JP7629574B2 - ROBOT SYSTEM, ROBOT ADJUSTMENT DEVICE, PROGRAM, AND ROBOT MANUFACTURING METHOD - Google Patents

Description

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

Patent Document 1 describes estimating the hand force of a robot by multiplying the disturbance torque of each axis of the robot by the inverse matrix of a Jacobian matrix.
[Prior Art Literature]
[Patent Documents]
[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 include a multi-joint robot having multiple axes, and a controller that controls the robot. The controller may have a memory unit that stores a relational equation that indicates the relationship between the multiple axes and the robot's hand and has elements that are adjusted for each individual robot by an optimization method. The controller may have a command generation unit that generates a motion command for the robot based on the relational equation stored in the memory unit.

The storage unit may store the relational equation having a Jacobian matrix composed of the elements adjusted for the individual robot. The storage unit may store the relational equation including the transmission efficiency of the multiple axes adjusted for the individual robot. The command generation unit may generate a motion command for the robot using an estimated hand force of the robot estimated based on the disturbance torque of the multiple axes and the rotation angles of the multiple axes when the robot operates, and the relational equation, without using a force sensor.

The controller may have a sensor value information acquisition unit that acquires sensor value information for the hand of the robot or the multiple axes when the robot operates. The controller may have an axis-related information acquisition unit that acquires axis-related information related to the multiple axes when the robot operates. The controller may have an adjustment unit that adjusts the elements of the relational equation based on an error between the sensor value information and calculation information calculated by the axis-related information and the relational equation. The storage unit may store the relational equation in which the elements have been adjusted by the adjustment unit. The relational equation may include a Jacobian matrix, and the adjustment unit may adjust the elements constituting the Jacobian matrix based on an error between the calculation information and the sensor value information. The relational equation may include a transmission efficiency of the multiple axes, and the adjustment unit may adjust the transmission efficiency of the multiple axes based on an error between the calculation information and the sensor value information. The sensor value information may include a sensor value measured by a force sensor that measures the hand force of the robot, the axis-related information may include rotation angles of the multiple axes and disturbance torques of the multiple axes when the robot operates, and the calculation information may include rotation angles of the multiple axes, disturbance torques of the multiple axes, and an estimated hand force of the robot calculated by the relational equation, and the command generation unit may generate a movement command for the robot using the estimated hand force included in the calculation information without using a force sensor.

The adjustment unit may adjust the elements of the relational expression by performing black-box optimization so as to reduce the error between the calculation 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 calculation 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 setting value of the robot. The relational expression may include a plurality of elements related to the axis for each of the multiple axes, and the adjustment unit may adjust the plurality of elements related to the axis for each of the multiple axes. The adjustment unit may adjust the plurality of elements related to the axis for each of the multiple axes in the order in which the multiple axes are connected. The adjustment unit may adjust the plurality of elements related to the axis for each of the multiple axes in the order from the root side to the hand tip side. The adjustment unit may change the elements of the relational expression based on the error between the calculation 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 the hand of a multi-jointed robot having multiple axes or the multiple axes when the robot is operating. The robot adjustment device may include an axis-related information acquisition unit that acquires axis-related information related to the multiple axes when the robot is operating. The robot adjustment device may include an adjustment unit that adjusts elements of the relational equation by an optimization method based on a relational equation indicating the relationship between the multiple axes and the hand of the robot and calculation information calculated using the axis-related information, and an error between the sensor value information.

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

According to one embodiment of the present invention, there is provided a method for manufacturing an articulated robot having multiple axes, which is executed by a computer. The manufacturing method may include an information acquisition step of acquiring sensor value information for the robot's hand or the multiple axes when the robot operates, and axis-related information related to the multiple axes when the robot operates. The manufacturing method may include an adjustment step of adjusting elements of the relational equation by an optimization method based on an error between the sensor value information and calculation information calculated using a relational equation indicating the relationship between the multiple axes and the robot's hand and the axis-related information.

Note that the above summary of the invention does not list all of the necessary features of the present invention. Also, subcombinations of these features may also be inventions.

1 illustrates a schematic diagram of an example of a robotic system 10. An example of the placement of the sensor 180 is shown diagrammatically. An example of the placement of the sensor 180 is shown diagrammatically. 2 shows an example of a functional configuration of a control device 200 and a robot adjustment device 300. FIG. 11 is an explanatory diagram for explaining a method for calculating a Jacobian matrix. 1 is an explanatory diagram for generally explaining an adjustment process by a robot adjustment device 300. FIG. An example of a six-axis robot 500 is shown diagrammatically. FIG. 2 is an explanatory diagram for explaining a relational expression 280 in a six-axis robot 500. 13 illustrates an example of a flow of adjustment processing by the robot adjustment device 300. 13 illustrates an example of a flow of adjustment processing by the robot adjustment device 300. 13 shows an example of a flow of a manufacturing method of the robot 100 by the robot adjustment device 300. 1 shows an example of a hardware configuration of a computer 1200 that functions as the robot adjustment device 300.

