Robot system and parameter identification method

Abstract

To easily identify a parameter for modeling.SOLUTION: A robot system comprises: a robot having three or more 3-axis degree of freedom; a control device that operates the robot on the basis of an operation plan; a measurement device that measures a trajectory of the robot when operating the robot on the basis of the operation plan; and a processor that estimates an error in setting a parameter for modeling that is necessary for the control device to calculate a position of a control point of the robot on the basis of the operation plan and the trajectory of the control point to identify the parameter for modeling.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present disclosure relates to a robot system and a parameter identification method. [Background technology]

[0002] Patent Document 1 discloses a machine error derivation method that can uniquely calculate a solution when deriving geometric errors in robots and NC machines that include linear axes, and that can speed up calculations and improve the accuracy of derived errors. The machine error derivation method first models a mechanism with a machining tool attached to its tip using the Denavit-Hartenberg notation, which represents the mechanism using various parameters. The machine error derivation method then measures the position and orientation of the machining tool when the mechanism is operated and estimates the position and orientation of the machining tool when the mechanism is operated using the model. The machine error derivation method then derives the machine error of the mechanism by comparing the measured and estimated values ​​of the position and orientation of the machining tool. Furthermore, the machine error derivation method uses only parameters that change the coordinates of the mechanism's drive axes in the model. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 7-28514 Summary of the Invention [Problem to be solved by the invention]

[0004] The present disclosure has been devised in view of the above-described conventional situation, and aims to easily identify modeling parameters. [Means for solving the problem]

[0005] The present disclosure provides a robot system having a robot with three or more degrees of freedom, a control device that operates the robot based on a motion plan, a measuring device that measures a trajectory of a control point of the robot when the robot operates based on the motion plan, and a processor that estimates setting errors of modeling parameters necessary for the control device to calculate the position of the control point of the robot based on the motion plan and the trajectory of the control point, and identifies the modeling parameters.

[0006] The present disclosure also provides a robot system including: a robot having three or more degrees of freedom; a control device that operates the robot based on a motion plan; a measuring device that measures a trajectory of a control point of the robot when the robot operates based on the motion plan; a processor that estimates a setting error of a modeling parameter based on the motion plan and the trajectory of the control point and identifies the modeling parameter; and a display device, wherein the processor displays the identification results of the modeling parameter on the display device.

[0007] The present disclosure also provides a robot system including: a robot having three or more degrees of freedom; a control device that operates the robot based on a motion plan; a measuring device that measures a trajectory of a control point of the robot when the robot operates based on the motion plan; a processor that estimates a setting error of a modeling parameter based on the motion plan and the trajectory of the control point, and identifies the modeling parameter; and a memory that changes the stored modeling parameter to the modeling parameter identified by the processor.

[0008] The present disclosure also provides a parameter identification method that operates a robot having three or more degrees of freedom based on a motion plan, measures a trajectory of a control point of the robot when the robot operates based on the motion plan, estimates setting errors of modeling parameters necessary to calculate the positions of the control points of the robot based on the motion plan and the trajectories of the control points, and identifies the modeling parameters.

[0009] These comprehensive or specific aspects may be realized as a system, an apparatus, a method, an integrated circuit, a computer program, or a recording medium, or may be realized as any combination of a system, an apparatus, a method, an integrated circuit, a computer program, and a recording medium. [Effects of the Invention]

[0010] According to the present disclosure, modeling parameters can be easily identified. [Brief explanation of the drawings]

[0011] [Figure 1] Robot system configuration diagram [Figure 2] 1 is a flowchart showing an example of a method for identifying modeling parameters. [Figure 3] An example of how to identify modeling parameters [Figure 4] A diagram showing an example of a robot [Figure 5] FIG. 10 is a diagram showing an example of corrected measured control point locus data. [Figure 6A] A diagram showing an example of data of the control point locus for correction calculation. [Figure 6B] A diagram showing an example of data of the control point locus for correction calculation. [Figure 6C] A diagram showing an example of data of the control point locus for correction calculation. [Figure 7] Graph showing the residual sum of squares for each error amount [Figure 8] A graph showing the residual sum of squares for each error amount DETAILED DESCRIPTION OF THE INVENTION

[0012] (Background to this embodiment) To operate a robot accurately and achieve complete offline teaching, it is necessary to identify and, if necessary, calibrate the modeling parameter errors contained in the robot system. A robot system includes a robot, a controller that controls the robot, and sensors that measure the robot's trajectory. Modeling parameters are parameters required for the controller to calculate the robot's control point position. Modeling parameters include, for example, the origin position of each axis of an articulated robot, tool coordinates, tool center point, link length, stiffness value in the rotational direction of each axis, stiffness value in the axis peeling direction, deflection stiffness value, torsional error between axes, or positional relationship error between axes. Tool coordinates are the coordinates of the tool control point in a Cartesian coordinate system with the tip of the articulated robot as the origin.

[0013] The theoretical position information of the control point position of an articulated robot is calculated by a kinematic forward transformation using angle information detected by the encoders of each axis and modeling parameters such as link length information. However, individual differences in modeling parameters such as link length occur due to processing errors and assembly errors of the components of the articulated robot. In order to accurately calculate the control point position of an articulated robot, it is necessary to determine the error in the modeling parameters for each articulated robot and perform calibration. Hereinafter, the error in the modeling parameters will be referred to as the "modeling parameter error."

[0014] The machine error derivation method in Patent Document 1 identifies modeling parameter errors using an error vector between the ideal coordinates of a control point in a robot coordinate system and the actually measured coordinates of the control point. However, measuring the control point positions in the robot coordinate system requires a large-scale measurement system including many sensors, which makes the measurement process complicated. Another problem is that it is difficult to measure the control point positions in the robot coordinate system with high accuracy.

[0015] In view of this situation, the present disclosure provides a robot system that easily identifies modeling parameters.

