CN113500585B - Robot measurement pose evaluation method and evaluation device for kinematic calibration - Google Patents

Robot measurement pose evaluation method and evaluation device for kinematic calibration Download PDF

Info

Publication number
CN113500585B
CN113500585B CN202110807852.1A CN202110807852A CN113500585B CN 113500585 B CN113500585 B CN 113500585B CN 202110807852 A CN202110807852 A CN 202110807852A CN 113500585 B CN113500585 B CN 113500585B
Authority
CN
China
Prior art keywords
robot
pose
error
determining
task space
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202110807852.1A
Other languages
Chinese (zh)
Other versions
CN113500585A (en
Inventor
吴军
叶豪
王煜天
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tsinghua University
Original Assignee
Tsinghua University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tsinghua University filed Critical Tsinghua University
Priority to CN202110807852.1A priority Critical patent/CN113500585B/en
Publication of CN113500585A publication Critical patent/CN113500585A/en
Application granted granted Critical
Publication of CN113500585B publication Critical patent/CN113500585B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/003—Programme-controlled manipulators having parallel kinematics
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/08—Programme-controlled manipulators characterised by modular constructions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
    • B25J9/1661—Programme controls characterised by programming, planning systems for manipulators characterised by task planning, object-oriented languages
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1679—Programme controls characterised by the tasks executed
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

Landscapes

  • Engineering & Computer Science (AREA)
  • Robotics (AREA)
  • Mechanical Engineering (AREA)
  • Numerical Control (AREA)
  • Manipulator (AREA)

Abstract

The application discloses a robot measurement pose evaluation method and an evaluation device for kinematic calibration, wherein the method is used for evaluating a set of given measurement poses and task space in which the robot needs to ensure precision, the estimated square value of the residual root mean square of poses in the task space subjected to the kinematic calibration based on the set of measurement poses is expected to serve as a standard for evaluating the set of measurement poses, and corresponding weighted evaluation indexes are given by considering different noise intensities of measurement components and different weights of pose precision. The problem that the conventional measuring pose evaluation method focuses on geometric error observability and cannot directly reflect the kinematic calibration effect is solved, the measuring pose evaluation and the kinematic calibration can be more accurately and intuitively connected, and the amplitude of the residual error after the kinematic calibration in the robot task space is reflected.