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

1 shows a schematic diagram of an example of a robot system 10. The robot system 10 includes a robot 100 and a control device 200. The robot system 10 may include a robot adjustment device 300. The robot system 10 may include a sensor 180.

The robot 100 is a multi-joint robot with multiple axes. The robot 100 may have a serial link mechanism, and the multiple axes may be serially connected by multiple links. The number of axes provided in the robot 100 may be any number, and for example, the robot 100 has 4 to 7 axes. The robot 100 may be a so-called industrial robot. The use of the robot 100 is not limited to industrial use, and it may also be for other uses such as medical use.

The control device 200 controls the robot 100. The control device 200 may control the robot 100 by transmitting to the robot 100 operation commands 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 the rotational angles, rotational speeds, and torques of each of the multiple axes.

The robot adjustment device 300 performs adjustments for the robot 100. The robot adjustment device 300 adjusts elements of a relational equation 280 that indicates the relationship between multiple 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 constituting the Jacobian matrix. The relational expression 280 may include the transmission efficiency of multiple axes, and the robot adjustment device 300 may adjust the transmission efficiency of the multiple axes. The robot adjustment device 300 may adjust both the elements constituting the Jacobian matrix and the transmission efficiency of the multiple axes. The robot adjustment device 300 may adjust only one of the elements constituting the Jacobian matrix and the transmission efficiency of the multiple axes.

The robot adjustment device 300 may adjust the elements of the relational equation 280 by performing learning using sensor value information indicating the sensor value 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 when the robot 100 operates, and feedback information by the robot 100. The robot adjustment device 300 adjusts the elements of the relational equation 280 based on the error between the sensor value information and the calculation information calculated by the feedback information and the relational equation 280. For example, the robot adjustment device 300 adjusts the elements of the relational equation 280 by performing an optimization process to reduce the error between the calculation information and the sensor value information. This can improve the accuracy of the calculation information using the feedback information and the relational equation 280.

The sensor 180 is, for example, a force sensor that measures the 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 acquires sensor value information including a force sensor value measured by the sensor 180 when the robot 100 operates with a force applied to the hand of the robot 100, and feedback information of the robot 100.

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 and stores in advance from the control device 200 the torque (sometimes referred to as undisturbed torque) obtained when the robot 100 moves on a specific trajectory without disturbance. The robot adjustment device 300 may obtain the difference between the torque included in the feedback information and the undisturbed 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 may adjust the elements of the relational expression 280 by performing optimization 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. The robot adjustment device 300 may set the relational expression 280 with the adjusted elements in the control device 200.

In assembly and processing processes using robots, it is sometimes necessary to perform a task while pressing the hand with a constant force by force control. In such cases, force control is generally achieved using a force sensor attached to the hand, but in some cases, the sensor system is expensive, or the environment is prone to sensor failure due to dust or water droplets, making it impossible to introduce a force control function using a sensor. For this reason, there is a need to develop a force control function that can be implemented at low cost without using a sensor. Sensorless force control has been implemented in the past, and is achieved by calculating the hand force from the disturbance torque of each axis of the robot using a Jacobian matrix and estimating the hand force. However, it is said to be difficult to estimate the hand force with practical accuracy due to deviations in the robot's link length and axis origin, and deviations from desk calculations of 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, etc.

In contrast, the robot adjustment device 300 according to the present embodiment optimizes the distance between the axes in the Jacobian matrix and the transmission efficiency of each axis as parameters, using, for example, the rotation angles and disturbance torque of multiple axes, as well as the error between the estimated hand force calculated by the relational expression 280 and the actual force sensor value as objective functions. This improves the accuracy of estimating the hand force using the rotation angles and disturbance torque of multiple axes. The control device 200, which stores the relational expression 280 optimized by the robot adjustment device 300, may generate operation commands related to the force control of the robot 100 using the estimated hand force of the robot 100 estimated by the relational expression 280 without using a force sensor. This makes it possible to provide a robot system 10 that can realize force control with practical accuracy at low cost.

The robot adjustment device 300 may set a relational equation 280 for each of multiple sets of robots 100 and control devices 200 by adjusting elements for each individual robot 100 using an optimization method.

For example, the robot adjustment device 300 is incorporated into a manufacturing system for the robot 100, and sequentially adjusts the elements of the relational equation 280 for multiple robots 100 to be manufactured. This makes it possible to provide a robot 100 and a control device 200 that can estimate the hand force with relatively high accuracy without a sensor, even if the link length and the origin of each axis deviate from the design value at the time of manufacturing.

For example, the robot adjustment device 300 is incorporated into a maintenance system for the robot 100 and sequentially adjusts the elements of the relational equation 280 for multiple robots 100 that are the subject of maintenance. A used robot 100 may deform or deteriorate, causing the link length and origin of each axis to shift compared to the state before use. Therefore, even if the estimation accuracy of the hand force using the relational equation 280 in the state before use is high, the estimation accuracy decreases after use. The robot adjustment device 300 can set the relational equation 280 appropriate for the state of the robot 100 at the time of maintenance, and can improve the estimation accuracy of the hand force 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 equation 280 periodically or irregularly. The robot adjustment device 300 may also adjust the elements of the relational equation 280 according to instructions from an administrator of the robot 100, etc. This allows the elements of the relational equation 280 to be adjusted to suit the state after use, even if the robot 100 is deformed or deteriorated through use, which contributes to maintaining a high level of accuracy in estimating the hand force.