[0016] Hereinafter, with appropriate reference to the drawings, embodiments specifically disclosing a robot system and a parameter identification method according to the present disclosure will be described in detail. However, more detailed description than necessary may be omitted. For example, detailed descriptions of well-known matters and redundant descriptions of substantially identical configurations may be omitted. This is to avoid unnecessary redundancy in the following description and to facilitate understanding by those skilled in the art. Note that the accompanying drawings and the following description are provided to enable those skilled in the art to fully understand the present disclosure, and are not intended to limit the subject matter recited in the claims.

[0017] First, the system configuration of the robot system 1 will be described with reference to Fig. 1. Fig. 1 is a system configuration diagram of the robot system 1.

[0018] The robot system 1 includes a robot 10, a computing device 20, a trajectory sensor system 30, and a control device 40. The robot system 1 is a system for setting modeling parameters used to calculate the positions of control points of the robot 10 (hereinafter referred to as "control points").

[0019] The robot 10 is, for example, an articulated robot having two or more axes. In FIG. 1, the robot 10 is shown as a robot having six degrees of freedom. The robot 10 is connected to a control device 40 via wire or wireless communication and operates based on control signals from the control device 40. An encoder is attached to each axis of the robot 10, and the robot 10 transmits angle information of each axis detected by the encoder to the control device 40.

[0020] The computing device 20 is a device that estimates modeling parameter errors and identifies modeling parameters. The computing device 20 is, for example, a personal computer or a server device. The computing device 20 includes a memory 21, a processor 22, a communication I / F 26, and a display device 27.

[0021] The memory 21 is configured using, for example, a random access memory (hereinafter referred to as "RAM") and a read only memory (hereinafter referred to as "ROM"), and temporarily stores programs necessary for the operation of the computing device 20, as well as data generated during operation. The RAM is, for example, a work memory used during the operation of the computing device 20. The ROM stores and holds, for example, programs for controlling the computing device 20 in advance. The memory 21 may also store initial values ​​of modeling parameters of the robot system 1.

[0022] The processor 22 is, for example, a central processing unit, a digital signal processor, a graphical processing unit, or a field programmable gate array. The processor 22 functions as a controller that manages the overall operation of the computing device 20. The processor 22 performs control processing for overseeing the operation of each part of the computing device 20, data input / output processing between each part of the computing device 20, data arithmetic processing, and data storage processing. The processor 22 operates according to a program stored in the memory 21. The processor 22 uses the memory 21 during operation, and temporarily stores data generated or acquired by the processor 22 in the memory 21. The processor 22 realizes the functions of the motion plan evaluation unit 23, the identification unit 24, and the identification evaluation unit 25 by using the programs and data stored in the memory 21.

[0023] The motion plan evaluation unit 23 evaluates whether or not the motion plan of the robot 10 when estimating the modeling parameter errors (hereinafter referred to as the "calibrated motion plan") acquired from the control device 40 via the communication I / F 26 satisfies predetermined conditions. The motion plan evaluation unit 23 may acquire the motion plan from the memory 21 or a memory (not shown) that stores the calibrated motion plan. The motion plan evaluation unit 23 may output the results of the evaluated calibrated motion plan to the display device 27.

[0024] The identification unit 24 estimates modeling parameter errors and identifies modeling parameters based on the trajectory of control points (hereinafter referred to as the "measured control point trajectory") acquired from the trajectory sensor system 30 via the communication I / F 26 and the calibration operation plan acquired from the control device 40 via the communication I / F 26. The identification unit 24 outputs the identified modeling parameters to the control device 40 via the communication I / F 26.

[0025] The identification evaluation unit 25 evaluates the likelihood of the modeling parameters identified by the identification unit 24. The identification evaluation unit 25 may output the results of the evaluated modeling parameters to the display device 27.

[0026] The communication I / F 26 is an interface circuit that performs wireless or wired communication between the calculation device 20 and the trajectory sensor system 30, and between the calculation device 20 and the control device 40. The communication method used by the communication I / F 26 is wired communication or wireless communication. Specifically, the wireless communication may be mobile communication such as WAN, LAN, LTE, 4G, or 5G, power line communication, short-range wireless communication (e.g., Bluetooth (registered trademark) communication), or communication for mobile phones. Note that the wireless communication method is not limited to the above-mentioned examples.

[0027] The display device 27 is a device that displays the identified modeling parameters, the evaluation results of the calibration operation plan, the evaluation results of the modeling parameters, etc. The display device 27 is, for example, a display.

[0028] The trajectory sensor system 30 is a system that measures the measured control point trajectory of the robot 10. The trajectory sensor system 30 is, for example, a three-dimensional laser or stereo camera. The trajectory sensor system 30 outputs the measured measured control point trajectory of the robot 10 to the identification unit 24 of the computing device 20.

[0029] The control device 40 is a controller that controls the control point positions of the robot 10 based on the calibrated motion plan. The control device 40 stores the calibrated motion plan in a memory (not shown) or the like. The control device 40 also stores the initial values ​​of the modeling parameters in a memory (not shown) or the like. The calculation device 20 and the control device 40 may be configured as an integrated device. The control device 40 controls the robot 10 using the modeling parameters identified by the calculation device 20. In other words, the control device 40 rewrites the modeling parameters that have been stored in advance with the modeling parameters identified by the calculation device 20.

[0030] Next, an example of a method for identifying modeling parameters will be described with reference to Fig. 2. Fig. 2 is a flowchart showing an example of a method for identifying modeling parameters. Note that Fig. 2 is limited to explaining the flow of the method for identifying modeling parameters, and the detailed contents of each process will be described later with reference to Figs. 4 to 8.

[0031] The identification unit 24 acquires a measured control point trajectory, which is a trajectory of the control points of the robot 10 that has been measured (step St101).

[0032] The motion plan evaluation unit 23 acquires the calibrated motion plan from the control device 40 (step St102).