Description

Robot measurement pose evaluation method and evaluation device for kinematic calibration
Technical Field
The application relates to the technical field of robot kinematics calibration, in particular to a robot measurement pose evaluation method and device for kinematics calibration.
Background
The geometric errors of the robot are caused by factors such as manufacturing, assembly and the like, so that the positioning accuracy of the robot is reduced, and the industrial application of the robot is limited, and therefore, the robot needs to be subjected to kinematic calibration before delivery. In the general kinematic calibration method, geometric errors are identified by measuring the pose of a plurality of groups of end effectors of the robot through an established geometric error model and utilizing theoretical and actual pose deviations, so that the kinematic model of the robot is corrected to improve the precision of the positioning pose of the end of the robot.
For kinematic calibration, in order to ensure the positioning accuracy after calibration in the whole task space and simultaneously consider the problem of measurement cost caused by high number of measurement pose, the measurement pose needs to be optimized. The premise of optimizing the measurement pose is to quantitatively evaluate the index of a group of determined measurement poses.
The current research on the evaluation method mainly focuses on the analysis of an error identification matrix, which is the analysis on the observability of geometric errors, and comprises parameter variance minimization indexesCondition number reciprocal indicator->Terminal pose uncertainty minimization index O 3 ＝σ L Noise method index->A is an optimal index->
In the prior art, the current evaluation method comprehensively analyzes the error identification equation in various aspects of statistics, but the limitation is that only the observability of the geometric error is analyzed, and in fact, the estimated deviation of the geometric error is not consistent with the pose influence of the end effector, so the observability of the geometric error and the residual error after the kinematic calibration are not strictly consistent. Note that the purpose of the kinematic calibration is to improve the positioning accuracy of the overall task space, and the observability of the geometric error is an indirect reaction, so that an index directly reflecting the positioning accuracy of the overall task space after the kinematic calibration can more intuitively embody the influence of the measurement pose evaluation on the kinematic calibration.
Disclosure of Invention
The present application aims to solve at least one of the technical problems in the related art to some extent.
Therefore, an object of the present application is to provide a method for evaluating a measuring pose of a robot for kinematic calibration, which solves the problem that the existing measuring pose evaluation method focuses on geometric error observability and cannot directly reflect the kinematic calibration effect.
Another object of the present application is to provide a robot measurement pose evaluation device for kinematic calibration.
In order to achieve the above object, an embodiment of an aspect of the present application provides a method for evaluating a robot measurement pose for kinematic calibration, including:
establishing a geometric error model of the robot;
determining and discretizing a task space of the robot, determining a measured noise intensity weight and a pose precision weight of the robot, determining a corresponding error transfer matrix set according to each set of poses in the discretized task space of the robot and preprocessing the set of error transfer matrices according to the pose precision weights;
and determining an error identification equation according to the pose to be evaluated and the geometric error model, determining a root mean square value expected mean value of the pose residual error after kinematic calibration in the task space of the discretized robot according to the measured noise intensity weight, and evaluating the calibration effect of the robot by utilizing the root mean square value expected mean value of the pose residual error.
In order to achieve the above object, another embodiment of the present application provides a robot measurement pose evaluation device for kinematic calibration, including:
the modeling module is used for establishing a geometric error model of the robot;
the computing module is used for determining and discretizing the task space of the robot, determining the measured noise intensity weight and the pose precision weight of the robot, determining a corresponding error transfer matrix set according to each set of poses in the discretized task space of the robot and preprocessing the set according to the pose precision weight;
the evaluation module is used for determining an error identification equation according to the pose to be evaluated and the geometric error model, determining a root mean square value expected mean value of the pose residual error after kinematic calibration in the task space of the discretized robot according to the measured noise intensity weight, and evaluating the calibration effect of the robot by utilizing the root mean square value expected mean value of the pose residual error.