The control device 200 may be an example of a controller. Furthermore, 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 may include both the control device 200 and the robot adjustment device 300. The control device 200 and the robot adjustment device 300 may be integrated. That is, the control device 200 may have the functions of the robot adjustment device 300.

In an environment in which the 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 may function as the robot controller. In this case, the robot system 10 may further include a general-purpose robot controller.

2 and 3 show schematic diagrams of an example of the arrangement of the sensor 180. Here, the sensor 180 is shown as a force sensor that measures the hand force of the robot 100.

The sensor 180 may be installed on the robot 100 as illustrated in FIG. 2. By operating the robot 100 in a number of postures while applying various forces to the hands, it is possible to collect sensor value information and feedback information to be used for learning. 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 a manufacturing system for the robot 100, the sensor 180 is installed on the robot 100 to be manufactured, and the sensor 180 is removed after information is collected while the robot 100 is operating. Similarly, when the robot adjustment device 300 is incorporated into a maintenance system for the robot 100, the sensor 180 is installed on the robot 100 to be maintained, and the sensor 180 is removed after information is collected while the robot 100 is operating. When the robot adjustment device 300 is attached to a set of robots 100 and control devices 200, the sensor 180 is installed on the robot 100 when adjusting the relational equation 280, and the sensor 180 is removed after information is collected while the robot 100 is operating.

The sensor 180 may be installed on the environment side instead of on the robot 100, as illustrated in FIG. 3. FIG. 3 illustrates a state in which the sensor 180 is installed on the workpiece 20. By having the robot 100 apply forces to the sensor 180 from multiple directions in multiple postures, or by having the robot 100 apply forces to the sensor 180 while appropriately changing the position and posture of the sensor 180, it is possible to collect sensor value information and feedback information to be used for learning. By installing the sensor 180 on the environment side, it is possible to save time in installing the sensor 180 on the robot 100.

Figure 4 shows an example of the functional configuration of the control device 200 and the robot adjustment device 300. The control device 200 includes a communication unit 202, a communication unit 204, a memory unit 206, and a command generation unit 208. The robot adjustment device 300 includes a communication unit 302, a communication unit 304, a memory unit 306, a learning preparation unit 308, and a learning processing unit 310.

The communication unit 202 communicates with the robot 100. The communication unit 202 may communicate with the robot 100 via wired communication. The communication unit 202 may communicate with the robot 100 wirelessly. 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 via wired communication. The communication unit 204 may communicate with the robot adjustment device 300 wirelessly. The communication unit 204 may communicate with the robot adjustment device 300 directly or via a network.

The memory unit 206 stores various information. The memory unit 206 stores, for example, the relational equation 280. The memory unit 206 may store setting values related to the robot 100. The setting values may include the distance between each axis of the robot 100. The setting values may include the position and posture relationship between each axis of the robot 100. The setting values may include the transmission efficiency of each axis of the robot 100.

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

The command generation unit 208 generates a motion command for the robot 100. The command generation unit 208 may generate a motion command for the robot 100 based on the relational expression 280 stored in the memory unit 206.

The communication unit 302 communicates with the control device 200. The communication unit 302 may communicate with the control device 200 via wired communication. The communication unit 302 may communicate with the control device 200 wirelessly. 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 via wired communication. The communication unit 304 may communicate with the sensor 180 wirelessly. The communication unit 304 may communicate with the sensor 180 directly or via a network.

The memory unit 306 stores various information. For example, the memory unit 306 stores information received by the communication unit 302 from the control device 200. For example, the communication unit 302 receives the relational equation 280 stored in the memory unit 206 of the control device 200 and stores it in the memory unit 306. For example, the communication unit 302 receives a setting value stored in the memory unit 206 and stores it in the memory unit 306.

The storage unit 306 may store operation data indicating operations to be performed by the robot 100 for learning. The storage unit 306 stores, for example, operation data generated for efficient learning and for causing the robot 100 to perform a variety of operations.

When the purpose of the robot 100 is determined, the memory unit 306 may store operation data generated according to the purpose. When the operation planned to be performed by the robot 100 is determined, the memory unit 306 may store operation data for causing the robot 100 to perform the operation planned to be performed. This makes it possible to execute learning that matches the operation planned to be performed by the robot 100, which contributes to improved accuracy.

The storage unit 306 may store operation data for acquiring the disturbance-free torque. The operation data for acquiring the disturbance-free torque may be data for causing the robot 100 to perform an operation similar to an operation performed for learning in a state without disturbance. The operation data for acquiring the disturbance-free torque may be data generated for accurately acquiring the disturbance-free torque.