[0033] The motion plan evaluation unit 23 determines whether or not the calibrated motion plan acquired in the process of step St102 satisfies predetermined conditions (step St103). Specific examples of the predetermined conditions will be described later.

[0034] When the motion plan evaluation unit 23 determines that the calibrated motion plan does not satisfy the predetermined condition (step St103, NO), it ends the process related to the identification of the modeling parameters.

[0035] When the motion plan evaluation unit 23 determines that the calibrated motion plan satisfies the predetermined conditions (step St103, YES), it outputs the calibrated motion plan to the identification unit 24. Note that the processing of step St101 may be executed after the processing of steps St102 and St103. In this case, the processing of step St101 is executed when the motion plan evaluation unit 23 determines that the calibrated motion plan satisfies the predetermined conditions.

[0036] The identification unit 24 acquires the calibration operation plan from the control device 40. The identification unit 24 estimates modeling parameter errors and identifies modeling parameters using the measured control point trajectory acquired in the processing of step St101 and the calibration operation plan (step St104). The identification unit 24 outputs the identified modeling parameters to the identification evaluation unit 25.

[0037] The identification evaluation unit 25 evaluates the reliability of the modeling parameters identified in the process of step St104 (step St105).

[0038] The identification evaluation unit 25 determines whether or not the result of evaluation in the process of step St105 satisfies a predetermined standard (step St106).

[0039] If the identification evaluation unit 25 determines that the evaluation result in the processing of step St105 does not satisfy the predetermined criterion (step St106, NO), it causes the identification unit 24 to execute the processing of step St101 again. Note that if the identification evaluation unit 25 determines that the evaluation result does not satisfy the predetermined criterion (step St106, NO), it may cause the identification unit 24 to execute the processing of step St101 again and display the evaluation result on the display device 27. Furthermore, if the identification evaluation unit 25 determines that the evaluation result does not satisfy the criterion a predetermined number of times, it may display the evaluation result and a signal indicating that the modeling parameters could not be identified on the display device 27 and end the processing.

[0040] If the identification evaluation unit 25 determines that the evaluation result in the processing of step St105 satisfies the predetermined criterion (step St106, YES), it displays the evaluation result on the display device 27 (step St107). Furthermore, if the identification evaluation unit 25 determines that the evaluation result in the processing of step St105 satisfies the predetermined criterion (step St106, YES), it outputs a signal indicating that the identified modeling parameters are highly reliable to the identification unit 24. The high reliability of the modeling parameters means that, when the control device 40 controls the control points of the robot 10 using the identified modeling parameters, the trajectory specified by the control device 40 matches the measured control point trajectory of the robot 10 with high accuracy. In other words, this means that there is a high degree of similarity between the trajectory specified by the control device 40 and the measured control point trajectory of the robot 10. Based on the evaluation result indicating high reliability received from the motion plan evaluation unit 23, the identification unit 24 outputs the identified modeling parameters to the control device 40 to reflect them in the motion of the robot 10. That is, the control device 40 controls the robot 10 with a motion plan that incorporates the identified modeling parameters.

[0041] The identification unit 24 outputs the identified modeling parameters to the memory 21, and changes the pre-stored modeling parameters to the identified modeling parameters (step St108). The memory 21 changes the pre-stored modeling parameters to the identified modeling parameters received from the identification unit 24.

[0042] At least one of the processes in steps St102, St103, St105, St106, St107, and St108 may be omitted.

[0043] Furthermore, the calculation device 20 transmits the modeling parameters identified in the processing of step St104 to the control device 40. The control device 40 rewrites the pre-stored modeling parameters with the modeling parameters received from the calculation device 20. The control device 40 controls the robot 10 using the rewritten modeling parameters. As a result, by controlling the robot 10 using the identified modeling parameters, the control device 40 can control the robot 10 with higher accuracy than when controlling the robot 10 using the modeling parameters before identification.

[0044] As described above, the robot system 1 can identify various modeling parameters collectively. Furthermore, the robot system 1 can evaluate the reliability of the identified modeling parameters, and if the reliability is low, can re-identify the modeling parameters using the measured control point trajectory of the robot 10 operated along a different trajectory. This allows the robot system 1 to identify the modeling parameters with high accuracy. Furthermore, when the robot system 1 identifies the modeling parameters, it can change the values ​​of the modeling parameters stored in the memory 21. This allows the robot system 1 to control the control point positions of the robot 10 with high accuracy using the identified modeling parameters. Furthermore, the robot system 1 can display the identification results on the display device 27 and notify the results to the user who calibrates the robot 10. This allows the robot system 1 to help the user recognize whether the identification results are reliable and improve the user's calibration workability.

[0045] Next, an example of a method for identifying modeling parameters will be outlined with reference to Fig. 3. Fig. 3 is an image diagram of an example of a method for identifying modeling parameters.

[0046] The basic principle of identifying modeling parameters will be explained below. The actual control point locus measured in a coordinate system parallel to the coordinate system set in the robot 10 (hereinafter referred to as the "robot coordinate system") is denoted as Sr.

[0047] If the error amount of each of n (n: integer equal to or greater than 1) modeling parameters to be identified is e (e1, ..., ei, ..., en, i = 1 to n), the control point trajectory in the robot coordinate system obtained by simulation analysis using modeling parameters including the error amount e (hereinafter referred to as the "calculated control point trajectory") is Sn.

[0048] The error amount e of the modeling parameters included in the robot system 1 is identified by finding the combination of the error amount e of the modeling parameters when the shapes of the trajectories of the actually measured control point trajectory Sr and the calculated control point trajectory Sn are closest to each other.

[0049] Graphs Sim1, Sim2, Sim3, and Sim4 are calculated control point loci Sn obtained by simulation analysis by the identification unit 24. Graphs Sim1, Sim2, Sim3, and Sim4 are simulation analysis results when each contains a different error amount e. The identification unit 24 arbitrarily sets the error amount e and calculates calculated control point loci Sn, which are multiple simulation analysis results.

