KR102847965B1 - Calibration model construction method, calibration model and 6-axes control apparatus including the calibration model - Google Patents

Abstract

The present invention relates to a calibration model construction method, a calibration model, and a 6-axis control device including the calibration model, and provides a calibration model construction method for comparing an output value arithmetically output from an input value input to each axis in a device controlling multiple axes with an actual measurement value to compensate so that the measurement value converges to the output value, the calibration model construction method comprising: a first module connection step in which a detection unit connected to each axis included in the multiple axes is connected to a first module that receives detection information detected from the detection unit; a data reception step in which the first module receives the detection information from the detection unit; a correction model construction step in which a learning unit receives the detection information converted into a digital signal from the first module and constructs a correction model; a first module application step in which the correction model constructed from the learning unit is applied to the first module, and the correction model is transmitted to the first module; and a correction data output step in which the correction model applied to the first module performs predetermined processing on the detection information transmitted from the detection unit and outputs correction data.

Description

Calibration model construction method, calibration model, and 6-axis control apparatus including the calibration model {CALIBRATION MODEL CONSTRUCTION METHOD, CALIBRATION MODEL AND 6-AXES CONTROL APPARATUS INCLUDING THE CALIBRATION MODEL}

The present invention relates to a calibration model construction method, a calibration model, and a 6-axis control device including the calibration model.

Calibration is typically applied to various devices to compensate for errors in measurements and other operations. In particular, axes that move in multiple directions can be affected by external forces or mechanical errors, resulting in discrepancies between the measured values and the output values even though they correspond to the input values. This discrepancy increases as the number of axes increases. For example, in a device that drives six axes, which is a combination of linear and rotational displacements, any external force or self-weight applied in one direction will induce displacement in the other connected directions, resulting in errors in various directions.

These errors reduce the precision and durability of the device, making it difficult to use effectively in industrial environments. While various methods have been implemented to compensate for these errors, they do not occur within a consistent error range across various directions, but rather through complex error-causing factors. Therefore, proactive measures are needed to compensate for these errors.

Republic of Korea Patent Publication No. 10-2020-0012596 (February 5, 2020)

One embodiment of the present invention aims to construct a model capable of compensating for the difference between input values and measured values.

One embodiment of the present invention aims to improve the accuracy of a 6-axis control device by applying a model that compensates for the difference between an input value and a measured value.

The present invention relates to a calibration model construction method, a calibration model, and a 6-axis control device including the calibration model, and provides a calibration model construction method for comparing an output value arithmetically output from an input value input to each axis in a device controlling multiple axes with an actual measurement value to compensate so that the measurement value converges to the output value, the calibration model construction method comprising: a first module connection step in which a detection unit connected to each axis included in the multiple axes is connected to a first module that receives detection information detected from the detection unit; a data reception step in which the first module receives the detection information from the detection unit; a correction model construction step in which a learning unit receives the detection information converted into a digital signal from the first module and constructs a correction model; a first module application step in which the correction model constructed from the learning unit is applied to the first module, and the correction model is transmitted to the first module; and a correction data output step in which the correction model applied to the first module performs predetermined processing on the detection information transmitted from the detection unit and outputs correction data.

And, the multi-axis is axes in the X, Y, and Z directions in three dimensions, and the detection unit may include a first axis sensor that detects a force occurring in an axial direction in the X direction, a first torque sensor that detects a torque occurring in an axis in the X direction, a second axis sensor that detects a force occurring in an axial direction in the Y direction, a second torque sensor that detects a torque occurring in an axis in the Y direction, a third axis sensor that detects a force occurring in an axial direction in the Z direction, and a third torque sensor that detects a torque occurring in an axis in the Z direction.

In addition, if the normal state of the first module is detected after the correction data output step, the correction data is transmitted to a control device including a multi-axis, and if an abnormal state is detected, the correction data can be transmitted to the control device after the second module execution step in which the second module that produces the correction data is performed is performed.

In addition, the first module may include a first memory in which a correction model transmitted from the learning unit is stored, the detection unit may include a built-in memory in which the correction model transmitted from the first memory is stored, and the second module may further include a second memory in which the correction model transmitted from the built-in memory is stored.

