WO2023157151A1 - Robot system, robot adjustment device, program, and robot manufacturing method - Google Patents

Abstract

Provided is a robot system including: a multi-joint robot having a plurality of shafts; and a controller for controlling the robot. The controller comprises: a storage unit for storing a relational expression that indicates the relationship between the plurality of shafts and the fingers of the robot and that has elements which have been adjusted to the individual robot by using an optimization scheme; and a command generation unit that generates an operation command for the robot on the basis of the relational expression stored in the storage unit. Provided is a robot adjustment device comprising: a sensor value information acquisition unit that acquires sensor value information pertaining to the plurality of shafts or the fingers of the robot during operation of the multi-joint robot having the plurality of shafts; a shaft-related information acquisition unit that acquires shaft-related information related to the plurality of shafts during operation of the robot; and an adjustment unit that uses an optimization scheme to adjust the elements in the relational expression on the basis of the discrepancy between the sensor value information and calculation information, said calculation information having been calculated by using the axis-related information and the relational expression that indicates the relationship between the plurality of shafts and the fingers of the robot.

Description

ROBOT SYSTEM, ROBOT ADJUSTMENT DEVICE, PROGRAM, AND ROBOT MANUFACTURING METHOD

The present invention relates to a robot system, a robot adjustment device, a program, and a robot manufacturing method.

Patent Literature 1 describes estimating the hand force of the robot by multiplying the disturbance torque of each axis of the robot by the inverse matrix of the Jacobian matrix.
[Prior art documents]
[Patent Literature]
[Patent Document 1] JP 2011-235374 A

General disclosure

According to one embodiment of the present invention, a robot system is provided. The robot system may comprise an articulated robot having multiple axes and a controller for controlling the robot. The controller may have a storage unit that stores a relational expression that indicates the relationship between the plurality of axes and the hand of the robot and has elements adjusted for the individual robot by an optimization technique. The controller may have a command generation unit that generates an operation command for the robot based on the relational expression stored in the storage unit.

The storage unit may store the relational expression having a Jacobian matrix composed of the elements adjusted for the individual robot. The storage unit may store the relational expression including the transmission efficiency of the plurality of axes adjusted for the individual robot. The command generation unit estimates the robot based on the disturbance torques of the plurality of axes and the rotation angles of the plurality of axes and the relational expression when the robot operates without using a force sensor. Hand forces may be used to generate movement commands for the robot.

The controller may have a sensor value information acquisition unit that acquires sensor value information for the hands of the robot or the plurality of axes when the robot operates. The controller may have an axis-related information acquisition unit that acquires axis-related information related to the plurality of axes when the robot operates. The controller may have an adjustment unit that adjusts the elements of the relational expression based on an error between the axis-related information and information calculated by the relational expression and the sensor value information. The storage unit may store the relational expression in which the elements are adjusted by the adjustment unit. The relational expression may include a Jacobian matrix, and the adjustment unit may adjust the elements constituting the Jacobian matrix based on an error between the calculated information and the sensor value information. The relational expression may include transmission efficiencies of the plurality of axes, and the adjustment unit may adjust the transmission efficiencies of the plurality of axes based on an error between the calculated information and the sensor value information. . The sensor value information may include sensor values measured by a force sensor that measures hand force of the robot, and the axis-related information includes rotation angles of the plurality of axes and the plurality of axes when the robot operates. and the calculated information includes the rotation angle of the plurality of axes, the disturbance torque of the plurality of axes, and the estimated hand force of the robot calculated by the relational expression, The command generation unit may generate an operation command for the robot using the estimated hand force included in the calculated information without using a force sensor.

The adjustment unit may adjust the elements of the relational expression by executing black-box optimization so as to reduce the error between the calculated information and the sensor value information. The adjustment unit may adjust the elements of the relational expression by performing Bayesian optimization so as to reduce the error between the calculated information and the sensor value information. The adjustment unit may perform the black-box optimization by limiting the search range to a predetermined range centered on the set value of the robot. The relational expression may include a plurality of axis-related elements for each of the plurality of axes, and the adjustment unit may adjust the plurality of axis-related elements for each of the plurality of axes. . For each of the plurality of axes, the adjustment section may adjust the plurality of elements related to the axes in the order in which the plurality of axes are connected. For each of the plurality of shafts, the adjusting section may adjust the plurality of elements related to the shaft in order from the root side to the tip side. The adjuster may change the elements of the relational expression based on the error between the calculated information and the sensor value information.

According to one embodiment of the present invention, a robot adjustment device is provided. The robot adjustment device may include a sensor value information acquisition unit that acquires sensor value information for a hand of the robot or the multiple axes when the articulated robot having multiple axes operates. The robot adjustment device may include an axis-related information acquisition unit that acquires axis-related information related to the plurality of axes when the robot operates. The robot adjusting device adjusts the element of the relational expression based on the error between the sensor value information and the relational expression indicating the relationship between the plurality of axes and the hand of the robot, the calculation information calculated by the axis-related information, and the sensor value information. may be provided with an adjustment unit that adjusts by an optimization method.

According to one embodiment of the present invention, there is provided a program for causing a computer to function as the robot adjustment device.

According to one embodiment of the present invention, a computer-executed method for manufacturing an articulated robot having multiple axes is provided. The manufacturing method acquires sensor value information for a hand of the robot or the plurality of axes when the robot operates, and axis-related information relating to the plurality of axes when the robot operates. It may have stages. In the manufacturing method, elements of the relational expression are calculated based on an error between the sensor value information and the relational expression indicating the relationship between the plurality of axes and the hand of the robot, the information calculated based on the axis-related information, and the There may be an adjustment stage that adjusts by an optimization technique.

It should be noted that the above outline of the invention does not list all the necessary features of the present invention. Subcombinations of these feature groups can also be inventions.

An example of a robot system 10 is shown schematically. An exemplary arrangement of sensors 180 is schematically shown. An exemplary arrangement of sensors 180 is schematically shown. An example of the functional configuration of the control device 200 and the robot adjustment device 300 is shown schematically. FIG. 4 is an explanatory diagram for explaining a method of calculating a Jacobian matrix; FIG. 4 is an explanatory diagram for schematically explaining adjustment processing by the robot adjustment device 300; An example of a six-axis robot 500 is shown schematically. FIG. 11 is an explanatory diagram for explaining a relational expression 280 in the 6-axis robot 500; An example of the flow of adjustment processing by the robot adjustment device 300 is shown schematically. An example of the flow of adjustment processing by the robot adjustment device 300 is shown schematically. An example of the flow of the manufacturing method of the robot 100 by the robot adjustment device 300 is schematically shown. 1 schematically shows an example of a hardware configuration of a computer 1200 functioning as a robot adjustment device 300. FIG.