The application provides a measuring pose evaluation method considering pose residual errors in a task space after the kinematic calibration based on the set of measuring poses, wherein for a given set of measuring poses and a task space in which the robot needs to guarantee precision, an estimated square value of the root mean square of the pose residual errors in the task space after the kinematic calibration based on the set of measuring poses is expected to be used as a standard for evaluating the set of measuring poses, and corresponding weighted evaluation indexes are given by considering different noise intensities of measuring components and different weights of pose precision. The provided index is different from the previous evaluation index for representing the observability of the kinematic parameter error, is used for representing the amplitude of the residual error after the kinematic calibration in the robot task space, is used for the field of the kinematic calibration of the robot, and aims to optimize the selected measurement pose based on the method to improve the precision of the robot or reduce the number of the measurement poses required by the kinematic calibration. Therefore, the problem that the conventional measuring pose evaluation method focuses on geometric error observability and cannot directly reflect the kinematic calibration effect is solved, so that the measuring pose evaluation and the kinematic calibration are more accurately and intuitively connected, and the amplitude of the residual error after the kinematic calibration in the robot task space is reflected.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
Drawings
The foregoing and/or additional aspects and advantages of the application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flow chart of a robot measurement pose evaluation method for kinematic calibration according to one embodiment of the present application;
FIG. 2 is a typical series-parallel robot configuration according to one embodiment of the present application;
FIG. 3 is a schematic diagram of a typical task space discretization;
fig. 4 is a schematic structural view of a robot measurement pose evaluation device for kinematic calibration according to an embodiment of the present application.
Reference numerals: 1-a first branch; 2-a second branch; 3-a third branch; 4-setting a platform; a 5-C type member; 6-a type member; 7-a movable platform; 8-setting a platform.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present application and should not be construed as limiting the application.
The method and the device for evaluating the measuring pose of the robot for kinematic calibration according to the embodiment of the application are described below with reference to the accompanying drawings.
First, a robot measurement pose evaluation method for kinematic calibration according to an embodiment of the present application will be described with reference to the accompanying drawings.
Fig. 1 is a flowchart of a robot measurement pose evaluation method for kinematic calibration according to an embodiment of the present application.
As shown in fig. 1, the robot measurement pose evaluation method for kinematic calibration includes the following steps:
and S1, establishing a geometric error model of the robot.
Optionally, in one embodiment of the present application, establishing the geometric error model of the robot is:
wherein ,δbE Is the position omega of the end effector of the robot E Is the attitude error of the robot terminal executor,representing the total n geometrical errors which are not related to each other, M is a corresponding error transfer matrix, and represents the influence of the geometrical errors in E on the position and posture errors of the robot terminal executor as a function of the displacement vector q of the driving shaft of the robot.
Step S2, determining and discretizing a task space of the robot, determining a measured noise intensity weight and a pose precision weight of the robot, determining a corresponding error transfer matrix set according to the pose precision weight for each group of poses in the task space of the discretized robot, and preprocessing the error transfer matrix set.
The method comprises the steps of determining basic information of a robot and preprocessing, firstly, determining and discretizing a task space of the robot, wherein the task space of the robot refers to a space formed by terminal poses of the robot for realizing task requirements, and positioning accuracy is generally required to be ensured. The task space is a space in which the terminal poses of the robot are continuously distributed, and because the space is provided with infinite elements, the infinite elements are required to be discretized for analysis, wherein the discretization refers to taking limited elements in the space as the representation of the whole space, and the task space can be uniformly sampled. The discretized task space can be represented as a set of robot drive axis displacement vectors, pi= { s 1 ，s 2 ，...，s m And can also be expressed as a mathematical representation that uniquely characterizes the discretized task space.
Secondly, determining the weight of the intensity of the measuring noise of the robot, measuring the tail end pose error of the measuring pose of the robot, wherein the measuring precision is influenced by the measuring noise, and the measuring noiseAssuming that an independent normal distribution with an average value of 0 is satisfied, but the variance of its normal distribution is not uniform due to the differences of the different component intensities, +.>Normalization of variance matrix to symmetric positive definite matrix W -1 The normalization method may be used to scale its specific element to 1 or otherwise characterize the measured noise intensity weight of the robot and is determined a priori by the measuring instrument and the measurement scheme.