A calibration model is provided that compares an output value arithmetically output from an input value input to each axis in a device for controlling multiple axes with an actual measured value to compensate so that the measured value converges to the output value, the calibration model comprising: a sensing unit including an axis sensor that detects a force acting in at least one axial direction and a torque sensor that detects a torque acting on at least one axis; a first module including a first converter that converts the sensed information detected by the sensing unit from an analog state to a digital state and a communication unit that transmits the converted sensed information; and a learning unit including a training unit that repeatedly learns an error by comparing the sensed information transmitted from the communication unit with an input value, and a transmission unit that transmits a correction model constructed by learning through the training unit to the first module; wherein the first module further includes a first memory that stores the correction model transmitted from the transmission unit.

And, when the correction model is stored in the first memory, the first memory can receive the detection information transmitted from the detection unit to the first module from the first converter and transmit the correction data calculated through the correction model to a control device including a multi-axis.

In addition, if the normal state of the first module is detected after the correction data output step, the correction data is transmitted to a control device including a multi-axis, and if an abnormal state is detected, a second module may be further included that produces the correction data by replacing the first module.

In addition, the first module may include a first memory in which a correction model transmitted from the learning unit is stored, the detection unit may include a built-in memory in which the correction model transmitted from the first memory is stored, and the second module may further include a second memory in which the correction model transmitted from the built-in memory is stored.

In addition, the second module further includes a second converter that converts the detection information from an analog state to a digital state, and when the second module is activated, the detection information from the detection unit is transmitted to the second converter, and the detection information converted from the second converter can transmit correction data to the control device through the second memory.

A six-axis control device is provided, which is driven by the calibration model construction method described above or driven by the calibration model described above.

According to one embodiment of the present invention, a calibration model construction method for constructing a model capable of compensating for a difference between an input value and a measured value can be provided.

According to one embodiment of the present invention, a six-axis control device to which a model for compensating for the difference between an input value and a measured value is applied can be provided.

FIG. 1 is a flowchart illustrating a method for constructing a calibration model according to one embodiment of the present invention.
Figure 2 is a conceptual diagram illustrating a calibration model according to one embodiment of the present invention.
FIG. 3 is a schematic diagram showing that data corrected through a constructed calibration model according to one embodiment of the present invention is transmitted to a control device.
FIG. 4 is a diagram illustrating a process of building a calibration model through a second module replacing a first module according to one embodiment of the present invention.
FIG. 5 is a schematic diagram showing the construction of a calibration model through a second module according to one embodiment of the present invention.

Hereinafter, specific embodiments of the present invention will be described with reference to the drawings. However, these are merely examples and the present invention is not limited thereto.

In describing the present invention, detailed descriptions of known technologies related to the present invention will be omitted if they are deemed to unnecessarily obscure the gist of the invention. Furthermore, the terms described below are defined based on their functions within the present invention and may vary depending on the intent or custom of the user or operator. Therefore, their definitions should be based on the overall content of this specification.

The technical idea of the present invention is determined by the claims, and the following examples are merely a means of efficiently explaining the technical idea of the present invention to a person having ordinary skill in the technical field to which the present invention belongs.

Below, a calibration model is described that compares the output values arithmetically output from the input values input to each axis in a device controlling multiple axes with actual measured values, thereby compensating for the measured values to converge to the output values. The calibration model can be applied to a multi-axis control device (400) to enable operation with improved precision.

FIG. 1 is a flowchart illustrating a method for constructing a calibration model according to one embodiment of the present invention.

Referring to FIG. 1, a calibration model construction method according to an embodiment of the present invention includes a first module connection step (S10) in which a detection unit (100) connected to each axis included in a multi-axis is connected to a first module (200) that receives detection information detected from the detection unit (100), a data reception step (S20) in which the first module (200) receives detection information from the detection unit (100), a correction model construction step (S30) in which the detection information converted into a digital signal from the first module (200) is received by the learning unit (300) to construct a correction model, a first module application step (S40) in which the correction model constructed from the learning unit (300) is applied to the first module (200), a data storage step (S50) in which the correction model is stored in the first memory (222) of the first module (200), and the correction model applied to the first module (200) is stored in the data storage step (S50). It includes a correction data output step (S60) that processes the detection information transmitted from the detection unit (100) in a predetermined manner and outputs correction data.