[0050] The graph Gr is the measured control point locus Sr.

[0051] In graphs Sim1, Sim2, Sim3, Sim4, and Gr, the horizontal axis represents time and the vertical axis represents coordinates (i.e., the position of the control point). The three lines shown in graphs Sim1, Sim2, Sim3, Sim4, and Gr represent the time changes of the X, Y, and Z coordinates of the three-dimensional coordinate system.

[0052] The identification unit 24 compares the graphs Sim1, Sim2, Sim3, and Sim4 with the graph Gr and selects a calculated control point trajectory Sn that is the trajectory most similar to the graph Gr. The identification unit 24 estimates the error amount e used in calculating the selected calculated control point trajectory Sn as the modeling parameter error. Note that a detailed method for calculating the modeling parameter error will be described later. The identification unit 24 identifies the modeling parameters based on the determined modeling parameter error.

[0053] (Example of a method for calculating modeling parameter errors) Next, an example of a method for estimating modeling parameter errors in this embodiment will be described with reference to Figs. 4 to 8. Fig. 4 is a diagram showing an example of a robot 10. Fig. 5 is a diagram showing an example of data on a corrected measured control point trajectory. Figs. 6A, 6B, and 6C are diagrams showing an example of data on a corrected calculated control point trajectory. Fig. 7 is a graph showing the residual sum of squares of each error amount. Fig. 8 is a diagram showing the numerical value of the residual sum of squares of each error amount.

[0054] The robot 10 shown in Figure 4 is an articulated robot with six degrees of freedom. The robot 10 has a control point 11 at its tip. The numerical values ​​shown on the robot 10 represent the lengths between the arrows and are measured in millimeters. Note that the lengths between the axes of the robot 10 shown in Figure 4 are merely examples and are not limiting. The axis parallel to the direction of gravity of the robot 10 is the Z-axis. The direction perpendicular to the Z-axis and extending from the installation point of the robot on the ground toward the control point 11 is the Y-axis. The axis perpendicular to the Z-axis and the Y-axis is the X-axis. These directional expressions are used for convenience of explanation and are not intended to limit the posture of the structure during actual use.

[0055] First, a description will be given of a method for measuring the measured control point trajectory corresponding to the processing of step St101 shown in Fig. 2. The trajectory sensor system 30 is installed at an appropriate position relative to the robot 10 to measure the measured control point trajectory.

[0056] The control device 40 transmits the calibrated motion plan to the robot 10 and operates the robot 10. The trajectory sensor system 30 measures the trajectory of the control points of the robot 10 operating according to the calibrated motion plan. The trajectory sensor system 30 transmits the measured trajectory to the calculation device 20 as the measured control point trajectory Sr.

[0057] Here, the measured control point trajectory Sr needs to be measured in a coordinate system parallel to the robot coordinate system. For this reason, the above-mentioned condition may be ensured by arranging the coordinate system of the trajectory sensor system 30 so that it is parallel to the robot coordinate system. Alternatively, the coordinate system of the trajectory sensor system 30 may be appropriately arranged, and the computing device 20 may determine a correspondence relationship between the coordinate system parallel to the robot coordinate system and the sensor coordinate system, and convert the measured control point trajectory based on the correspondence relationship to calculate the measured control point trajectory Sr, thereby ensuring the above-mentioned condition.

[0058] The measured control point trajectory Sr is a series of control point position information at each measurement posture acquired in a coordinate system parallel to the robot coordinate system, and may be three-dimensional information, or only one of the three dimensions may be used. When the measured control point trajectory Sr is three-dimensional position information at each measurement posture, the formula becomes Equation (1).

[0059]

number

[0060] Here, l represents the number of measurement posture identifications (hereinafter referred to as "measurement posture IDs") and is an integer of 1 or greater.

[0061] In this way, the calculation device 20 obtains the measured control point trajectory Sr.

[0062] Next, a description will be given of an example of a method for identifying modeling parameters corresponding to the processing of step St104 shown in Fig. 2. Note that the content of the processing of step St104 is not limited to the example described below.

[0063] First, the identification unit 24 sets the combination of modeling parameters of the robot 10 as E (E1, E2, . . . , Ei, . . . , En), and sets the combination of the error amounts of each modeling parameter as e i (e i1 ,e i2 ,…,e ij ,…,e im, j=1 to m, m: number of combinations). E is set arbitrarily according to the robot 10. For example, when the tool offset parameters of the robot 10 are to be identified, E=(X component of the tool offset parameter, Y component of the tool offset parameter, Z component of the tool offset parameter) and n=3. Here, the tool offset parameters are the positions of the tool action point and tool control point of the robot 10 relative to the coordinates of the tip axis of the robot 10. E and e do not need to be determined after measuring the measured control point trajectory Sr, but may be determined in advance.

[0064] Next, the identification unit 24 selects an arbitrary combination of error amounts e i The calculated control point trajectory is calculated by numerical calculation from the calibrated motion plan assuming that the robot 10 is included in the control point trajectory Sr. The initial position is set to arbitrary coordinates (X0, Y0, Z o ) are defined as the corrected measured control point locus Sr' and the corrected calculated control point locus Sn'. The calculated control point locus Sn is expressed by equation (2), the corrected measured control point locus Sr' is expressed by equation (3), and the corrected calculated control point locus Sn' is expressed by equation (4).

[0065]

number

[0066]

number

[0067]

number

[0068] In addition, the calculated control point trajectory Sn and the corrected calculated control point trajectory Sn' do not need to be calculated after measuring the measured control point trajectory Sr, and can be calculated if the calibration operation plan or setting values ​​of the robot 10, which are the conditions for obtaining the measured control point trajectory Sr, are known in advance, so they may be obtained in advance.