The present invention will be described below through embodiments of the invention, but the following embodiments do not limit the invention according to the scope of claims. Also, not all combinations of features described in the embodiments are essential to the solution of the invention.

FIG. 1 schematically shows an example of a robot system 10. A robot system 10 includes a robot 100 and a control device 200 . The robotic system 10 may include a robotic adjustment device 300 . Robotic system 10 may include sensor 180 .

The robot 100 is an articulated robot with multiple axes. The robot 100 may have a serial linkage, and multiple axes may be serially connected by multiple links. The robot 100 may have any number of axes, for example, the robot 100 has 4-7 axes. The robot 100 may be a so-called industrial robot. The application of the robot 100 is not limited to industrial use, and may be other uses such as medical use.

The control device 200 controls the robot 100. The control device 200 may control the robot 100 by sending to the robot 100 an operation command for operating the servo motors of the robot 100 while receiving feedback from the robot 100 . Examples of feedback information fed back from the robot 100 include rotation angles, rotation speeds, and torques of each of the multiple axes.

The robot adjustment device 300 performs adjustment regarding the robot 100 . The robot adjustment device 300 adjusts the elements of the relational expression 280 indicating the relationship between the axes of the robot 100 and the hand of the robot 100 .

The relational expression 280 may include a Jacobian matrix, and the robot adjustment device 300 may adjust the elements that make up the Jacobian matrix. Relational expression 280 may include transmission efficiencies of multiple axes, and robot adjuster 300 may adjust the transmission efficiencies of multiple axes. The robot adjustment device 300 may adjust both the elements that make up the Jacobian matrix and the transmission efficiency of multiple axes. The robot adjustment device 300 may adjust only one of the elements that make up the Jacobian matrix and the transmission efficiency of a plurality of axes.

The robot adjustment device 300 may adjust the elements of the relational expression 280 by performing learning using sensor value information indicating sensor values measured by the sensor 180 when the robot 100 operates. For example, the robot adjustment device 300 acquires sensor value information for the hand of the robot 100 and feedback information from the robot 100 when the robot 100 operates. The robot adjustment device 300 adjusts the elements of the relational expression 280 based on the feedback information, the calculation information calculated by the relational expression 280, and the sensor value information. For example, the robot adjustment device 300 adjusts the elements of the relational expression 280 by executing optimization processing so as to reduce the error between the calculated information and the sensor value information. Thereby, the accuracy of the information calculated using the feedback information and the relational expression 280 can be improved.

The sensor 180 is, for example, a force sensor that measures force applied to the hand of the robot 100 (sometimes referred to as hand force). For example, for learning purposes, the robot adjustment device 300 stores sensor value information including force sensor values measured by the sensor 180 when the robot 100 operates with a force applied to the hand of the robot 100, and Get feedback and information.

The robot adjustment device 300 calculates the disturbance torque from the torque included in the feedback information. The disturbance torque indicates a value obtained by subtracting the torque required for control from the actual torque. For example, the robot adjustment device 300 receives from the control device 200 in advance the torque (may be referred to as disturbance-free torque) when the robot 100 operates on a specific trajectory with no disturbance, and stores it. put. The robot adjustment device 300 may acquire the difference between the torque included in the feedback information and the non-disturbance torque as the disturbance torque.

The robot adjustment device 300 may estimate the hand force of the robot 100 based on the rotation angles of the multiple axes included in the feedback information, the calculated disturbance torques of the multiple axes, and the relational expression 280 . The robot adjustment device 300 performs optimization so as to reduce the error between the estimated hand force (sometimes referred to as estimated hand force) and the force sensor value included in the sensor value information. Elements of equation 280 may be adjusted. The robot adjusting device 300 may set the relational expression 280 with adjusted elements in the control device 200 .

In the process of assembly and processing using robots, there are times when it is required to carry out work while pressing the fingers with a certain amount of force using force control. At this time, force control is generally achieved by using a force sensor attached to the hand, but the sensor system is expensive and the environment is such that the sensor is prone to failure due to dust or water droplets. Therefore, it may not be possible to introduce force control functions using sensors. Therefore, it is required to develop a force control function that can be realized at low cost without using sensors. Sensorless force control has also been implemented in the past, and is realized by estimating the hand force by calculating the hand force from the disturbance torque of each axis of the robot using the Jacobian matrix. However, it is said that it is difficult to estimate the hand force with practical accuracy due to the deviation of the link length of the robot, the origin of the axis, etc., and the deviation from the theoretical calculation of the torque transmission efficiency. Values such as the distance between each axis and transmission efficiency are determined by the design of the robot, but deviations from the design occur due to the environment, origin deviation, deterioration, and the like.

On the other hand, the robot adjustment device 300 according to the present embodiment uses, for example, the error between the estimated hand force calculated by the rotation angles and disturbance torques of a plurality of axes and the relational expression 280 and the actual force sensor value as an objective function. , the distance between each axis and the transmission efficiency of each axis in the Jacobian matrix are optimized as parameters. As a result, it is possible to improve the accuracy of estimating the hand force using the rotation angles and disturbance torques of a plurality of shafts. The control device 200, which stores the relational expression 280 optimized by the robot adjustment device 300, uses the estimated hand force of the robot 100 estimated by the relational expression 280 without using a force sensor, and relates to the force control of the robot 100. may generate an operation command to Accordingly, it is possible to provide the robot system 10 capable of realizing force control with practical precision at low cost.

The robot adjustment device 300 may set a relational expression 280 in which elements are adjusted for individual robots 100 by an optimization technique for each of the plurality of sets of robots 100 and control devices 200 .

For example, the robot adjusting device 300 is incorporated in a manufacturing system for the robots 100 and sequentially adjusts the elements of the relational expression 280 for a plurality of robots 100 to be manufactured. This provides a robot 100 and a control device 200 capable of estimating hand force with relatively high accuracy without a sensor even when the link length, the origin of each axis, etc. deviate from the design values at the time of manufacture. be able to.

For example, the robot adjusting device 300 is incorporated in the maintenance system of the robot 100 and sequentially adjusts the elements of the relational expression 280 for the plurality of robots 100 to be maintained. When the used robot 100 is deformed or deteriorated, the link length and the origin of each axis may deviate from the state before use. Therefore, even if the hand force estimation accuracy using the relational expression 280 is high before use, the estimation accuracy decreases after use. According to the robot adjustment device 300, the relational expression 280 suitable for the state of the robot 100 at the time of maintenance can be set, and the accuracy of hand force estimation can be improved compared to before maintenance.

