matrix:
and establishing a joint stiffness identification model of the industrial robot according to the Cartesian stiffness matrix and the relational expression of the joint stiffness matrix.
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