[Study Preparation]
The learning preparation unit 308 executes a preparation process for learning. The learning preparation unit 308 may transmit operation data to the control device 200 via the communication unit 302, thereby causing the control device 200 to control the robot 100. The command generation unit 208 of the control device 200 may generate an operation command according to the operation data received from the robot adjustment device 300, and transmit the command to the robot 100.

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

The learning preparation unit 308, for example, transmits operation data for learning to the control device 200. 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 a sensor value 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. The robot adjustment device 300 receives sensor value information including a sensor value measured by the sensor 180. The robot adjustment device 300 may receive the sensor value information from the sensor 180 via the communication unit 304. When the sensor 180 is capable of communicating with the robot 100 or when the sensor 180 is capable of communicating with the control device 200, the robot adjustment device 300 may receive the sensor value information via the control device 200. The memory unit 306 stores the received feedback information and sensor value information.

Here, the case where the robot adjustment device 300 takes the lead in carrying out the learning preparation has been described as an example, but this is not limited to the above. The person who carries out the learning preparation may be a person such as an administrator of the robot 100, or the person may proceed with the learning preparation while operating the control device 200.

[Learning process]
The learning processing unit 310 executes the learning process and includes a learning information generating unit 312, a sensor value information acquiring unit 314, an axis related information acquiring unit 316, and an adjusting unit 318.

[Generation of learning information]
The learning information generating unit 312 generates learning information from the information stored in the storage unit 306. The learning information generating 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 disturbance-free torque as the disturbance torque. The learning processing unit 310 then generates learning information including the rotation angles of the multiple axes included in the feedback information, axis-related information including the calculated disturbance torque, and sensor value information, and stores the learning information in the memory unit 306.

[Learning process content]
The sensor value information acquisition unit 314, the axis related information acquisition unit 316, and the adjustment unit 318 execute a learning process. The sensor value information acquisition unit 314 acquires sensor value information from the learning information stored in the storage unit 306. The axis related information acquisition unit 316 acquires axis related information from the learning information stored in the storage unit 306.

The adjustment unit 318 adjusts the elements of the relational equation 280 for each individual robot 100 using an optimization method. The adjustment unit 318 may adjust the elements of the relational equation 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. For example, the adjustment unit 318 adjusts the elements of the relational equation 280 based on the error between the axis related information and the calculation information calculated by the relational equation 280, and the sensor value information.

The adjustment unit 318 may adjust the elements constituting the Jacobian matrix included in the relational equation 280 based on the error between the calculation information and the sensor value information. For example, the adjustment unit 318 adjusts the elements constituting the Jacobian matrix included in the relational equation 280 by performing optimization so as to reduce the error between the calculation information and the sensor value information. By adjusting the elements of the Jacobian matrix, the accuracy of the estimation of the hand force can be improved.

The adjustment unit 318 may adjust the transmission efficiency of the multiple axes included in the relational equation 280 based on the error between the calculation information and the sensor value information. For example, the adjustment unit 318 adjusts the transmission efficiency of the multiple axes included in the relational equation 280 by performing optimization so as to reduce the error between the calculation information and the sensor value information. By adjusting the transmission efficiency of the multiple axes in addition to the elements constituting the Jacobian matrix, the estimation accuracy of the hand force can be further improved.

The adjustment unit 318 sets the relational equation 280 with the adjusted elements in the control device 200. The memory unit 206 stores the relational equation 280 with the adjusted elements by the adjustment unit 318.

The adjustment unit 318 may adjust the elements of the relational equation 280 by changing the elements of the relational equation 280 based on the error between the calculation information and the sensor value information. The adjustment unit 318 may adjust the elements of the relational equation 280 by calculating a correction value that corrects the elements of the relational equation 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 memory unit 206 in association with the relational equation 280.

By changing the elements of relational equation 280, it is possible to reduce the amount of data, reduce the management burden, and reduce the calculation load during robot operation, compared to when a correction value is calculated. By calculating a correction value that corrects the elements of relational equation 280, it is possible to more easily return to the state before adjustment, compared to when the elements are changed. For example, if a situation occurs in which the accuracy is reduced as a result of adjusting the elements of relational equation 280, it is possible to more quickly return to the original state.

The command generating unit 208 may generate a motion command for the robot 100 based on the relational expression 280 stored in the storage unit 206 and whose elements have been adjusted by the adjustment unit 318. For example, the command generating unit 208 generates a motion command for the robot 100 using an estimated hand force of the robot 100 estimated based on the relational expression 280 used to estimate the hand force of the robot 100. For example, the command generating unit 208 generates a motion command for the robot 100 to perform a polishing operation in which the robot 100 polishes a workpiece while pressing the hand with a constant force by force control. For example, the command generating unit 208 generates a motion command for the robot 100 to polish a workpiece by a specified operation without force control, and to end polishing when the hand force is no longer applied. The command generating unit 208 generates a motion command for the robot 100 using the relational expression 280 including a Jacobian matrix composed of elements adjusted for each individual robot 100, thereby reducing the influence of the link length of the robot 100 and the deviation of the axis origin, and improving the accuracy of the robot control. The command generation unit 208 generates an operation command for the robot 100 using a relational equation 280 including the transmission efficiencies of multiple axes adjusted for each individual robot 100, thereby reducing the influence of deviations from desk calculations of the transmission efficiencies of each axis, and improving the accuracy of robot control.

Figure 5 is an explanatory diagram for explaining a method for calculating a Jacobian matrix. Here, a method for calculating a Jacobian matrix used for force estimation is explained as an example. As shown in Figure 5, when the force acting on the coordinate system Σ is F, the force acting on the coordinate system Σ' is F', the position of Σ based on the position of Σ' is P = (px, py, pz), the rotation matrix is R, and F' = JF, the Jacobian matrix J is expressed by the following formula 1.

ただし、Ｐ×は下記数式２のとおりである。

Here, P× is as shown in the following formula 2.

Figure 6 is an explanatory diagram for explaining an outline of the adjustment process by the robot adjustment device 300. Here, the adjustment process of the relational equation 406 used to estimate the hand force of the robot 100 is explained.

The axis-related information acquisition unit 316 acquires each axis disturbance torque τ402 and each axis rotation angle θ404 from one of the multiple pieces of learning information. The adjustment unit 318 calculates the estimated hand force 408 by calculating a relational expression 406 using each axis disturbance torque τ402 and each axis rotation angle θ404. The adjustment unit 318 calculates the 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, a mean square error as the error between the force sensor value 410 indicating the fluctuation of the hand force over time and the estimated hand force 408.

The adjustment unit 318 optimizes the elements of the relational expression 406 by an optimization method 420, using the error between the estimated hand force 408 and the force sensor value 410 as an objective function. The adjustment unit 318 may optimize the position and orientation between the axes 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 adjustment unit 318 may use black-box optimization as an optimization method. The adjustment unit 318 may adjust the elements of the relational expression 280 by performing black-box optimization so as to reduce the error between the estimated hand force 408 and the force sensor value 410. By using black-box optimization, it is possible to perform optimization suitable for the conditions of this example, in which the configuration of the objective function to be minimized is not specified but an output for a certain input can be obtained.

[Bayesian Optimization]
The adjustment unit 318 may use Bayesian optimization as an optimization method. That is, the adjustment unit 318 may adjust the elements of the relational expression 280 by performing Bayesian optimization so as to reduce the error between the estimated hand force 408 and the force sensor value 410. In this example, the elements of the relational expression 280 to be optimized are six parameters of the position and orientation and seven parameters of the transmission efficiency for each axis, and since the number of parameters is large, for example, even if a random search is performed, it is difficult to find an optimal solution. However, by using Bayesian optimization, it becomes possible to efficiently find an optimal solution. Note that the adjustment unit 318 may use other methods such as random search, grid search, and full search.

[Limit search range]
The adjustment unit 318 may determine the search range of the black-box optimization based on the setting values of the robot 100 stored in the storage unit 306. For example, the adjustment unit 318 executes the black-box optimization by limiting the search range to a predetermined range centered on the setting values of the robot 100. This makes it possible to appropriately limit the search range and improve the efficiency of the optimization.

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

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

The range information is determined, for example, by experiment. For example, a distance range, such as ±5 mm, can be determined by collecting the difference between the design value and the actual measured value of each inter-axis distance, or by collecting the difference between each inter-axis distance before and after use of the robot 100. The range information may also be determined by human experience.

7 shows a schematic diagram of an example of a six-axis robot 500. The six-axis robot 500 may be an example of the robot 100. The six-axis robot 500 includes an S-axis 510, an L-axis 520, a U-axis 530, an R-axis 540, a B-axis 550, and a T-axis 560.

8 is an explanatory diagram for explaining the relational expression 280 in the six-axis robot 500. Here, a coordinate system is set so that the rotation direction of the multiple axes is the Z axis, the moment about the Z axis is represented by _MZ , and the transmission efficiency x reduction ratio for each axis is represented by η.

When a force F is applied to the hand of the six-axis robot 500, a force F _T applied to the T-axis 560 is expressed by the following formula 3, and a disturbance torque τ _T of the T-axis 560 is expressed by the following formula 4.

The force F _B applied to the B-axis 550 is expressed by the following formula 5, and the disturbance torque τ _B of the B-axis 550 is expressed by the following formula 6.

The force F _R applied to the R-shaft 540 is expressed by the following formula 7, and the disturbance torque τ _R of the R-shaft 540 is expressed by the following formula 8.

The force F _U applied to the U-shaft 530 is expressed by the following formula 9, and the disturbance torque τ _U of the U-shaft 530 is expressed by the following formula 10.

The force F _L applied to the L-shaft 520 is expressed by the following formula 11, and the disturbance torque τ _L of the L-shaft 520 is expressed by the following formula 12.

The force F _S applied to the S-shaft 510 is expressed by the following formula 13, and the disturbance torque τ _S of the S-shaft 510 is expressed by the following formula 14.

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

In the case of a six-axis robot 500, the elements of the relational equation 280 to be optimized are six position and orientation parameters for each axis and seven parameters for transmission efficiency, totaling 42 parameters. Optimizing all parameters can be a heavy calculation load and can take a long time.

[Adjustment for each axis]
The adjustment unit 318 may adjust a plurality of elements related to each of the plurality of axes. In the case of the six-axis robot 500, the adjustment unit 318 may adjust the parameters for each of the six axes. This makes it possible to limit the number of parameters to be optimized to seven parameters, thereby reducing the calculation load.

The adjustment unit 318 may adjust the multiple elements associated with each of the multiple axes in the order in which the multiple axes are connected. The closer the order in which the multiple axes are connected, the greater the influence they have on each other. Therefore, by adjusting the axes in the order in which they are connected, it may be possible to optimize the system to reduce errors, compared to adjusting the axes regardless of the order in which they are connected.

For example, the adjustment unit 318 adjusts multiple elements related to each of the multiple axes in the order from the root side to the fingertip side. In the case of a six-axis robot 500, the adjustment unit 318 adjusts seven 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 axes on the fingertip side have a greater effect on the fingertip than the axes on the root side, when the time available for the adjustment process is short, optimization from the root to the fingertip can be performed with less error than optimization from the fingertip to the root.

For example, the adjustment unit 318 adjusts multiple elements related to each of the multiple axes in the order from the hand end to the base end. In the case of a six-axis robot 500, the adjustment unit 318 adjusts seven parameters for each of the T-axis 560, B-axis 550, R-axis 540, U-axis 530, L-axis 520, and S-axis 510 in that order. If the time available for the adjustment process is long, it is possible to sufficiently optimize the axes on the hand end side, which have the greatest 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 fingertip side to the root side or from the root side to the fingertip side, depending on the time available for the adjustment process. For example, if the time available for the adjustment process is equal to or greater than a predetermined time threshold, the adjustment unit 318 adjusts from the fingertip side to the root side, and if the time available for the adjustment process is less than the threshold, the adjustment unit 318 adjusts from the root side to the fingertip side. This makes it possible to select an optimization order that reduces error depending on the time available for the adjustment process.

Figure 9 shows an example of the flow of adjustment processing by the robot adjustment device 300. Here, the description is given assuming that the learning preparation unit 308 has completed learning preparation and that multiple pieces of learning information are stored in the memory unit 306 as the starting state.

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

In S104, the sensor value information acquisition unit 314, the axis-related information acquisition unit 316, and the adjustment unit 318 use an optimization method to perform adjustments on the elements of all axes in the relational equation 280. Specifically, the adjustment unit 318 calculates an estimated hand force using the rotation angle and disturbance torque of each axis acquired by the axis-related information acquisition unit 316 and the relational equation 280, and adjusts the elements of all axes of the relational equation 280 to minimize the error between the force sensor value included in the sensor value information acquired by the sensor value information acquisition unit 314 and the estimated hand force.

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

Figure 10 shows an example of the flow of adjustment processing by the robot adjustment device 300. Figure 9 describes a case where adjustment is made to the elements of all axes in the relational equation 280, but here we will explain a case where multiple elements related to each of multiple axes are adjusted. Note that we will mainly explain the points that are different from Figure 9.

In S202, the learning processing unit 310 determines an axis of interest from among the multiple axes. For example, the learning processing unit 310 determines the rootmost axis from among the multiple axes as the axis of interest. In S204, the learning processing unit 310 acquires one of the multiple pieces of learning information from the memory unit 306.

In S206, the sensor value information acquisition unit 314, the axis related information acquisition unit 316, and the adjustment unit 318 use an optimization method to perform adjustment on the elements of the axis of interest in the relational equation 280. If adjustment has not been completed for all of the multiple pieces of learning information for the axis of interest (NO in S208), the process returns to S204, where the learning processing unit 310 acquires the next piece of learning information from the multiple pieces of learning information. If adjustment has been completed for all of the multiple pieces of learning information (YES in S208), the process proceeds to S210.

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

Figure 11 shows an example of the flow of a manufacturing method of the robot 100 by the robot adjustment device 300. Here, the process flow is described in which the robot adjustment device 300 accepts the robot 100 and the control device 200 for which assembly and initial settings have been completed as a starting state, and the robot 100 and the control device 200 are completed by adjusting the relational equation 280 of the control device 200.

In S302, the communication unit 302 and the communication unit 204 are connected, and a communication connection is established between the robot adjustment device 300 and the control device 200. 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 collects various information. The learning preparation unit 308 may collect disturbance-free torque. The learning preparation unit 308 collects feedback information and sensor value information when the robot 100 performs an operation for learning.

In S306, the learning information generating unit 312 generates learning information using the information collected in S304. In S308, the adjusting unit 318 acquires from the control device 200 the position and orientation of the multiple axes of the robot 100 and the setting values of the transmission efficiency of the multiple axes that 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 use an optimization method to adjust the elements of the relational equation 280. In S312, the adjustment unit 318 sets the relational equation 280, the elements of which have been adjusted in S310, in the control device 200.

If the manufacturing process is not to be terminated (NO at S314), the process returns to S302 and the next robot 100 and control device 200 are connected to the robot adjustment device 300.

[Adjustment of other relations used in force estimation]
In the above embodiment, an example has been described in which the elements of the Jacobian matrix of the relational equation for calculating an estimated hand force from each axis disturbance torque and each axis rotation angle is adjusted using sensor value information from the sensor 180 that measures the hand force of the robot 100, but this is not limited to the example.

The robot adjustment device 300 may adjust elements of the relational equation 280 indicating the relationship between the torque of the multiple axes and the hand force, using sensor value information for the multiple axes of the robot 100 when the robot 100 operates. In this case, a sensor 180 may be used that measures the torque of each of the multiple axes of the robot 100. The sensor value information acquisition unit 314 may acquire sensor value information for the multiple axes of the robot 100 when the robot 100 operates.

[Adjustment of the relational expressions used for estimations 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 this is not limiting. 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 elements of a relational equation including a Jacobian matrix and a transmission efficiency used to estimate the speed of the robot 100. In this case, for example, a speed sensor that measures the speed of the tip of the robot 100 may be used as the sensor 180. Also, for example, an angular velocity sensor that measures the angular velocities of multiple axes of the robot 100 may be used.

For example, the robot adjustment device 300 adjusts elements of a relational equation including a Jacobian matrix and transmission efficiency used to estimate 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 the angular acceleration of multiple axes of the robot 100 may be used.

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

12 shows a schematic diagram of an example of a hardware configuration of a computer 1200 functioning as a robot adjustment device 300. A program installed on the computer 1200 can cause the computer 1200 to function as one or more "parts" of the device according to the present embodiment, or to execute operations or one or more "parts" associated with the device according to the present embodiment, and/or to execute a process or steps of the process according to the present embodiment. Such a program can be executed by the CPU 1212 to cause the computer 1200 to execute certain operations associated with some or all of the blocks of the flowcharts and block diagrams described herein.

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

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

The communication interface 1222 communicates with other electronic devices via a network. The storage device 1224 stores programs and data used by the CPU 1212 in the 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 an IC card and/or writes programs and data to an IC card.

ROM 1230 stores therein a boot program, etc., executed by computer 1200 upon activation, and/or a program that depends 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, etc.

The programs are provided by a computer-readable storage medium such as a DVD-ROM or an IC card. The programs are read from the 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. Information processing described in these programs is read by computer 1200, and brings about cooperation between the programs and the various types of hardware resources described above. An apparatus or method may be constructed by realizing operations or processing of information according to the use of computer 1200.

For example, when communication is performed between computer 1200 and an external device, CPU 1212 may execute a communication program loaded into RAM 1214 and instruct communication interface 1222 to perform communication processing based on the processing described in the communication program. Under the control of CPU 1212, communication interface 1222 reads transmission data stored in a transmission buffer area provided in RAM 1214, storage device 1224, a DVD-ROM, or a recording medium such as an IC card, and transmits the read transmission data to the network, or writes received data received from the network to a reception buffer area or the like provided on the recording medium.

CPU 1212 may also cause all or a necessary portion of a file or database stored in an external recording medium such as storage device 1224, a DVD drive (DVD-ROM), an IC card, etc. to be read into RAM 1214, and perform various types of processing on the data on RAM 1214. CPU 1212 may then write back the processed data to the external recording medium.

Various types of information, such as various types of programs, data, tables, and databases, may be stored in the recording medium and undergo information processing. The CPU 1212 may perform various types of processing on the data read from the RAM 1214, including various types of operations, information processing, conditional judgment, conditional branching, unconditional branching, information search/replacement, etc., as described throughout this disclosure and specified by the instruction sequence of the program, and write back the results to the RAM 1214. The CPU 1212 may also search for information in a file, database, etc. in the recording medium. 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 may search for an entry whose attribute value of the first attribute matches a specified condition from among the plurality of entries, read the attribute value of the second attribute stored in the entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

The above-described program or software module may be stored in a computer-readable storage medium on or near the computer 1200. In addition, 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, thereby providing the program to the computer 1200 via the network.

The blocks in the flowcharts and block diagrams in this embodiment may represent stages of a process in which an operation is performed or "parts" of a device responsible for performing the operation. Particular stages and "parts" may be implemented by dedicated circuitry, programmable circuitry provided with computer-readable instructions stored on a computer-readable storage medium, and/or a processor provided with computer-readable instructions stored on a computer-readable storage medium. The dedicated circuitry may include digital and/or analog hardware circuits, and may include integrated circuits (ICs) and/or discrete circuits. The programmable circuitry may include reconfigurable hardware circuits, such as, for example, field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), and the like, including AND, OR, XOR, NAND, NOR, and other logical operations, flip-flops, registers, and memory elements.

A computer-readable storage medium may include any tangible device capable of storing instructions executed by a suitable device, such that a computer-readable storage medium having instructions stored thereon comprises an article of manufacture that includes instructions that can be executed to create means for performing the operations specified in the flowchart or block diagram. 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 may include floppy (registered trademark) disks, diskettes, hard disks, random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), electrically erasable programmable read-only memories (EEPROMs), static random access memories (SRAMs), compact disk read-only memories (CD-ROMs), digital versatile disks (DVDs), Blu-ray (registered trademark) disks, memory sticks, integrated circuit cards, and the like.

The computer readable instructions may include either assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk (registered trademark), JAVA (registered trademark), C++, etc., and conventional procedural programming languages such as the "C" programming language or similar programming languages.

The computer-readable instructions may be provided to a processor of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, or a programmable circuit, either locally or over a local area network (LAN), a wide area network (WAN), such as the Internet, etc., so that the processor of the general-purpose computer, special-purpose computer, or other programmable data processing apparatus, or the programmable circuit executes the computer-readable instructions to generate means for performing the operations specified in the flowcharts or block diagrams. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, etc.

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

It should be noted that the order of execution of each process, such as operations, procedures, steps, and stages, in the devices, systems, programs, and methods shown in the claims, specifications, and drawings is not specifically stated as "before" or "prior to," and may be realized in any order, unless the output of a previous process is used in a later process. Even if the operational flow in the claims, specifications, and drawings is explained using "first," "next," etc. for convenience, it does not mean that it is necessary to perform the process in that order.

10 Robot system, 20 Workpiece, 100 Robot, 180 Sensor, 200 Control device, 202 Communication unit, 204 Communication unit, 206 Memory unit, 208 Command generation unit, 280 Relational expression, 300 Robot adjustment device, 302 Communication unit, 304 Communication unit, 306 Memory 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 (13)

A multi-joint robot having multiple axes;
A controller for controlling the robot;
Equipped with
The controller:
a sensor value information acquisition unit that acquires sensor value information for a hand or the plurality of axes of the robot when the robot is operating;
an axis-related information acquisition unit that acquires axis-related information related to the plurality of axes when the robot is operating;
an adjustment unit that adjusts elements of the relational equation by an optimization method so as to reduce an error between the sensor value information and calculation information calculated from the axis-related information and a relational equation indicating a relationship between the multiple axes and the hand of the robot;
A storage unit that stores the relational expression;
a command generating 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 the adjustment unit adjusts elements of the relational expression by performing black-box optimization so as to reduce an error between the calculation information and the sensor value information. The robot system according to claim 2 , wherein the adjustment unit adjusts elements of the relational expression by performing Bayesian optimization so as to reduce an error between the calculation information and the sensor value information. The robot system according to claim 2 , wherein the adjustment unit executes the black-box optimization by limiting a search range to a predetermined range centered on a setting value of the robot. the sensor value information includes a sensor value measured by a sensor that measures a tip end 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 calculation information includes rotation angles of the plurality of axes, disturbance torques of the plurality of axes, and an estimated hand force, an estimated velocity, an estimated acceleration, or an estimated position of the robot calculated by the relational expressions;
5. The robot system according to claim 1, wherein the command generation unit generates a motion command for the robot by using the estimated hand force, the estimated velocity, the estimated acceleration, or the estimated position included in the calculation information, without using a sensor that measures a hand of the robot. the sensor value information includes a sensor value measured by a force sensor that measures a hand force of the robot,
the calculation 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 a motion command for the robot by using the estimated hand force included in the calculation information without using a force sensor.
The robot system according to claim 5 . the relational expression includes, for each of the plurality of axes, a plurality of elements associated with the axis;
The robot system according to claim 1 , wherein the adjustment unit adjusts the elements associated with each of the plurality of axes in an order in which the plurality of axes are connected. The relationship includes a Jacobian matrix:
The robot system according to claim 1 , wherein the adjustment unit adjusts the elements constituting the Jacobian matrix so as to reduce an error between the calculation information and the sensor value information. the relational expression includes a transmission efficiency of the plurality of shafts,
The robot system according to claim 1 , wherein the adjustment unit adjusts the transmission efficiency of the plurality of axes so as to reduce an error between the calculation information and the sensor value information. The robot system according to claim 7 , wherein the adjustment unit adjusts the elements associated with each of the plurality of axes in an order from a base side to a tip side. a sensor value information acquisition unit that acquires sensor value information for a hand or the multiple axes of a multi-joint type robot having multiple axes when the robot is in operation;
an axis-related information acquisition unit that acquires axis-related information related to the plurality of axes when the robot is operating;
and an adjustment unit that adjusts elements of the relational equation by an optimization method so as to reduce an error between the sensor value information and calculation information calculated from the axis-related information and a relational equation indicating a relationship between the multiple axes and the hand of the robot. A program for causing a computer to function as the robot adjustment device according to claim 11 . 1. A computer-implemented method for manufacturing a multi-axis articulated robot, comprising the steps of:
an information acquisition step of acquiring sensor value information for the hand of the robot or the plurality of axes when the robot operates, and axis-related information related to the plurality of axes when the robot operates;
and an adjustment step of adjusting elements of the relational equation by an optimization method so as to reduce an error between the sensor value information and calculation information calculated from the axis related information and a relational equation indicating a relationship between the multiple axes and the hand of the robot.

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