The robot adjustment device 300 may be provided for a set of the robot 100 and the control device 200. In this case, the robot adjustment device 300 may adjust the elements of the relational expression 280 periodically or irregularly. Further, the robot adjustment device 300 may adjust the elements of the relational expression 280 according to instructions from the administrator of the robot 100 or the like. As a result, even if the robot 100 is deformed or deteriorated through use, the elements of the relational expression 280 can be adjusted to suit the state after use, and the hand force can be estimated. It can contribute to maintaining high accuracy.

The control device 200 may be an example of a controller. Also, a combination of the control device 200 and the robot adjustment device 300 may be an example of a controller. That is, the controller may include only the control device 200 or both the control device 200 and the robot adjustment device 300 . Note that the control device 200 and the robot adjustment device 300 may be integrated. That is, the control device 200 may have the function of the robot adjustment device 300 .

In an environment where control of the robot 100 is realized by a combination of a so-called robot controller and a so-called general-purpose robot controller that generates teaching points for the robot 100 and provides them to the robot controller, the control device 200 functions as a robot controller. good. In this case, the robot system 10 may further comprise a general-purpose robot controller.

2 and 3 schematically show an example of arrangement of the sensor 180. FIG. Here, the sensor 180 is illustrated as a force sensor that measures the hand force of the robot 100 .

The sensor 180 may be installed with respect to the robot 100 as illustrated in FIG. Sensor value information and feedback information used for learning can be collected by operating the robot 100 in a plurality of postures while applying various forces to the hand. By installing the sensor 180 on the robot 100, it is possible to collect information with high accuracy.

When the robot adjustment device 300 is incorporated into the manufacturing system of the robot 100, the sensor 180 is installed on the robot 100 to be manufactured, and after collecting information while operating the robot 100, the sensor 180 is removed. Similarly, when the robot adjustment device 300 is incorporated into the maintenance system of the robot 100, the sensor 180 is installed on the robot 100 to be maintained, and after collecting information while operating the robot 100, the sensor 180 is removed. . When the robot adjustment device 300 is installed for a set of the robot 100 and the control device 200, the sensor 180 is installed on the robot 100 when adjusting the relational expression 280, and information is obtained while the robot 100 is operating. After collection, sensor 180 is removed.

The sensor 180 may be installed not on the robot 100 but on the environment side, as illustrated in FIG. FIG. 3 illustrates a state in which the sensor 180 is installed with respect to the workpiece 20. As shown in FIG. By having the robot 100 apply force to the sensor 180 from a plurality of directions in a plurality of postures, or by applying a force to the sensor 180 while appropriately changing the position and posture of the sensor 180, learning can be facilitated. Sensor value information and feedback information can be collected for use. By placing the sensor 180 on the environment side, the time to install the sensor 180 on the robot 100 can be saved.

4 schematically shows an example of functional configurations of the control device 200 and the robot adjustment device 300. FIG. The control device 200 includes a communication section 202 , a communication section 204 , a storage section 206 and a command generation section 208 . The robot adjustment device 300 includes a communication section 302 , a communication section 304 , a storage section 306 , a learning preparation section 308 and a learning processing section 310 .

The communication unit 202 communicates with the robot 100. The communication unit 202 may communicate with the robot 100 by wire. The communication unit 202 may wirelessly communicate with the robot 100 . The communication unit 202 may communicate with the robot 100 directly or via a network.

The communication unit 204 communicates with the robot adjustment device 300. The communication unit 204 may communicate with the robot adjustment device 300 by wire. The communication unit 204 may wirelessly communicate with the robot adjustment device 300 . The communication unit 204 may communicate with the robot adjustment device 300 directly or via a network.

The storage unit 206 stores various information. The storage unit 206 stores a relational expression 280, for example. The storage unit 206 may store setting values for the robot 100 . The setting value may include the distance between each axis of the robot 100 . The setting values may include the position and orientation relationship between each axis of the robot 100 . The set value may include the transmission efficiency of each axis of the robot 100 .

The setting value is, for example, the design value of the robot 100. The set value is, for example, an adjusted value obtained by adjusting the design value by calibration or the like. The setting value may be, for example, a value set according to the state of the robot 100 after the robot 100 is used.

The command generation unit 208 generates motion commands for the robot 100 . The command generation unit 208 may generate an operation command for the robot 100 based on the relational expression 280 stored in the storage unit 206 .

The communication unit 302 communicates with the control device 200. The communication unit 302 may communicate with the control device 200 by wire. The communication unit 302 may wirelessly communicate with the control device 200 . The communication unit 302 may communicate with the control device 200 directly or via a network.

The communication unit 304 communicates with the sensor 180. The communication unit 304 may communicate with the sensor 180 by wire. The communication unit 304 may wirelessly communicate with the sensor 180 . The communication unit 304 may communicate with the sensor 180 directly or via a network.

The storage unit 306 stores various information. The storage unit 306 stores information received by the communication unit 302 from the control device 200, for example. For example, the communication unit 302 receives the relational expression 280 stored in the storage unit 206 of the control device 200 and stores it in the storage unit 306 . For example, the communication unit 302 receives setting values stored in the storage unit 206 and stores them in the storage unit 306 .

The storage unit 306 may store motion data indicating motions to be performed by the robot 100 for learning. The storage unit 306 stores, for example, motion data for causing the robot 100 to perform various motions generated for efficient learning.

When the use of the robot 100 is determined, the storage unit 306 may store motion data generated according to the use. When the action scheduled to be performed by the robot 100 is determined, the storage unit 306 may store action data for causing the robot 100 to perform the action scheduled to be performed. As a result, it is possible to perform learning according to the operation that the robot 100 is scheduled to perform, which contributes to an improvement in accuracy.

The storage unit 306 may store operation data for obtaining the disturbance-free torque. The motion data for obtaining the disturbance-free torque may be data for causing the robot 100 to perform motions similar to those performed for learning in a state without disturbance. Also, the motion data for obtaining the disturbance-free torque may be generated for accurately obtaining the disturbance-free torque.

[Study Preparation]
The learning preparation unit 308 executes a preparation process for learning. The learning preparation unit 308 may cause the control device 200 to control the robot 100 by transmitting motion data to the control device 200 via the communication unit 302 . The command generation unit 208 of the control device 200 may generate a motion command according to the motion data received from the robot adjustment device 300 and transmit it to the robot 100 .