Thirdly, determining pose precision weight of the robot: the position and the gesture of the robots are different in expression units, the requirements of the actual robots on the position and the gesture precision are different, the gesture precision weight can be expressed as a diagonal matrix C, and particularly, the ratio of the position and the gesture precision requirements is r (rad) -1 ) When C can be determined asC＝diag(1，1，1，r，r，r)。
Finally, data preprocessing is carried out: for each group of poses in the discretized task space pi, determining a corresponding error transfer matrix set to be pi A ＝{A 1 ，A 2 ，...，A m}, wherein Ai ＝M(s i ) Preprocessing matrixThe robot needs to calculate only once for a particular structural parameter, task space and geometric error model.
And S3, determining an error identification equation according to the pose to be evaluated and the geometric error model, determining a root mean square value expected mean value of the pose residual error after kinematic calibration in the task space of the discretized robot according to the measured noise intensity weight, and evaluating the calibration effect of the robot by using the root mean square value expected mean value of the pose residual error.
Optionally, in one embodiment of the present application, S3 further includes:
the error identification equation is determined according to the pose to be evaluated and the geometric error model, and is as follows:
δ＝M a ∈
wherein ,M i the delta is a corresponding pose error measurement value;
based on the measured noise intensity weight of the robot, determining an estimated value of the geometric error according to a weighted least square methodWherein W is the inverse of the measurement noise variance matrix;
determining an estimated value and an actual value of a geometric error, and normalizing pose residual errors after kinematic calibration in a task space of the discretized robot wherein ,/>e as geometric error estimate * Is the actual value of the geometric error, C is the matrix corresponding to the pose accuracy weight, A i For the pose s i A corresponding error transfer matrix;
determining root mean square of kinematically calibrated pose residual errors in task space of discretized robot according to estimated values and pose residual errorsWherein m is the total pose number in the discretized task space;
determining the expected mean of the root mean square value from statistical knowledgeWherein A is a pretreatment matrix.
Specifically, for evaluating any measurement pose, first, based on the measurement pose to be evaluated combining Ω and a geometric error model, an error recognition equation of δ=m can be determined a∈, wherein To be transmitted by error matrix M i ＝M(q i ) Stack determination, δ is the corresponding pose error measurement.
Based on the measured noise intensity weight of the robot, the estimated value of epsilon can be determined according to a weighted least square method The widely used least squares method e=mapma-1 MaT δ can be regarded as a special case of taking equal values on the components of the noise intensity.
Estimate for epsilonAnd the actual value epsilon * In the task space s i The normalized pose residual after kinematic calibration at the site can be expressed as +.>Wherein the pose accuracy weight matrix C of the robot converts the pose errors into the same units to realize normalization, and directly considers +.>The normalized error is a special case of the weight matrix C as an identity matrix.
Based on the estimated value, the root mean square of the pose residual error after kinematic calibration in the discretized task space pi is expressed asThe expected mean value of the square value can be determined through analysis of statistical knowledgeAnd taking eta as a pose evaluation value provided by the method, wherein the larger the value is, the larger the pose residual error after the kinematic calibration is, and the poorer the kinematic calibration effect is.
For the pose evaluation indexIn, η can also be expressed as +.>And the like.
Fig. 2 shows a typical serial robot configuration, which includes a three-degree-of-freedom parallel mechanism and a two-degree-of-freedom serial mechanism connected in series with the parallel mechanism. The three-degree-of-freedom parallel mechanism comprises an upper fixed platform 8, a lower fixed platform 4, a parallel linkage platform 7 and three branch assemblies 1, 2 and 3. The first branch assembly 1 and the second branch assembly 2 which are the same in structure in the three branch assemblies are positioned on the same plane and pass through the upper fixed platform 8, and are connected with the upper fixed platform 8 through a rotary hinge. The third branch assembly 3 passes through the lower fixed platform 4 and is connected with the lower fixed platform 4 by a rotary hinge. The front ends of the first branch assembly 1 and the second branch assembly 2 are connected with the parallel linkage platform 7 through a rotary hinge, and the front end of the third branch assembly 3 is fixedly connected with the parallel linkage platform 7. The two-degree-of-freedom posture serial mechanism includes a C-shaped member 5 and an a-shaped member 6. The C-shaped member 5 is connected with the parallel connection platform 7 by a rotating hinge. The first end of the A-shaped component 6 is provided with a matching hole connected with the knife handle, the plane where the hole is located is used as a terminal moving platform of the robot, and the second end of the A-shaped component is connected with the C-shaped component through a rotating hinge. The C-shaped member 5, the a-shaped member 6 and the three branch assemblies 1, 2, 3 serve as five drive shafts for the robot. The provided flow chart of the robot measurement pose evaluation method for kinematic calibration is applied to the series-parallel robot, and the specific method comprises the following steps:
1) Analyzing the configuration of the robot, a geometric error model of the robot can be established:
wherein δbE 、ω E Representing the position and attitude errors of the robot end effector,representing a total of 38 uncorrelated geometric errors, can be expressed as:
m is a corresponding error transfer matrix, and represents the influence of geometric errors in epsilon on the position and posture errors of the robot terminal executor, and is a robot driving shaft displacement vector q= [ l ] 1 ，l 2 ，l 3 ，θ C ，θ A ] T Wherein l is a function of 1 、l 2 and l3 Respectively areLength of three branches, θ C and θA Is the angle of rotation of the C-shaped and a-shaped members relative to the initial pose.
2) Determining basic information and preprocessing of a robot, wherein the basic information and preprocessing mainly comprises the following steps:
2-1) determining and discretizing a task space of the robot: taking the 2-dimensional space shown in fig. 3 as an example, where the rectangular range represents the task space of the robot, then the set of diamond points evenly distributed within the rectangular range of fig. 3 can be used as the discretized task space of the robot, where the ellipses represent diamond points between. For a specific implementation of the five-degree-of-freedom hybrid robot, a discrete mode in a 5-dimensional space in fig. 3 is adopted, and the task space after discretization can be expressed as a set pi= { s of displacement vectors of a driving shaft of the robot 1 ，s 2 ，...，s m }。
2-2) determining a measured noise intensity weight of the robot: the pose measurement noise can be determined a priori through a measuring instrument and a measuring scheme in the kinematic calibration of the five-degree-of-freedom hybrid robotIs a symmetric positive definite matrix P, willAs a normalized variance matrix, where P (1, 1) is the value of row 1 and column 1 of matrix P.
2-3) determining pose accuracy weight of the robot: the five-degree-of-freedom hybrid robot has different requirements on position and posture precision, the posture precision weight of the five-degree-of-freedom hybrid robot can be expressed as a diagonal matrix C, and particularly, the ratio of the position and posture precision requirements is r (rad) -1 ) When C may be determined as c=diag (1, r), r may be adjusted according to the robot requirements.
2-4) data preprocessing: for each group of poses in pi, determining the corresponding error transfer matrix set to be pi A ＝{A 1 ，A 2 ，...，A m}, wherein Ai ＝M(s i ) Preprocessing matrix
3) For any group of measurement poses to be evaluated, the measurement poses are expressed as a set of robot drive shaft displacement vectors, Ω= { q 1 ，q 2 ，...，q n }。
4) Determining an evaluation value of the set of measurement poses: based on the measured pose combination omega to be evaluated and the geometric error model, an error identification equation can be determined as follows: delta=m a∈, wherein To be transmitted by error matrix M i ＝M(q i ) Stacking determination, wherein delta is a corresponding pose error measurement value, and the expected mean value of the root mean square value of the pose residual error after the kinematic calibration in the discretization task space can be determined through analysis of statistical knowledge>η is the evaluation value of the set of poses calculated according to the method.
According to the robot measurement pose evaluation method for the kinematic calibration provided by the embodiment of the application, the measurement pose evaluation method considering pose residual errors in the task space after the kinematic calibration based on the set of measurement poses is provided, for a given set of measurement poses and the task space in which the robot needs to guarantee precision, the estimated square value of the root mean square of the pose residual errors in the task space after the kinematic calibration based on the set of measurement poses is expected to be used as a standard for evaluating the set of measurement poses, and corresponding weighted evaluation indexes are given by considering different noise intensities of measurement components and different weights of pose precision. The provided index is different from the previous evaluation index for representing the observability of the kinematic parameter error, is used for representing the amplitude of the residual error after the kinematic calibration in the robot task space, is used for the field of the kinematic calibration of the robot, and aims to optimize the selected measurement pose based on the method to improve the precision of the robot or reduce the number of the measurement poses required by the kinematic calibration. Therefore, the problem that the conventional measuring pose evaluation method focuses on geometric error observability and cannot directly reflect the kinematic calibration effect is solved, so that the measuring pose evaluation and the kinematic calibration are more accurately and intuitively connected, and the amplitude of the residual error after the kinematic calibration in the robot task space is reflected.
Next, a robot measurement pose evaluation device for kinematic calibration according to an embodiment of the present application will be described with reference to the accompanying drawings.
Fig. 4 is a schematic structural view of a robot measurement pose evaluation device for kinematic calibration according to an embodiment of the present application.
As shown in fig. 4, the robot measurement pose evaluation device for kinematic calibration includes: a modeling flat module 100, a calculation module 200, and an evaluation module 300.