The above steps are performed sequentially and include a process of constructing a correction model. The correction model can be constructed based on the detection information transmitted from the detection unit (100). Specifically, the multi-axes are axes in the X, Y, and Z directions in three dimensions, and the detection unit (100) may include a first axis sensor (111) that detects a force generated in an axial direction in the X direction, a first torque sensor (112) that detects a torque generated in an axis in the X direction, a second axis sensor (121) that detects a force generated in an axial direction in the Y direction, a second torque sensor (122) that detects a torque generated in an axis in the Y direction, a third axis sensor (131) that detects a force generated in an axial direction in the Z direction, and a third torque sensor (132) that detects a torque generated in an axis in the Z direction.

When a force is applied in one or more directions among multiple axes, the axes that are directly or indirectly linked to each other can transmit the force to the adjacent axes. Therefore, even if a force is transmitted in the X direction, for example, the force can also be influenced in the Y and Z directions. For example, displacement of one end of a control device (400) connected through multiple axes can occur in the Z and Y directions even when only a force is applied in the X direction. Furthermore, not only displacement in the axial direction but also torque occurring in the torsional direction of the axes can occur depending on the structure in which the multiple axes are connected, and the displacement of one end of the control device (400) can be changed by the torque.

If an abnormality in the first module (200) is detected in each of the aforementioned steps, the second module (500) may be driven to generate a correction model, and correction data may be generated using the generated correction module. The generated correction data is transmitted to the control device (400) so that the control device (400) can implement an output value corresponding to the input value.

The configuration for implementing the above calibration model construction method is as described with reference to Figure 2 below.

Figure 2 is a conceptual diagram illustrating a calibration model according to one embodiment of the present invention.

Referring to FIG. 2, a calibration model according to one embodiment of the present invention includes a sensing unit (100), a first module (200), and a learning unit (300). Specifically, the sensing unit (100) includes an axis sensor that detects a force acting in at least one axial direction and a torque sensor that detects a torque acting on at least one axis. In addition, the first module (200) includes a first converter (210) that converts the sensing information detected by the sensing unit (100) from an analog state to a digital state, and a communication unit (221) that transmits the converted sensing information. In addition, the learning unit (300) includes a training unit (310) that repeatedly learns an error by comparing the sensing information transmitted from the communication unit (221) with an input value, and a transmission unit (320) that transmits a correction model constructed by being learned through the training unit (310) to the first module (200).

Here, the first module (200) may further include a first memory (222) that stores a correction model transmitted from the transmission unit (320). Accordingly, the first module (200) stores a correction model generated based on the detection information, and at the same time, receives the detection information transmitted from the detection unit (100) to the first module (200) from the first converter (210) and transmits correction data calculated through the correction model to a control device (400) including multiple axes. The correction data may be calculated through the correction model stored in the first memory (222) and transmitted to the control device (400).

The second module (500) described above can be operated in place of the first module (200) when the first module (200) is detected as being in an abnormal state. That is, the first module (200) and the second module (500) can be selectively operated. To this end, the second module (500) can be further included from the above-described embodiment, and the transmission path of the correction information model and the detection information can be changed. In this regard, this will be described in detail later with reference to FIGS. 3 and 4 below.

FIG. 3 is a schematic diagram showing that data corrected through a constructed calibration model according to one embodiment of the present invention is transmitted to a control device (400), and FIG. 4 is a diagram showing a process of constructing a calibration model through a second module (500) replacing a first module (200) according to one embodiment of the present invention.

Referring to FIGS. 3 and 4, if the normal state of the first module (200) is detected after the correction data output step described through FIG. 1, the correction data is transmitted to a control device (400) including a multi-axis, and if an abnormal state is detected, a second module (500) that produces correction data by replacing the first module (200) may be further included. Here, the normal state may be determined based on satisfaction or dissatisfaction based on a predetermined error range.