The learning preparation unit 308 transmits to the control device 200, for example, operation data for obtaining disturbance-free torque. The control device 200 operates the robot 100 according to the motion data and receives feedback information from the robot 100 . The control device 200 transmits the received feedback information to the robot adjustment device 300 . Storage unit 306 stores the torque included in the received feedback information as non-disturbance torque.

The learning preparation unit 308 transmits motion data for learning to the control device 200, for example. The control device 200 operates the robot 100 according to the operation data and receives feedback information from the robot 100 when the robot 100 operates. The sensor 180 measures sensor values for the hand of the robot 100 when the robot 100 operates.

The robot adjustment device 300 receives feedback information from the control device 200 . Robotic conditioning device 300 receives sensor value information including sensor values measured by sensors 180 . The robot adjustment device 300 may receive sensor value information from the sensor 180 via the communication unit 304 . The robot adjustment device 300 may receive sensor value information via the control device 200 when the sensor 180 can communicate with the robot 100 or when the sensor 180 can communicate with the control device 200 . The storage unit 306 stores the received feedback information and sensor value information.

Here, a case where the robot adjustment device 300 takes the lead in executing preparation for learning has been described as an example, but the present invention is not limited to this. A person such as an administrator of the robot 100 may perform the learning preparation, and the person may operate the control device 200 while proceeding with the learning preparation.

[Learning process]
The learning processing unit 310 executes learning processing. The learning processing unit 310 has a learning information generation unit 312 , a sensor value information acquisition unit 314 , an axis related information acquisition unit 316 and an adjustment unit 318 .

[Generation of learning information]
The learning information generation unit 312 generates learning information from information stored in the storage unit 306 . The learning information generation unit 312 generates learning information including, for example, sensor value information for the hand of the robot 100 when the robot 100 operates, and axis-related information related to a plurality of axes when the robot 100 operates. .

For example, the learning processing unit 310 calculates the difference between the torque included in the feedback information and the non-disturbance torque as the disturbance torque. Then, the learning processing unit 310 generates learning information including sensor value information and shaft-related information including the rotation angles of the plurality of shafts and the calculated disturbance torque included in the feedback information, and stores the learning information in the storage unit 306. Memorize.

[Contents of learning process]
The sensor value information acquisition unit 314, the axis-related information acquisition unit 316, and the adjustment unit 318 execute learning processing. A sensor value information acquisition unit 314 acquires sensor value information from learning information stored in the storage unit 306 . The axis-related information acquisition unit 316 acquires axis-related information from learning information stored in the storage unit 306 .

The adjustment unit 318 adjusts the elements of the relational expression 280 for the individual robot 100 by an optimization method. The adjustment unit 318 adjusts the elements of the relational expression 280 stored in the storage unit 306 based on the sensor value information acquired by the sensor value information acquisition unit 314 and the axis-related information acquired by the axis-related information acquisition unit 316. can be adjusted. For example, the adjuster 318 adjusts the elements of the relational expression 280 based on the error between the axis-related information and the calculation information calculated by the relational expression 280 and the sensor value information.

The adjustment unit 318 may adjust the elements forming the Jacobian matrix included in the relational expression 280 based on the error between the calculated information and the sensor value information. For example, the adjuster 318 adjusts the elements that make up the Jacobian matrix included in the relational expression 280 by performing optimization to reduce the error between the calculated information and the sensor value information. By adjusting the elements of the Jacobian matrix, the accuracy of hand force estimation can be improved.

The adjustment unit 318 may adjust the transmission efficiency of the multiple axes included in the relational expression 280 based on the error between the calculated information and the sensor value information. For example, the adjuster 318 adjusts the transmission efficiencies of the multiple axes included in the relational expression 280 by performing optimization to reduce the error between the calculated information and the sensor value information. By adjusting the transmission efficiencies of a plurality of axes in addition to the elements that make up the Jacobian matrix, the accuracy of hand force estimation can be further improved.

The adjustment unit 318 sets the relational expression 280 in which the elements are adjusted in the control device 200 . The storage unit 206 stores a relational expression 280 whose elements have been adjusted by the adjustment unit 318 .

The adjustment unit 318 may adjust the elements of the relational expression 280 by changing the elements of the relational expression 280 based on the error between the calculated information and the sensor value information. The adjuster 318 may adjust the elements of the relational expression 280 by calculating correction values for correcting the elements of the relational expression 280 based on the error between the calculation information and the sensor value information. The adjustment unit 318 may transmit the calculated correction value to the control device 200 , and the control device 200 may store the received correction value in the storage unit 206 in association with the relational expression 280 .

By changing the elements of the relational expression 280, it is possible to reduce the amount of data, reduce the management load, and reduce the calculation load during robot operation, as compared with the case of calculating the correction value. . By calculating the correction value for correcting the element of the relational expression 280, it is possible to easily return to the state before adjustment compared to the case of changing the element. For example, if the adjustment of the elements of the relational expression 280 causes a situation in which the accuracy is degraded, the original state can be restored more quickly.

The command generation unit 208 may generate an operation command for the robot 100 based on the relational expression 280 stored in the storage unit 206 and having the elements adjusted by the adjustment unit 318 . For example, the command generating unit 208 generates an operation command for the robot 100 using the estimated hand force of the robot 100 estimated based on the relational expression 280 used for estimating the hand force of the robot 100 . For example, the command generation unit 208 generates an operation command for causing the robot 100 to perform a polishing operation of polishing the workpiece while pressing the fingertip with a constant force through force control. For example, the command generation unit 208 generates an action command for causing the robot 100 to polish the workpiece with a prescribed action without force control, and finish polishing when the hand force is no longer applied. do. The command generation unit 208 generates an operation command for the robot 100 using a relational expression 280 including a Jacobian matrix composed of elements adjusted for the individual robot 100, thereby determining the link length and the axis origin of the robot 100. It is possible to reduce the influence of the deviation, and improve the accuracy of the robot control. The command generation unit 208 generates an operation command for the robot 100 using a relational expression 280 including the transmission efficiencies of a plurality of axes adjusted for each individual robot 100, thereby matching the transmission efficiency of each axis with the desk calculation. The influence of deviation can be reduced, and the accuracy of robot control can be improved.

FIG. 5 is an explanatory diagram for explaining the method of calculating the Jacobian matrix. Here, a method for calculating the Jacobian matrix used for force estimation will be described as an example. As shown in FIG. 5, let F be the force acting on the coordinate system Σ, F' be the force acting on the coordinate system Σ', and P=(px, py, pz) be the position of Σ with respect to the position of Σ'. , the rotation matrix is R, and F'=JF, the Jacobian matrix J is expressed by Equation 1 below.

　ただし、Ｐ×は下記数式２のとおりである。

However, Px is as shown in Equation 2 below.

FIG. 6 is an explanatory diagram for schematically explaining the adjustment processing by the robot adjustment device 300. FIG. Here, adjustment processing of the relational expression 406 used for hand force estimation of the robot 100 will be described.

The axis-related information acquisition unit 316 acquires each axis disturbance torque τ 402 and each axis rotation angle θ 404 from one piece of learning information among a plurality of pieces of learning information. The adjustment unit 318 calculates an estimated hand force 408 by calculating a relational expression 406 using each shaft disturbance torque τ 402 and each shaft rotation angle θ 404 . The adjustment unit 318 calculates an error between the force sensor value 410 acquired from the learning information by the sensor value information acquisition unit 314 and the estimated hand force 408 . The adjustment unit 318 uses, for example, the mean squared error as the error between the force sensor value 410 and the estimated hand force 408, which indicate changes in the hand force over time.

The adjustment unit 318 optimizes the elements of the relational expression 406 by the optimization method 420 using the error between the estimated hand force 408 and the force sensor value 410 as the objective function. The adjustment unit 318 may optimize the position and orientation between each axis and the transmission efficiency of each axis as parameters so as to reduce the error between the estimated hand force 408 and the force sensor value 410 .

[Black box optimization]
The adjuster 318 may use black-box optimization as the optimization technique. Adjuster 318 may adjust the elements of relationship 280 by performing black-box optimization to make the error between estimated hand force 408 and force sensor value 410 smaller. By using black-box optimization, it is possible to perform optimization suitable for the conditions of this example, that is, the configuration of the objective function to be minimized cannot be specified, but the output can be obtained for a given input.

[Bayesian optimization]
The adjuster 318 may use Bayesian optimization as the optimization technique. That is, the adjuster 318 may adjust the elements of the relational expression 280 by performing Bayesian optimization to make the error between the estimated hand force 408 and the force sensor value 410 smaller. In this example, the elements of the relational expression 280 to be optimized are 6 parameters for the position and orientation and 7 parameters for the transmission efficiency for each axis. Where it is difficult to find a solution, it is possible to efficiently find an optimal solution by using Bayesian optimization. Note that the adjustment unit 318 may use other techniques such as random search, grid search, and exhaustive search.

[Limit search range]
The adjustment unit 318 may determine the search range for black box optimization based on the setting values of the robot 100 stored in the storage unit 306 . For example, the adjustment unit 318 performs black-box optimization by limiting the search range to a predetermined range centered on the set values of the robot 100 . As a result, the search range can be appropriately restricted, and optimization efficiency can be improved.

Range information indicating a predetermined range may be registered in the adjustment unit 318 in advance. The adjustment unit 318 may limit the search range using the setting values of the robot 100 stored in the storage unit 306 and range information registered in advance.

Range information may be registered for each parameter included in the setting value. For example, range information indicating the range of distance is registered for each inter-axle distance. For example, range information indicating the range of the positional relationship is registered for the positional relationship between the axes. For example, range information indicating the range of the posture relationship is registered for the posture relationship between the axes. For example, range information is registered for the transmission efficiency range for each axis.

Range information is determined, for example, by experiment. For example, by collecting the difference between the design value and the actual measurement value of each axis distance, or by collecting the difference between each axis distance before using the robot 100 and after using each axis distance, ±5 mm etc., a range of distances can be determined. Range information may be determined by human experience.

FIG. 7 schematically shows an example of a 6-axis robot 500. Six-axis robot 500 may be an example of robot 100 . Six-axis robot 500 includes S-axis 510 , L-axis 520 , U-axis 530 , R-axis 540 , B-axis 550 and T-axis 560 .

FIG. 8 is an explanatory diagram for explaining the relational expression 280 in the 6-axis robot 500. As shown in FIG. Here, a coordinate system is set so that the rotation direction of a plurality of axes is the Z axis, the moment about the Z axis is expressed by _MZ , and the transmission efficiency×reduction ratio for each axis is expressed by η.

When a force F is applied to the hand of the 6-axis robot 500, the force _FT applied to the T-axis 560 is expressed by Equation 3 below, and the disturbance torque τ _T of the T-axis 560 is expressed by Equation 4 below.

A force F _B applied to the B-axis 550 is expressed by Equation 5 below, and a disturbance torque τ _B of the B-axis 550 is expressed by Equation 6 below.

A force F _R applied to the R-axis 540 is expressed by Equation 7 below, and a disturbance torque τ _R of the R-axis 540 is expressed by Equation 8 below.

A force _FU applied to the U-axis 530 is expressed by Equation 9 below, and a disturbance torque _τU of the U-axis 530 is expressed by Equation 10 below.

A force F _L applied to the L-axis 520 is expressed by Equation 11 below, and a disturbance torque τ _L of the L-axis 520 is expressed by Equation 12 below.

The force F _S applied to the S-axis 510 is expressed by Equation 13 below, and the disturbance torque τ _S of the S-axis 510 is expressed by Equation 14 below.

From the above, the relational expression 280 for calculating the estimated hand force of the 6-axis robot 500 is given by Equation 15 below.

In the case of the 6-axis robot 500, the elements of the relational expression 280 to be optimized include 6 parameters for the position and orientation and 7 parameters for the transmission efficiency for each axis, for a total of 42 parameters. If all parameters are optimized, the computational load is high and it may take a long time.

[Adjustment for each axis]
Adjuster 318 may adjust multiple elements associated with the axes for each of the multiple axes. In the case of the 6-axis robot 500, the adjuster 318 may adjust parameters for each of the 6 axes. As a result, the number of parameters to be optimized can be reduced to 7 parameters, and the computational load can be reduced.

The adjustment unit 318 may adjust a plurality of elements related to each of the multiple axes in the order in which the multiple axes are connected. The closer the order in which the axes are connected, the greater the influence they have on each other. Therefore, by adjusting in the order in which they are connected, it is possible to optimize with less error than in the case of adjusting regardless of the order in which they are connected.

For example, the adjustment unit 318 adjusts a plurality of elements related to each of the shafts in order from the root side to the tip side. In the case of the 6-axis robot 500, the adjuster 318 adjusts 7 parameters for each of the S-axis 510, L-axis 520, U-axis 530, R-axis 540, B-axis 550, and T-axis 560 in that order. Since the tip-side axis has a greater effect on the tip than the base-side axis, if the time available for the adjustment process is short, it is better to optimize from the tip to the tip than to optimize in order from the tip to the tip. Optimizing in the order of to , can be optimized so as to reduce the error.

For example, the adjustment unit 318 adjusts a plurality of elements related to each of the shafts in order from the tip side to the base side. In the case of the 6-axis robot 500, the adjustment unit 318 adjusts seven parameters in the order of the T-axis 560, B-axis 550, R-axis 540, U-axis 530, L-axis 520, and S-axis 510, respectively. If the time available for adjustment processing is long, it is possible to fully optimize from the axis on the hand side, which has a greater impact on the hand, and as a result, it is possible to optimize with less error. .

The adjustment unit 318 may select whether to adjust from the tip side to the root side or from the root side to the tip side according to the time available for adjustment processing. For example, if the time available for adjustment processing is equal to or greater than a predetermined time threshold, the adjustment unit 318 adjusts in order from the fingertip side to the base side, and if not longer than the threshold, adjusts in order from the base side to the fingertip side. to adjust. This allows the selection of an optimization order with fewer errors depending on the time available for the adjustment process.

FIG. 9 schematically shows an example of the flow of adjustment processing by the robot adjustment device 300. FIG. Here, a state in which learning preparation by the learning preparation unit 308 is completed and a plurality of pieces of learning information are stored in the storage unit 306 will be described as a start state.

In step (the step may be abbreviated as S) 102 , the learning processing unit 310 acquires one of a plurality of pieces of learning information from the storage unit 306 .

In S104, the sensor value information acquisition unit 314, the axis-related information acquisition unit 316, and the adjustment unit 318 perform adjustment for all axis elements in the relational expression 280 by an optimization technique. Specifically, the adjustment unit 318 calculates the estimated hand force based on the rotation angle and disturbance torque of each axis acquired by the axis-related information acquisition unit 316 and the relational expression 280, and the sensor value information acquisition unit 314 acquires In order to minimize the error between the force sensor value included in the sensor value information and the estimated hand force, the elements of all axes of the relational expression 280 are adjusted.

If adjustment has not been completed for all of the plurality of learning information (NO in S106), the process returns to S102, and the learning processing unit 310 acquires the next learning information among the plurality of learning information. If the adjustment has been completed for all of the pieces of learning information (YES in S106), the process ends.

FIG. 10 schematically shows an example of the flow of adjustment processing by the robot adjustment device 300. FIG. In FIG. 9, the case of adjusting the elements of all the axes in the relational expression 280 has been described, but here, the case of adjusting a plurality of elements related to each of the plurality of axes will be described. Note that differences from FIG. 9 will be mainly described.

In S202, the learning processing unit 310 determines an axis of interest among the multiple axes. The learning processing unit 310 determines, for example, the axis closest to the base as the axis of interest among the plurality of axes. In S<b>204 , learning processing unit 310 acquires one of the plurality of pieces of learning information from storage unit 306 .

In S206, the sensor value information acquisition unit 314, the axis-related information acquisition unit 316, and the adjustment unit 318 adjust the elements of the axis of interest in the relational expression 280 by an optimization method. If all of the pieces of learning information have not been adjusted for the axis of interest (NO in S208), returning to S204, the learning processing unit 310 acquires the next piece of learning information among the pieces of learning information. do. If adjustment has been completed for all pieces of learning information (YES in S208), the process proceeds to S210.

In S210, the learning processing unit 310 determines whether or not adjustment has been completed for all axes. If it is determined that the process has not ended, the process returns to S202, and the learning processing unit 310 determines the next axis of interest among the multiple axes. If it is determined that the process has ended, the process ends.

FIG. 11 schematically shows an example of the flow of the method for manufacturing the robot 100 by the robot adjusting device 300. FIG. Here, the state in which the robot adjustment device 300 receives the robot 100 and the control device 200 that have been assembled and initialized is the starting state, and by adjusting the relational expression 280 of the control device 200, the robot 100 and the control device 200 will be described.

In S302, the communication unit 302 and the communication unit 204 are connected, and the robot adjustment device 300 and the control device 200 establish a communication connection. In this example, the robot adjustment device 300 acquires sensor value information from the sensor 180 via the control device 200 .

In S304, the learning preparation unit 308 causes the control device 200 to operate the robot 100 and collect various information. The learning preparation unit 308 may collect undisturbed torques. The learning preparation unit 308 collects feedback information and sensor value information when the robot 100 performs an action for learning.

At S306, the learning information generation unit 312 generates learning information using the information collected at S304. In S<b>308 , the adjustment unit 318 acquires from the control device 200 the set values of the positions and orientations of the axes of the robot 100 and the transmission efficiencies of the axes, which are set in the control device 200 .

In S310, the sensor value information acquisition unit 314, the axis-related information acquisition unit 316, and the adjustment unit 318 adjust the elements of the relational expression 280 using an optimization technique. In S<b>312 , the adjustment unit 318 sets the relational expression 280 with the elements adjusted in S<b>310 in the control device 200 .

If the manufacturing process is not to end (NO in S314), the process returns to S302, and the next robot 100 and control device 200 are connected to the robot adjusting device 300.

[Adjustment of other relational expressions used for force estimation]
In the above embodiment, the sensor value information from the sensor 180 that measures the hand force of the robot 100 is used to adjust the elements of the Jacobian matrix of the relational expression for calculating the estimated hand force from each axis disturbance torque and each axis rotation angle. was mainly described, but the present invention is not limited to this.

The robot adjustment device 300 adjusts the elements of the relational expression 280 indicating the relationship between the torques of the multiple axes and the hand force using the sensor value information for the multiple axes of the robot 100 when the robot 100 operates. good too. In this case, sensors 180 that measure the torque of each of the axes of robot 100 may be used. The sensor value information acquisition unit 314 may acquire sensor value information for a plurality of axes of the robot 100 when the robot 100 operates.

[Adjustment of relational expressions used for estimation other than force estimation]
In the above embodiment, an example in which the robot adjustment device 300 adjusts the elements of the relational expression 280 used for force estimation has been mainly described, but the present invention is not limited to this. The robot adjustment device 300 may adjust elements of the relational expression 280 used for other estimations.

For example, the robot adjustment device 300 adjusts the elements of the relational expressions including the Jacobian matrix and the transmission efficiency used for estimating the speed of the robot 100 . In this case, for example, a speed sensor that measures the speed of the hand of the robot 100 may be used as the sensor 180 . Also, for example, an angular velocity sensor that measures angular velocities of a plurality of axes of the robot 100 may be used.