The modeling module 100 is configured to build a geometric error model of the robot.
The computing module 200 is configured to determine and discretize a task space of the robot, determine a measured noise intensity weight and a pose accuracy weight of the robot, determine a corresponding error transfer matrix set for each set of poses in the task space of the discretized robot according to the pose accuracy weights, and perform preprocessing on the set of error transfer matrix sets.
The evaluation module 300 is configured to determine an error recognition equation according to the pose to be evaluated and the geometric error model, determine a desired mean value of the root mean square value of the pose residual error after kinematic calibration in the task space of the discretized robot according to the measured noise intensity weight, and evaluate the calibration effect of the robot by using the desired mean value of the root mean square value of the pose residual error.
Optionally, in one embodiment of the present application, the geometric error model is:
wherein ,δbE Is the position omega of the end effector of the robot E Is the attitude error of the robot terminal executor,representing the total n geometrical errors which are not related to each other, M is a corresponding error transfer matrix, and represents the influence of the geometrical errors in E on the position and posture errors of the robot terminal executor as a function of the displacement vector q of the driving shaft of the robot.
Optionally, in one embodiment of the application, for each set of poses in the task space of the discretized robot, determining and preprocessing the respective set of error transfer matrices comprises:
for each group of poses in the task space of the discretized robot, determining a corresponding error transfer matrix set as: pi (II) A ＝{A 1 ，A 2 ，...，A m And (b) wherein A is i ＝M(s i ) Preprocessing matrixC is a matrix corresponding to pose precision weight, s i The displacement vector is the ith driving shaft in the discretization task space, and m is the total pose number in the discretization task space.
Optionally, in one embodiment of the present application, the evaluation module 300 is specifically configured to determine, according to the pose to be evaluated and the geometric error model, an error recognition equation as follows:
δ＝M a ∈
wherein ,M i the delta is a corresponding pose error measurement value;
based on the measured noise intensity weight of the robot, determining an estimated value of the geometric error according to a weighted least square methodWherein W is the inverse of the measurement noise variance matrix;
determining an estimated value and an actual value of a geometric error, and normalizing pose residual errors after kinematic calibration in a task space of the discretized robot wherein ,/>E as geometric error estimate * Is the actual value of the geometric error, C is the matrix corresponding to the pose accuracy weight, A i For the pose s i A corresponding error transfer matrix;
determining root mean square of kinematically calibrated pose residual errors in task space of discretized robot according to estimated values and pose residual errorsWherein m is the total pose number in the discretized task space;
determining the expected mean of the root mean square value from statistical knowledgeWherein A is a pretreatment matrix.
Optionally, in one embodiment of the present application, evaluating the calibration effect of the robot using the expected mean value of the root mean square value of the pose residual includes:
the greater the expected mean value of the root mean square value of the pose residual error is, the poorer the calibration effect of the robot is.
It should be noted that the foregoing explanation of the method embodiment is also applicable to the apparatus of this embodiment, and will not be repeated here.
According to the robot measurement pose evaluation device for kinematic calibration provided by the embodiment of the application, a measurement pose evaluation method considering pose residual errors in a task space after the kinematic calibration based on the set of measurement poses is provided, for a given set of measurement poses and a task space in which the robot needs to guarantee precision, an estimated square value of the root mean square of the pose residual errors in the task space after the kinematic calibration based on the set of measurement poses is expected to be used as a standard for evaluating the set of measurement poses, and corresponding weighted evaluation indexes are given by considering different noise intensities of measurement components and different weights of pose precision. The provided index is different from the previous evaluation index for representing the observability of the kinematic parameter error, is used for representing the amplitude of the residual error after the kinematic calibration in the robot task space, is used for the field of the kinematic calibration of the robot, and aims to optimize the selected measurement pose based on the method to improve the precision of the robot or reduce the number of the measurement poses required by the kinematic calibration. Therefore, the problem that the conventional measuring pose evaluation method focuses on geometric error observability and cannot directly reflect the kinematic calibration effect is solved, so that the measuring pose evaluation and the kinematic calibration are more accurately and intuitively connected, and the amplitude of the residual error after the kinematic calibration in the robot task space is reflected.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.