The above-determined error range can be determined as an abnormal state when the error rate exceeds 0% or 10% in which the difference between the arithmetic output value according to the input value and the actual measurement value measured by the detection unit (100) is an error rate. Here, 0% is an ideal case where the output value and the measurement value are the same as the input value, but since the possibility is low, re-measurement can be performed through the second module (500) in the sense that re-measurement is requested. In addition, since a case exceeding 10% can be an upper limit rate of the error range selectively set by the user, it is not limited thereto.

The above measurement values may be for comparison between the measurement values detected by the first axis sensor (111) to the third axis sensor (131) and the first torque sensor (112) to the third torque sensor (132) and the output values. The first module (200) includes a first memory (222) in which a correction model transmitted from the learning unit (300) is stored, the detection unit (100) includes a built-in memory (140) in which a correction model transmitted from the first memory (222) is stored, and the second module (500) may further include a second memory (520) in which a correction model transmitted from the built-in memory (140) is stored.

In addition, the second module (500) further includes a second converter (510) that converts the detection information from an analog state to a digital state, and when the second module (500) is activated, the detection information from the detection unit (100) is transmitted to the second converter (510), and the converted detection information from the second converter (510) can transmit correction data to the control device (400) through the second memory (520).

Meanwhile, when the input value, which is output data, is performed through the control device (400), the correction model can continuously generate correction data from the detection information transmitted from the detection unit (100) and transmit it to the control device (400). That is, the flow for operating the control device (400) and the flow for generating the correction model may be different. In order to generate the correction model, there may be an operation flow that passes through the learning unit (300), and the flow for operating the control device (400) may have a flow that does not include the learning unit (300).

And, when the second module (500) replaces the first module (200), as illustrated in FIG. 4, a correction model can be newly generated separately from the one generated through the first module (200). That is, the steps of connecting the second module (P10), receiving data from the first memory (222) (P20), constructing a correction model (P30), applying the correction model to the second module (P40), and outputting correction data (P50) can be sequentially performed. Outputting the correction data (P50) may be a process of transmitting output data to the control device (400). Although the second module (500) partially plays the role of the learning unit (300), it is separately distinguished, and the model generated in the learning unit (300) can be used as a correction model in the first module (200) or the second module (500).

This will be explained in more detail with reference to Figure 5 below.

FIG. 5 is a schematic diagram showing the construction of a calibration model through a second module (500) according to one embodiment of the present invention.

Referring to FIG. 5, if the normal state of the first module (200) is detected after the correction data output step as described above, the correction data is transmitted to the control device (400) including the multi-axis, and if an abnormal state is detected, the correction data can be transmitted to the control device (400) after the execution step of the second module (500) in which the second module (500) that produces the correction data is performed.

The first module (200) includes a first memory (222) in which a correction model transmitted from the learning unit (300) is stored, the detection unit (100) includes a built-in memory (140) in which the correction model transmitted from the first memory (222) is stored, and the second module (500) may further include a second memory (520) in which the correction model transmitted from the built-in memory (140) is stored. That is, the correction model generated in the learning unit (300) can be shared by the first memory (222) and the second memory (520) to produce output data, and when the correction model is generated for the sharing, it can be configured to be backed up to the built-in memory (140), which is a component of the detection unit (100), through the first module (200).

According to this configuration, if a problem occurs in the first module (200) and normal operation becomes difficult, the correction module can be driven through the second module (500). At this time, the detection information from the detection unit (100) transmitted to the second module (500) is directly transmitted to the second converter (510) of the second module (500). That is, the detection information detected by the detection unit (100) can be transmitted from the second memory (520) of the second module (500) that stores the correction model, which is the backed-up information, to produce output data.

While representative embodiments of the present invention have been described in detail above, those skilled in the art will appreciate that various modifications to the above-described embodiments are possible without departing from the scope of the present invention. Therefore, the scope of the present invention should not be limited to the described embodiments, but should be defined not only by the claims set forth below but also by equivalents thereof.