For example, the robot adjustment device 300 adjusts the elements of the relational expressions including the Jacobian matrix and the transmission efficiency used for estimating the acceleration of the robot 100 . In this case, for example, an acceleration sensor that measures the acceleration of the hand of the robot 100 may be used as the sensor 180 . Also, for example, an angular acceleration sensor that measures angular accelerations of a plurality of axes of the robot 100 may be used.

For example, the robot adjustment device 300 adjusts the elements of the relational expressions including the Jacobian matrix and the transmission efficiency used for estimating the hand position of the robot 100 . In this case, for example, a sensor that measures the position of the hand of the robot 100 may be used as the sensor 180 . Also, for example, a sensor that measures rotation angles of multiple axes of the robot 100 may be used.

FIG. 12 schematically shows an example of the hardware configuration of a computer 1200 functioning as the robot adjusting device 300. FIG. Programs installed on the computer 1200 cause the computer 1200 to function as one or more "parts" of the apparatus of the present embodiments, or cause the computer 1200 to operate or perform operations associated with the apparatus of the present invention. Multiple "units" can be executed and/or the computer 1200 can be caused to execute the process or steps of the process according to the present invention. Such programs may be executed by CPU 1212 to cause computer 1200 to perform certain operations associated with some or all of the blocks in the flowcharts and block diagrams described herein.

A computer 1200 according to this embodiment includes a CPU 1212 , a RAM 1214 and a graphics controller 1216 , which are interconnected by a host controller 1210 . Computer 1200 also includes input/output units such as communication interface 1222 , storage device 1224 , DVD drive, and IC card drive, which are connected to host controller 1210 via input/output controller 1220 . The DVD drive may be a DVD-ROM drive, a DVD-RAM drive, and the like. Storage devices 1224 may be hard disk drives, solid state drives, and the like. Computer 1200 also includes legacy input/output units, such as ROM 1230 and keyboard, which are connected to input/output controller 1220 via input/output chip 1240 .

The CPU 1212 operates according to programs stored in the ROM 1230 and RAM 1214, thereby controlling each unit. Graphics controller 1216 retrieves image data generated by CPU 1212 into a frame buffer or the like provided in RAM 1214 or itself, and causes the image data to be displayed on display device 1218 .

A communication interface 1222 communicates with other electronic devices via a network. Storage device 1224 stores programs and data used by CPU 1212 within computer 1200 . The DVD drive reads programs or data from a DVD-ROM or the like and provides them to the storage device 1224 . The IC card drive reads programs and data from IC cards and/or writes programs and data to IC cards.

ROM 1230 stores therein programs such as boot programs that are executed by computer 1200 upon activation and/or programs that depend on the hardware of computer 1200 . Input/output chip 1240 may also connect various input/output units to input/output controller 1220 via USB ports, parallel ports, serial ports, keyboard ports, mouse ports, and the like.

The program is provided by a computer-readable storage medium such as a DVD-ROM or an IC card. The program is read from a computer-readable storage medium, installed in storage device 1224 , RAM 1214 , or ROM 1230 , which are also examples of computer-readable storage media, and executed by CPU 1212 . The information processing described within these programs is read by computer 1200 to provide coordination between the programs and the various types of hardware resources described above. An apparatus or method may be configured by implementing information operations or processing according to the use of computer 1200 .

For example, when communication is performed between the computer 1200 and an external device, the CPU 1212 executes a communication program loaded into the RAM 1214 and sends communication processing to the communication interface 1222 based on the processing described in the communication program. you can command. The communication interface 1222 reads transmission data stored in a transmission buffer area provided in a recording medium such as a RAM 1214, a storage device 1224, a DVD-ROM, or an IC card under the control of the CPU 1212, and transmits the read transmission data. Data is transmitted to the network, or received data received from the network is written in a receive buffer area or the like provided on the recording medium.

In addition, the CPU 1212 causes the RAM 1214 to read all or necessary portions of files or databases stored in an external recording medium such as a storage device 1224, a DVD drive (DVD-ROM), an IC card, etc. Various types of processing may be performed on the data. CPU 1212 may then write back the processed data to an external recording medium.

Various types of information such as various types of programs, data, tables, and databases may be stored in recording media and subjected to information processing. CPU 1212 performs various types of operations on data read from RAM 1214, information processing, conditional decisions, conditional branching, unconditional branching, and information retrieval, which are described throughout this disclosure and are specified by instruction sequences of programs. Various types of processing may be performed, including /replace, etc., and the results written back to RAM 1214 . In addition, the CPU 1212 may search for information in a file in a recording medium, a database, or the like. For example, when a plurality of entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored in the recording medium, the CPU 1212 selects the first attribute from among the plurality of entries. search for an entry that matches the specified condition of the attribute value of the attribute, read the attribute value of the second attribute stored in the entry, and thereby determine the first attribute that satisfies the predetermined condition An attribute value of the associated second attribute may be obtained.

The programs or software modules described above may be stored in a computer-readable storage medium on or near computer 1200 . Also, a recording medium such as a hard disk or RAM provided in a server system connected to a dedicated communication network or the Internet can be used as a computer-readable storage medium, whereby the program can be transferred to the computer 1200 via the network. provide.

The blocks in the flowcharts and block diagrams in this embodiment may represent steps in the process in which the operations are performed or "parts" of the device responsible for performing the operations. Certain steps and "sections" may be provided with dedicated circuitry, programmable circuitry provided with computer readable instructions stored on a computer readable storage medium, and/or computer readable instructions provided with computer readable instructions stored on a computer readable storage medium. It may be implemented by a processor. Dedicated circuitry may include digital and/or analog hardware circuitry, and may include integrated circuits (ICs) and/or discrete circuitry. Programmable circuits, such as Field Programmable Gate Arrays (FPGAs), Programmable Logic Arrays (PLAs), etc., perform AND, OR, EXCLUSIVE OR, NOT AND, NOT OR, and other logical operations. , flip-flops, registers, and memory elements.

A computer-readable storage medium may comprise any tangible device capable of storing instructions to be executed by a suitable device, such that a computer-readable storage medium having instructions stored thereon may be illustrated in flowchart or block diagram form. It will comprise an article of manufacture containing instructions that can be executed to create means for performing specified operations. Examples of computer-readable storage media may include electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, and the like. More specific examples of computer readable storage media include floppy disks, diskettes, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory) , electrically erasable programmable read only memory (EEPROM), static random access memory (SRAM), compact disc read only memory (CD-ROM), digital versatile disc (DVD), Blu-ray disc, memory stick , integrated circuit cards, and the like.

The computer readable instructions may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state configuration data, or instructions such as Smalltalk, JAVA, C++, etc. any source or object code written in any combination of one or more programming languages, including object-oriented programming languages, and conventional procedural programming languages such as the "C" programming language or similar programming languages; may include

Computer readable instructions are used to produce means for a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, or programmable circuits to perform the operations specified in the flowchart or block diagrams. A general purpose computer, special purpose computer, or other programmable data processor, locally or over a wide area network (WAN) such as the Internet, etc., to execute such computer readable instructions. It may be provided in the processor of the device or in a programmable circuit. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, and the like.

Although the present invention has been described above using the embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It is obvious to those skilled in the art that various modifications or improvements can be made to the above embodiments. It is clear from the description of the scope of the claims that forms with such modifications or improvements can also be included in the technical scope of the present invention.

The execution order of each process such as actions, procedures, steps, and stages in devices, systems, programs, and methods shown in claims, specifications, and drawings is etc., and it should be noted that they can be implemented in any order unless the output of a previous process is used in a later process. Regarding the operation flow in the claims, specification, and drawings, even if explanations are made using "first," "next," etc. for the sake of convenience, it means that it is essential to carry out in this order. isn't it.

10 Robot system, 20 Work, 100 Robot, 180 Sensor, 200 Control device, 202 Communication unit, 204 Communication unit, 206 Storage unit, 208 Command generation unit, 280 Relational expression, 300 Robot adjustment device, 302 Communication unit, 304 Communication unit , 306 storage unit, 308 learning preparation unit, 310 learning processing unit, 312 learning information generation unit, 314 sensor value information acquisition unit, 316 axis related information acquisition unit, 318 adjustment unit, 402 each axis disturbance torque τ, 404 each axis rotation Angle θ, 406 relational expression, 408 estimated hand force, 410 force sensor value, 420 optimization method, 500 6-axis robot, 510 S-axis, 520 L-axis, 530 U-axis, 540 R-axis, 550 B-axis, 560 T-axis , 1200 computer, 1210 host controller, 1212 CPU, 1214 RAM, 1216 graphic controller, 1218 display device, 1220 input/output controller, 1222 communication interface, 1224 storage device, 1230 ROM, 1240 input/output chip

Claims (18)

an articulated robot with multiple axes;
a controller that controls the robot;
with
The controller is
a storage unit that stores a relational expression showing the relationship between the plurality of axes and the hand of the robot and having elements adjusted for the individual robot by an optimization method;
a command generation unit that generates an operation command for the robot based on the relational expression stored in the storage unit;
having
robot system. The robot system according to claim 1, wherein said storage unit stores said relational expression including a Jacobian matrix formed by said elements adjusted for said robot individual. The robot system according to claim 1 or 2, wherein the storage unit stores the relational expression including the transmission efficiency of the plurality of axes adjusted for the individual robot. The command generation unit estimates the robot based on the disturbance torques of the plurality of axes and the rotation angles of the plurality of axes and the relational expression when the robot operates without using a force sensor. The robot system according to any one of claims 1 to 3, wherein a finger force is used to generate an operation command for the robot. The controller is
a sensor value information acquisition unit that acquires sensor value information for the hand of the robot or the plurality of axes when the robot operates;
an axis-related information acquiring unit that acquires axis-related information related to the plurality of axes when the robot operates;
an adjustment unit that adjusts the elements of the relational expression based on an error between the axis-related information and information calculated by the relational expression and the sensor value information;
further having
The robot system according to any one of claims 1 to 4, wherein the storage unit stores the relational expression in which the elements are adjusted by the adjustment unit. The relational expression includes a Jacobian matrix,
6. The robot system according to claim 5, wherein said adjustment unit adjusts said elements constituting said Jacobian matrix based on an error between said calculated information and said sensor value information. the relational expression includes transmission efficiencies of the plurality of shafts;
7. The robot system according to claim 5, wherein said adjustment unit adjusts said transmission efficiency of said plurality of axes based on an error between said calculated information and said sensor value information. The sensor value information includes a sensor value measured by a force sensor that measures hand force of the robot,
the axis-related information includes rotation angles of the plurality of axes and disturbance torques of the plurality of axes when the robot operates;
The calculated information includes rotation angles of the plurality of axes, disturbance torques of the plurality of axes, and an estimated hand force of the robot calculated by the relational expression,
The command generation unit generates an operation command for the robot using the estimated hand force included in the calculated information without using a force sensor.
A robot system according to any one of claims 5 to 7. 9. Any one of claims 5 to 8, wherein the adjustment unit adjusts the elements of the relational expression by performing black-box optimization so as to reduce the error between the calculated information and the sensor value information. The robotic system described in . The robot system according to claim 9, wherein the adjusting unit adjusts the elements of the relational expression by performing Bayesian optimization so as to reduce the error between the calculated information and the sensor value information. The robot system according to claim 9 or 10, wherein the adjustment unit performs the black-box optimization by limiting the search range to a predetermined range centered on the set value of the robot. the relational expression includes, for each of the plurality of axes, a plurality of elements related to the axes;
The robot system according to any one of claims 5 to 11, wherein the adjuster adjusts the plurality of elements related to each of the plurality of axes. The robot system according to claim 12, wherein the adjustment unit adjusts the plurality of elements related to each of the plurality of axes in the order in which the plurality of axes are connected. 14. The robot system according to claim 13, wherein the adjusting unit adjusts the plurality of elements related to each of the plurality of axes in order from the base side to the tip side. The robot system according to any one of claims 5 to 14, wherein the adjustment unit changes elements of the relational expression based on an error between the calculated information and the sensor value information. a sensor value information acquisition unit that acquires sensor value information for a hand of the robot or the plurality of axes when an articulated robot having a plurality of axes operates;
an axis-related information acquiring unit that acquires axis-related information related to the plurality of axes when the robot operates;
The elements of the relational expression are adjusted by an optimization method based on the difference between the relational expression indicating the relationship between the plurality of axes and the hand of the robot, the calculation information calculated by the axis-related information, and the sensor value information. A robot adjusting device comprising: A program for causing a computer to function as the robot adjustment device according to claim 16. A computer-implemented method for manufacturing an articulated robot with multiple axes, comprising:
an information acquisition step of acquiring sensor value information for the hands of the robot or the plurality of axes when the robot operates, and axis-related information relating to the plurality of axes when the robot operates;
The elements of the relational expression are adjusted by an optimization method based on the difference between the relational expression indicating the relationship between the plurality of axes and the hand of the robot, the calculation information calculated by the axis-related information, and the sensor value information. A robot manufacturing method comprising:

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