Claims (6)

1. The robot measurement pose evaluation method for kinematic calibration is characterized by comprising the following steps of:
establishing a geometric error model of the robot;
determining and discretizing a task space of the robot, determining a measured noise intensity weight and a pose precision weight of the robot, determining a corresponding error transfer matrix set according to each set of poses in the discretized task space of the robot and preprocessing the set of error transfer matrices according to the pose precision weights;
determining an error identification equation according to the pose to be evaluated and the geometric error model, determining a root mean square value expected mean value of a pose residual error after kinematic calibration in a task space of the discretized robot according to the measured noise intensity weight, and evaluating the calibration effect of the robot by utilizing the root mean square value expected mean value of the pose residual error;
wherein for each set of poses in the discretized task space of the robot, determining and preprocessing a corresponding set of error transfer matrices comprises: for each group of poses in the discretized task space of the robot, determining a corresponding error transfer matrix set as:, wherein />Preprocessing matrixC is a matrix corresponding to the pose accuracy weight, and is +.>For the ith drive axis in discretized task spaceA motion vector, m is the total pose number in the discretized task space;
determining an error identification equation according to the pose to be evaluated and the geometric error model, determining a root mean square expected mean value of a pose residual error after kinematic calibration in a task space of the discretized robot according to the measured noise intensity weight, wherein the method comprises the following steps:
the determining an error identification equation according to the pose to be evaluated and the geometric error model is as follows:
wherein ,，/>is an error transfer matrix>Measuring the corresponding pose error;
determining an estimated value of the geometric error according to a weighted least square method based on the measured noise intensity weight of the robot； wherein ,/>An inverse matrix of the noise variance matrix is measured;
determining an estimated value and an actual value of the geometric error, and normalizing pose residual errors after kinematic calibration in a discretized task space of the robot； wherein ,/>For geometric error estimate, +.>Is the actual value of the geometrical error +.>Is a matrix corresponding to pose precision weight value, < ->For pose->A corresponding error transfer matrix;
determining the root mean square of the pose residual error after kinematic calibration in the task space of the discretized robot according to the estimated value and the pose residual errorWherein m is the total pose number in the discretized task space;
determining the expected mean of the root mean square value according to statistical knowledgeWherein A is a pretreatment matrix.
2. The method of claim 1, wherein the geometric error model is:
wherein ,for the position of the robot terminal actuator, +.>Executing for a robot terminalPosture error of the device, < >>Representing common->Geometric errors of items independent of each other, +.>Is a corresponding error transfer matrix, representing +.>The influence of the geometrical error on the position and posture error of the robot terminal executor is the displacement vector of the driving shaft of the robot>Is a function of (2).
3. The method of claim 1, wherein evaluating the calibration effect of the robot using the expected mean of the root mean square squared value of the pose residual comprises:
the greater the expected mean value of the root mean square value of the pose residual error is, the poorer the calibration effect of the robot is.
4. A robot measurement pose evaluation device for kinematics calibration is characterized in that includes:
the modeling module is used for establishing a geometric error model of the robot;
the computing module is used for determining and discretizing the task space of the robot, determining the measured noise intensity weight and the pose precision weight of the robot, determining a corresponding error transfer matrix set according to each set of poses in the discretized task space of the robot and preprocessing the set according to the pose precision weight;
the evaluation module is used for determining an error identification equation according to the pose to be evaluated and the geometric error model, determining a root mean square value expected mean value of the pose residual error after kinematic calibration in the task space of the discretized robot according to the measured noise intensity weight, and evaluating the calibration effect of the robot by utilizing the root mean square value expected mean value of the pose residual error;
for each set of poses in the discretized task space of the robot, determining and preprocessing a corresponding set of error transfer matrices comprises:
for each group of poses in the discretized task space of the robot, determining a corresponding error transfer matrix set as:, wherein />Pretreatment matrix->C is a matrix corresponding to the pose accuracy weight, and is +.>The displacement vector is the ith driving shaft in the discretization task space, and m is the total pose number in the discretization task space;
the evaluation module is specifically configured to determine, according to the pose to be evaluated and the geometric error model, an error recognition equation as follows:
wherein ,，/>is an error transfer matrix>Measuring the corresponding pose error;
determining an estimated value of the geometric error according to a weighted least square method based on the measured noise intensity weight of the robot； wherein ,/>An inverse matrix of the noise variance matrix is measured;
determining an estimated value and an actual value of the geometric error, and normalizing pose residual errors after kinematic calibration in a discretized task space of the robot； wherein ,/>For geometric error estimate, +.>Is the actual value of the geometrical error +.>Is a matrix corresponding to pose precision weight value, < ->For pose->A corresponding error transfer matrix;
determining the root mean square of the pose residual error after kinematic calibration in the task space of the discretized robot according to the estimated value and the pose residual errorWherein m is the total pose number in the discretized task space；
Determining the expected mean of the root mean square value according to statistical knowledgeWherein A is a pretreatment matrix.
5. The apparatus of claim 4, wherein the geometric error model is:
wherein ,for the position of the robot terminal actuator, +.>For the attitude error of the robot terminal actuator, < +.>Representing common->Geometric errors of items independent of each other, +.>Is a corresponding error transfer matrix, representing +.>The influence of the geometrical error on the position and posture error of the robot terminal executor is the displacement vector of the driving shaft of the robot>Is a function of (2).
6. The apparatus of claim 4, wherein evaluating the calibration effect of the robot using the expected mean of the root mean square squared value of the pose residual comprises:
the greater the expected mean value of the root mean square value of the pose residual error is, the poorer the calibration effect of the robot is.
CN202110807852.1A 2021-07-16 2021-07-16 Robot measurement pose evaluation method and evaluation device for kinematic calibration Active CN113500585B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110807852.1A CN113500585B (en) 2021-07-16 2021-07-16 Robot measurement pose evaluation method and evaluation device for kinematic calibration