S10: First module connection stage
S20: 6-axis data reception stage
S30: Calibration model construction stage
S40: Step of applying the correction model to the first module
S50: Data storage stage in the first memory
S60: Correction data output stage
S70: Step 1: Check for module failure
P00: Second module execution stage
P10: Second module connection step
P20: Data reception stage from module 1
P30: Calibration model construction stage
P40: Step of applying the correction model to the second module
P50: Correction data output stage
100: Detection unit
111: First axis sensor
112: First torque sensor
121: Second axis sensor
122: Second torque sensor
131: Third axis sensor
132: Third torque sensor
140: Built-in memory
200: Module 1
210: First converter
221: Communications Department
222: First Memory
300: Learning Department
310: Training Department
320: Transmission Department
400: Control unit
500: Module 2
510: Second converter
520: Second Memory

Claims (11)

In a method for constructing a calibration model that compares an output value arithmetically output from an input value input to each axis in a device controlling multiple axes with an actual measurement value and compensates so that the measurement value converges to the output value,
A first module connection step in which a first module that receives detection information detected from a detection unit is connected to a detection unit connected to each axis included in a multi-axis;
A data receiving step in which the first module receives the detection information from the detection unit;
A correction model construction step in which the learning unit receives the detection information converted into a digital signal from the first module and constructs a correction model;
A first module application step in which the correction model constructed from the learning unit is transferred to the first module and the correction model is applied to the first module; and
A correction data output step in which the correction model applied to the first module processes the detection information transmitted from the detection unit and outputs correction data;
A calibration model construction method in which, after the above correction data output step, if the normal state of the first module is detected, the correction data is transmitted to a control device including a multi-axis, and after the second module execution step in which the second module that produces the correction data is performed if an abnormal state is detected, the correction data is transmitted to the control device.
In claim 1,
The above multi-axis is the axes in the X, Y and Z directions in three dimensions,
The above detection unit,
A calibration model construction method comprising a first axis sensor that detects a force occurring in an axial direction in the X direction, a first torque sensor that detects a torque occurring on an axis in the X direction, a second axis sensor that detects a force occurring in an axial direction in the Y direction, a second torque sensor that detects a torque occurring on an axis in the Y direction, a third axis sensor that detects a force occurring in an axial direction in the Z direction, and a third torque sensor that detects a torque occurring on an axis in the Z direction.
delete In claim 1,
A calibration model construction method, wherein the first module includes a first memory in which the correction model transmitted from the learning unit is stored, the detection unit includes a built-in memory in which the correction model transmitted from the first memory is stored, and the second module further includes a second memory in which the correction model transmitted from the built-in memory is stored.
In a calibration model that compares the output value arithmetically output from the input value input to each axis in a device controlling multiple axes with the actual measurement value and compensates so that the measurement value converges to the output value,
A sensing unit including an axis sensor for detecting a force acting in at least one axial direction and a torque sensor for detecting a torque acting on at least one axis;
A first module including a first converter that converts the detection information detected by the above detection unit from an analog state to a digital state and a communication unit that transmits the converted detection information; and
A learning unit including a training unit that repeatedly learns errors by comparing the input values with the detection information transmitted from the communication unit, and a transmission unit that transmits a correction model constructed by learning through the training unit to the first module;
The above first module further includes a first memory that stores the correction model transmitted from the transmission unit,
When the above correction model is stored in the first memory,
The above first memory is,
The detection information transmitted from the detection unit to the first module is received from the first converter and the correction data calculated through the correction model is transmitted to the control device including the multi-axis.
A calibration model further comprising a second module that, when a normal state of the first module is detected after the above-mentioned correction data output step, transmits correction data to the control device including multi-axes, and when an abnormal state is detected, produces the correction data by replacing the first module.
delete delete In claim 5,
A calibration model, wherein the first module includes a first memory in which the correction model transmitted from the learning unit is stored, the detection unit includes a built-in memory in which the correction model transmitted from the first memory is stored, and the second module further includes a second memory in which the correction model transmitted from the built-in memory is stored.
In claim 8,
The above second module,
Further comprising a second converter that converts the above detection information from an analog state to a digital state,
When the above second module is activated,
A calibration model in which the detection information from the detection unit is transmitted to the second converter, and the detection information converted from the second converter is transmitted to the control device as correction data through the second memory.
A six-axis control device driven by the calibration model construction method described in claim 1.
A six-axis control device driven by the calibration model described in claim 5.