[0069] Next, the similar locus Sn' that is most similar to the corrected measured control point locus Sr' is calculated. MostSimilar Search for similar trajectories Sn' MostSimilar The combination of error quantities used to calculate e MostSimilar A method for estimating the error of the modeling parameter is described below. The similarity between trajectories can be evaluated using, for example, cosine similarity, correlation coefficient, or residual sum of squares. MostSimilar As a method for obtaining the above, a full search method will be described as an example.

[0070] The identification unit 24 sets a combination e of the error amounts at an appropriate interval within the expected range for each element of E. For example, in the case of the X component of the tool offset parameter, if the expected range of the error amount is ±0.2 [mm] and the appropriate interval is 0.01 [mm], the combination e of the error amounts will be [-0.2, -0.19, ..., 0.18, 0.19, 0.2]. The combination e of the error amounts for each element of E i The similarity between the corrected measured control point locus Sr' and the corrected calculated control point locus Sn' calculated for (i = 1 to m) is evaluated, and the corrected calculated control point locus Sn' with the highest similarity is called the similar locus Sn'. MostSimilar The combination of error amounts e used in calculating the corrected calculation control point locus Sn' with the highest similarity is selected as e MostSimilar It is decided that:

[0071] As a result of the above, the identification unit 24 can estimate the modeling parameter errors and identify the modeling parameters, which is the processing of step St104 shown in FIG.

[0072] Next, a method for evaluating the similarity between the corrected measured control point locus Sr' and the corrected calculated control point locus Sn' using the sum of squared residuals will be described. The method for evaluating the similarity between the corrected measured control point locus Sr' and the corrected calculated control point locus Sn' described below is an example of the processing of step St105 shown in FIG.

[0073] In the robot 10 shown in Figure 4, the tool offset setting value is [185,0,0]. [185,0,0] represents the X, Y, and Z coordinate values ​​in the tool coordinate system, measured in millimeters. The calibration operation plan fixes the control point 11 to its initial value [0,1150,265], and specifies a so-called pivot operation in which the tool angle is rotated 140 degrees around the Y axis from the initial tool orientation, where the vector from the tool coordinate origin to the control point is equal to the negative direction of the Z axis. The actual control point trajectory Sr is set to only the Y component of the three-dimensional coordinate system, X, Y, and Z, and is set to (X0,Y0,Z0) = (0,0,0).

[0074] The graph shown in Figure 5 is the corrected measured control point trajectory Sr' when the robot 10 performs an operation in accordance with the calibrated operation plan. The vertical axis represents the displacement [mm] from the start point of the trajectory, since (X0, Y0, Z0) = (0, 0, 0). The horizontal axis represents the measured posture ID linked to the posture information of the robot 10 when each piece of data was measured on the measured control point trajectory Sr. In the graph shown in Figure 5, the difference in the amount of rotation around the Y axis between each piece of data is set to be at equal intervals.

[0075] Furthermore, when the robot 10 performs the pivot operation described above, it is expected that modeling parameter errors exist only in the X and Z components of the tool offset parameters. Let the error amount of the X component of the tool offset parameters be Tx and the error amount of the Z component be Tz. If the error amount Tx is [-0.1, 0, 0.1] and the error amount Tz is [-0.1, 0, 0.1], there are 9 error amount combinations e (m=9), or 3 × 3 = 9. The graphs shown in Figures 6A, 6B, and 6C represent the corrected calculation control point trajectories Sn' calculated when the error amount combinations e1 to e9 are included in the robot system 1. The horizontal axes of the graphs shown in Figures 6A, 6B, and 6C represent the measured posture ID, the same as the measured control point trajectory Sr, and each component is only the Y component of the XYZ components, just like the measured control point trajectory Sr. The titles of the graphs in Figures 6A, 6B, and 6C represent the values ​​of the error amount Tx and the error amount Tz, respectively. For example, "Tx-0.1 Tz-0.1" in Fig. 6A indicates that the error amount Tx is -0.1 and the error amount Tz is -0.1. Each graph represents the corrected calculation control point locus Sn' when the error amount Tx and error amount Tz shown in the title are set.

[0076] 7 shows the residual sum of squares for each combination of the values ​​of the error amounts Tx and Tz. The residual sum of squares is calculated using equation (5).

[0077]

number

[0078] When evaluating the similarity using the residual sum of squares, the identification unit 24 determines that the trajectory with the smallest evaluation value has the highest similarity. As shown in Fig. 8, in the example of this embodiment, when the error amount Tx is 0.1 and the error amount Tz is -0.1, the residual sum of squares is 0.03, which is the smallest. As a result, the identification unit 24 estimates that the error amount Tx of the tool offset parameter is 0.1 [mm] and the error amount Tz is -0.1 [mm] as the modeling parameter errors.

[0079] The identification evaluation unit 25 may determine whether the estimation result satisfies the criteria by using, for example, a preset threshold value for the value of the residual sum of squares of the estimated modeling parameter errors. For example, if the value of the residual sum of squares of the estimated modeling parameter errors is equal to or greater than the threshold value, the identification evaluation unit 25 determines that the estimation result does not satisfy the criteria. Alternatively, if the value of the residual sum of squares of the estimated modeling parameter errors is less than the threshold value, the identification evaluation unit 25 determines that the estimation result satisfies the criteria.

[0080] (Evaluation of the calibration motion plan) Next, we will explain the conditions used by the motion plan evaluation unit 23 when evaluating whether the calibrated motion plan is an appropriate motion plan for identifying modeling parameters in the processing of step St103 in Fig. 2. By making the calibrated motion plan a motion plan that satisfies the three conditions described below, the calculation device 20 can calculate an identification result of modeling parameters that is easier and more reliable.

[0081] First, the independence condition can be mentioned as the first condition. The independence condition is a condition that the calculated control point trajectories Sn are dissimilar to each other. If the calibration operation plan satisfies the independence condition, the calculation device 20 can obtain highly reliable modeling parameter identification results. For example, when the calculation device 20 estimates modeling parameter errors and identifies modeling parameters using the above-mentioned exhaustive search method, consider a case in which the calculated control point trajectories Sn1 and Sn2 are highly similar to each other among the calculated control point trajectories Sn numerically calculated from all error amount combinations e. If the measured control point trajectory Sr is highly similar to the two trajectories, the calculated control point trajectories Sn1 and Sn2, it becomes difficult to determine which is the true identification result. For this reason, to increase the reliability of the modeling parameter identification results, the calibration operation plan must satisfy the independence condition. If the calibration operation plan satisfies the independence condition, the obtained identification results are highly reliable, and the calculation device 20 can eliminate the process of verifying whether the identification results are correct.

[0082] Here, a specific example of a method for evaluating the independence condition will be described. For example, when estimating modeling parameter errors and identifying modeling parameters using an exhaustive search method, the calculation device 20 numerically calculates a calculated control point trajectory Sn for all possible combinations e of error amounts. The motion plan evaluation unit 23 of the calculation device 20 calculates the similarity of each calculated control point trajectory Sn and determines whether all evaluation values ​​are equal to or less than a predetermined reference value. The reference value is a value set depending on the identification accuracy or the modeling parameters of the identification target, and is a value that allows the identification result to be uniquely determined. This allows the calculation device 20 to determine whether the calibrated motion plan satisfies the independence condition.

[0083] Next, the second condition is a no-mechanical-play condition. The no-mechanical-play condition is a condition in which factors such as backlash, lost motion, or other specific mechanical play or friction of a reducer used in the robot 10 do not affect the trajectory of the control point while the robot 10 is executing an operation according to the calibrated motion plan. If the calibrated motion plan satisfies the no-mechanical-play condition, the calculation device 20 does not need to model the above-mentioned influences when estimating the modeling parameter errors and take them into account in the numerical calculation, making it easy to perform the estimation. This is because it is difficult to accurately numerically calculate the influence of backlash, lost motion, or other specific mechanical play or friction on the trajectory of the control point.

[0084] Here, a specific example of a method for evaluating the no-play condition will be described. Backlash or lost motion occurs when the direction of torque acting on a mechanical component reverses in the direction of backlash or lost motion. When the robot 10 executes an operation according to the calibrated motion plan, the calculation device 20 uses numerical calculations to determine the torque acting on a mechanical component that may be susceptible to backlash or lost motion. If the torque acting direction does not reverse during the robot 10's operation, the calculation device 20 determines that the no-play condition is evaluated as good (i.e., the no-play condition is met). If the torque acting direction reverses, the calculation device 20 determines that the no-play condition is evaluated as bad (i.e., the no-play condition is not met). The same applies to other specific mechanical backlash or friction. If the torque or force acting on a mechanical component reverses in the direction that causes backlash or friction, backlash or friction occurs. When the robot 10 executes an operation according to the calibrated motion plan, the calculation device 20 uses numerical calculations to determine the force or torque acting on the mechanical component. The calculation device 20 determines that the no mechanical play condition is evaluated as good if the direction of torque or force application does not reverse while the robot 10 is operating, and determines that the no mechanical play condition is evaluated as bad if the direction of torque application reverses.

[0085] Next, the third condition is a low acceleration condition. The low acceleration condition is a condition in which the influence of the dynamic deflection of the robot 10 that occurs when the robot 10 accelerates or decelerates is such that it does not affect the accuracy of estimating the modeling parameter error. When the calibrated motion plan satisfies the low acceleration condition, the calculation device 20 does not need to model the influence and consider it in the numerical calculation when estimating the modeling parameter error, and therefore identification can be easily performed. This is because it is difficult to accurately numerically calculate the influence of the dynamic deflection that accompanies acceleration and deceleration on the control point trajectory.

[0086] (Method for evaluating the results of model parameter identification) Next, a method for evaluating the reliability of the identification result of the modeling parameters will be described, which is an example of the process of step St105 in Fig. 2. As a method for evaluating the identification result of the modeling parameters, MostSimilar One example is a method for determining whether the similarity between the corrected measured control point trajectory Sr′ satisfies a predetermined criterion. The predetermined criterion depends on the method for calculating the similarity or the identification accuracy of the desired modeling parameters, and is therefore appropriately determined according to the identification conditions. For example, a predetermined threshold value is used as the predetermined criterion. If the identification evaluation unit 25 determines that the similarity satisfies the predetermined criterion, i.e., that the similarity is equal to or greater than the threshold, the control point trajectory can be explained by a model that takes into account the modeling parameter errors of the calibration target in the reference model, and the reliability of the identification result can be evaluated as high. On the other hand, if the similarity does not satisfy the predetermined criterion, i.e., that the identification evaluation unit 25 determines that the similarity is less than the threshold, it is possible that the robot 10 contains modeling parameter errors not subject to calibration, or that the control point trajectory was not correctly acquired, and the reliability of the identification result can be evaluated as low. Note that the method for evaluating the identification results of the modeling parameters is not limited to the above example.

[0087] (Robot calibration operation) The trajectory sensor system 30 must measure the measured control point trajectory in a sensor coordinate system that is parallel to the robot coordinate system. To achieve this parallel relationship between the robot coordinate system and the sensor coordinate system, the sensor coordinate system of the trajectory sensor system 30 must be positioned parallel to the robot coordinate system, or a coordinate transformation matrix must be calculated to convert the sensor coordinate system to the robot coordinate system. However, if there is an angular misalignment in the parallel relationship, the shapes of the measured control point trajectory and the calculated control point trajectory may differ, potentially reducing the accuracy of the estimation of modeling parameter errors. Furthermore, it is difficult to accurately calculate a coordinate transformation matrix that converts from the sensor coordinate system to the robot coordinate system. Here, the robot system 1 uses a calibration motion plan in which the posture of the robot 10 changes while the three-dimensional position of the control point of the robot 10 is only slightly displaced. This allows the robot system 1 to measure the measured control point trajectory while minimizing the effect of slight angular misalignment in the parallel relationship between the sensor coordinate system and the robot coordinate system. For example, the calibration motion plan may involve a motion plan in which the displacement of the control point of the robot 10 from its initial position is 300 mm or less. When measuring the control point trajectory of the robot 10 executing a calibration plan operation in the robot coordinate system, small displacements of the trajectory from the initial position can reduce coordinate transformation errors, resulting in appropriate identification accuracy of the modeling parameters. Experiments have shown that, although this depends on the desired identification accuracy, the robot's characteristics, and the type of modeling parameters to be identified, appropriate identification accuracy cannot be achieved if the control point trajectory contains an error of 0.05 mm or more. Furthermore, when measuring the robot coordinate system by actually operating the robot, experimental results have shown that coordinate transformation errors of approximately 0.01° occur in uncalibrated robots. Furthermore, assuming a coordinate transformation error of 0.01°, when a calibration plan is measured for a movement in which the control points are displaced 300 mm or less from their initial positions, it can be calculated that the measured trajectory contains an error of up to approximately 0.05 mm. Based on the above, when a calibration plan is for a movement in which the control points are displaced 300 mm from their initial positions, the robot system 1 is more likely to achieve appropriate identification accuracy.As a result, the robot system 1 does not require a large-scale measuring device because the displacement of the control points is small, and the actual control point trajectories can be measured with a small-scale measuring device. Furthermore, as a calibration planning operation, the displacement of the three-dimensional position of the control points of the robot 10 is kept to 0.05 mm or less, which is about the same as the calculation error when calculating each axis angle plan from the motion plan, and by using so-called pivot operation, which is a motion plan in which the control points are not moved intentionally, the impact of errors in the parallel relationship between the sensor coordinate system and the robot coordinate system on the identification of modeling parameters can be further reduced.

[0088] (Summary of this embodiment) The above description of the present embodiment discloses the following techniques.

[0089] <Technology 1> The robot system according to this embodiment includes a robot having three or more degrees of freedom, a control device that operates the robot based on a motion plan, a measuring device that measures the trajectory of the control point of the robot when the robot operates based on the motion plan, and a processor that estimates setting errors of modeling parameters necessary for the control device to calculate the position of the robot's control point based on the motion plan and the trajectory of the control point, and identifies the modeling parameters.

[0090] As a result, by using the trajectory of the control points, the robot system according to this embodiment does not need to align the origins of the coordinate system set in the robot and the coordinate system set in the measuring device, and can estimate the setting error of the modeling parameters and identify the modeling parameters simply by achieving parallelism. As a result, the robot system does not require a large-scale device to measure the trajectory of the control points, and can easily identify the modeling parameters.

[0091] <Technology 2> In the robot system described in Technology 1, the processor obtains a plurality of calculated control point trajectories by calculating the trajectories of control points using the motion plan and modeling parameters including any setting error, calculates the similarity between each of the calculated control point trajectories and the trajectories of control points measured by a measurement device, and estimates the setting error used to calculate the calculated control point trajectory with the highest similarity as the setting error of the modeling parameters.

[0092] As a result, the robot system according to this embodiment can estimate the setting errors of various modeling parameters all at once.

[0093] <Technology 3> In the robot system described in Technology 1 or Technology 2, the processor evaluates the reliability of the identified modeling parameters based on a determination of whether the similarity is equal to or greater than a threshold, and when it determines that the similarity is less than the threshold, causes the measurement device to measure the trajectory of the control points again, and estimates the setting error of the modeling parameters based on the operation plan and the trajectory of the control points measured again, thereby identifying the modeling parameters.

[0094] As a result, the robot system according to this embodiment can evaluate the reliability of the identified modeling parameters, and if the reliability is low, measure the trajectories of the control points again and identify the modeling parameters. In other words, the robot system can identify modeling parameters whose reliability meets a predetermined standard. This allows the robot system to calibrate the robot with high accuracy.

[0095] <Technology 4> The robot system according to any one of Techniques 1 to 3 further includes a display device, and when the processor determines that the similarity is equal to or greater than a threshold, the processor displays the evaluation result on the display device.

[0096] As a result, the robot system according to this embodiment can notify the user or the like who manages the robot of the results of the reliability of the identified modeling parameters. In other words, the robot system can support the user in the task of calibrating the robot.

[0097] <Technology 5> In the robot system according to any one of Techniques 1 to 4, the control device changes the operation of controlling the robot based on the setting error of the modeling parameter.

[0098] As a result, the robot system according to this embodiment can control the position of the robot with high precision by using the identified modeling parameters.

[0099] <Technology 6> In the robot system according to any one of Techniques 1 to 5, the processor rewrites the modeling parameters of the control device with the identified modeling parameters.

[0100] As a result, the robot system according to this embodiment can control the robot with high precision by using the identified modeling parameters.

[0101] <Technology 7> In the robot system according to any one of Techniques 1 to 6, the motion plan is characterized in that the displacement of the control point from the initial position is 300 mm or less.

[0102] As a result, the robot system according to this embodiment can measure the positions of the control points using a small-scale measuring device by using a motion plan that minimizes displacement of the control points, and thus the robot system can easily determine the setting errors of the modeling parameters using a small-scale measuring device without requiring a large-scale measuring device.

[0103] <Technology 8> In the robot system according to any one of Techniques 1 to 7, the motion plan is a motion plan in which the posture of the robot changes in a state in which the displacement of the three-dimensional position of the control point is 0.05 mm or less.

[0104] As a result, in the robot system according to this embodiment, the displacement of the control point position is extremely small, at 0.05 mm or less, and this reduces the impact that errors in the parallel relationship between the coordinate system set in the measuring device and the coordinate system set in the robot have on the identification of the setting errors of the modeling parameters. This enables the robot system to estimate the modeling parameter setting errors with high accuracy and identify the modeling parameters.

[0105] <Technology 9> In the robot system according to any one of Techniques 1 to 8, the processor determines whether or not the calculated control point trajectories are motion plans that satisfy a condition that they are not similar to each other.

[0106] As a result, the robot system according to this embodiment can uniquely estimate the setting error of the modeling parameters and identify the modeling parameters, thereby obtaining highly reliable identification results.

[0107] <Technology 10> In the robot system according to any one of Techniques 1 to 9, the modeling parameters are any combination of the origin of each axis, the tool offset, the stiffness value in the rotation direction of each axis, the stiffness value in the axis peeling direction, the torsional error between axes, the positional relationship error between axes, and the link length.

[0108] As a result, the robot system according to this embodiment can collectively identify setting errors of various modeling parameters.

[0109] <Technology 11> In the robot system described in any one of Techniques 1 to 10, when the control device causes the robot to operate based on the operation plan and then causes the robot to operate again based on the operation plan, the control device causes the robot to operate in a manner different from the robot's previous behavior.

[0110] As a result, when re-identifying the setting errors of the modeling parameters, the robot system according to this embodiment can use data on the trajectory of the control points that is different from the immediately preceding behavior of the robot. In other words, the robot system can estimate the setting errors of the modeling parameters with high accuracy and identify the modeling parameters.

[0111] Although the embodiments have been described above with reference to the accompanying drawings, the present disclosure is not limited to such examples. It is clear that a person skilled in the art can conceive of various modifications, alterations, substitutions, additions, deletions, and equivalents within the scope of the claims, and it is understood that these also fall within the technical scope of the present disclosure. Furthermore, the components in the above-described embodiments may be combined in any manner without departing from the spirit of the invention. [Industrial Applicability]

[0112] The techniques of the present disclosure are useful as a robot system that easily identifies modeling parameters and as a parameter error estimation method. [Explanation of symbols]

[0113] 1. Robot System 10. Robot 20 Computing equipment 21 Memory 22 processors 23 Motion planning evaluation unit 24 Identification section 25 Identification and Evaluation Unit 26 Communication I / F 27 Display Devices 30 Trajectory Sensor System 40 Control device

Claims

1. A robot having three or more degrees of freedom; A control device that operates the robot based on a motion plan; a measurement device for measuring a trajectory of a control point of the robot when the robot moves based on the motion plan; A processor that estimates setting errors of modeling parameters necessary for the control device to calculate the positions of the control points of the robot based on the motion plan and the trajectory of the control points, and identifies the modeling parameters. Robot system.

2. The processor, A plurality of calculated control point trajectories are obtained by calculating the trajectories of the control points using the motion plan and modeling parameters including any set error; calculating a similarity between the locus of control points measured by the measurement device and each of the calculated loci of control points; A setting error used to calculate the calculated control point locus having the highest similarity is estimated as a setting error of the modeling parameters. The robot system of claim 1 .

3. The processor, evaluating the reliability of the identified modeling parameters based on a determination of whether the similarity is equal to or greater than a threshold; if it is determined that the degree of similarity is less than a threshold, causing the measurement device to measure the locus of the control points again; estimating setting errors of the modeling parameters based on the motion plan and the trajectory of the control points measured again, and identifying the modeling parameters; The robot system according to claim 2 .

4. Further comprising a display device, When the processor determines that the similarity is equal to or greater than a threshold, the processor displays an evaluation result on the display device. The robot system according to claim 3 .

5. The control device changes an operation of controlling the robot based on a setting error of the modeling parameter. The robot system of claim 1 .

6. The processor rewrites modeling parameters of the control device to the identified modeling parameters. The robot system of claim 1 .

7. The motion plan is characterized in that the displacement of the control point from an initial position is 300 mm or less. The robot system of claim 1 .

8. The motion plan is a motion plan in which the posture of the robot changes in a state in which the displacement of the three-dimensional position of the control point is 0.05 mm or less. The robot system according to claim 7.

9. The processor determines whether the calculated control point trajectories are motion plans that satisfy a condition that the calculated control point trajectories are dissimilar to each other. The robot system according to claim 2 .

10. The modeling parameters are any combination of the origin of each axis, a tool offset, a rigidity value in the rotation direction of each axis, a rigidity value in the axis peeling direction, a torsional error between the axes, a positional relationship error between the axes, and a link length. The robot system of claim 1 .

11. and when the control device causes the robot to operate again based on the operation plan after the robot has operated based on the operation plan, the control device causes the robot to operate in a different behavior from the behavior of the robot immediately before the operation plan. The robot system of claim 1 .

12. A robot having three or more degrees of freedom; A control device that operates the robot based on a motion plan; a measurement device for measuring a trajectory of a control point of the robot when the robot moves based on the motion plan; a processor for estimating setting errors of modeling parameters based on the motion plan and the trajectory of the control points and identifying the modeling parameters; A display device, The processor causes the display device to display the modeling parameter identification results. Robot system.

13. A robot having three or more degrees of freedom; A control device that operates the robot based on a motion plan; a measurement device for measuring a trajectory of a control point of the robot when the robot moves based on the motion plan; a processor for estimating setting errors of modeling parameters based on the motion plan and the trajectory of the control points and identifying the modeling parameters; and a memory for modifying stored modeling parameters to the modeling parameters identified by the processor. Robot system.

14. Operate a robot having three or more degrees of freedom based on the motion plan; measuring a trajectory of a control point of the robot when the robot moves based on the motion plan; estimating setting errors of modeling parameters necessary for calculating the positions of the control points of the robot based on the motion plan and the trajectories of the control points, and identifying the modeling parameters; Parameter identification methods.

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