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110807852.1A CN113500585B (en) 2021-07-16 2021-07-16 Robot measurement pose evaluation method and evaluation device for kinematic calibration

Publications (2)

Publication Number Publication Date
CN113500585A CN113500585A (en) 2021-10-15
CN113500585B true CN113500585B (en) 2023-09-05

Family

ID=78013254

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110807852.1A Active CN113500585B (en) 2021-07-16 2021-07-16 Robot measurement pose evaluation method and evaluation device for kinematic calibration

Country Status (1)

Country Link
CN (1) CN113500585B (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4763276A (en) * 1986-03-21 1988-08-09 Actel Partnership Methods for refining original robot command signals
CN107443382A (en) * 2017-09-12 2017-12-08 清华大学 Industrial robot structure parameter error recognizes and compensation method
CN108015808A (en) * 2017-12-07 2018-05-11 天津大学 A kind of Kinematic Calibration method of series-parallel robot
CN110842927A (en) * 2019-11-30 2020-02-28 天津大学 Robot joint geometric error compensation method based on multiple regression
CN110977940A (en) * 2019-11-28 2020-04-10 清华大学 Geometric error modeling method and device for parallel-series robot
CN112197770A (en) * 2020-12-02 2021-01-08 北京欣奕华数字科技有限公司 Robot positioning method and positioning device thereof

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4763276A (en) * 1986-03-21 1988-08-09 Actel Partnership Methods for refining original robot command signals
CN107443382A (en) * 2017-09-12 2017-12-08 清华大学 Industrial robot structure parameter error recognizes and compensation method
CN108015808A (en) * 2017-12-07 2018-05-11 天津大学 A kind of Kinematic Calibration method of series-parallel robot
CN110977940A (en) * 2019-11-28 2020-04-10 清华大学 Geometric error modeling method and device for parallel-series robot
CN110842927A (en) * 2019-11-30 2020-02-28 天津大学 Robot joint geometric error compensation method based on multiple regression
CN112197770A (en) * 2020-12-02 2021-01-08 北京欣奕华数字科技有限公司 Robot positioning method and positioning device thereof

Also Published As

Publication number Publication date
CN113500585A (en) 2021-10-15

Similar Documents

Publication Publication Date Title
CN111259927B (en) Rocket engine fault diagnosis method based on neural network and evidence theory
CN108015808B (en) Kinematics calibration method of series-parallel robot
CN108297093B (en) Step-by-step identification method for mechanical arm dynamic parameters
CN103231375A (en) Industrial robot calibration method based on distance error models
CN110977940B (en) Geometric error modeling method and device for parallel-series robot
CN113459104B (en) Robot Positioning Error Prediction Method and Prediction Device Based on Support Vector Regression
Le et al. Robot manipulator calibration using a model based identification technique and a neural network with the teaching learning-based optimization
CN113500585B (en) Robot measurement pose evaluation method and evaluation device for kinematic calibration
CN111007390B (en) Analog circuit fault diagnosis model based on algebraic method
CN113878581A (en) Error prediction and real-time compensation technology for five-degree-of-freedom hybrid robot
CN113799137B (en) Mobile processing robot rapid visual positioning method based on neural network
CN112207800B (en) Three-degree-of-freedom rotating crank connecting rod parallel platform pose control method
Kerner et al. Parametrization of manual work in automotive assembly for wearable force sensing
Sintov et al. A gripper design algorithm for grasping a set of parts in manufacturing lines
CN108548729A (en) A kind of method and apparatus measuring material maximum stress in bend
Yang et al. Sensitivity analysis of factors affecting motion reliability of manipulator and fault diagnosis based on kernel principal component analysis
CN113334388B (en) Robot kinematics calibration method and calibration device based on local linear regression
CN114030008A (en) Industrial robot practical training energy consumption measuring method based on data driving
CN111323048B (en) Performance test method and system for single relative attitude measurement machine
Shammas et al. Kinematic calibration of serial manipulators using Bayesian inference
Gao et al. Kinematic calibration for articulated arm coordinate measuring machines base on particle swarm optimization
Luo et al. Study of error compensations and sensitivity analysis for 6-Dof serial robot
CN115972260B (en) Industrial robot calibration method and system based on vector inner product error model
Neumayr et al. Precise pose measurement with single camera calibration for planar parallel manipulators
Chen et al. A Highly-Accurate Robot Calibration Method with Line Constraint

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant