CN110039528A - A kind of industrial robot Zero calibration method based on dynamic learning - Google Patents
A kind of industrial robot Zero calibration method based on dynamic learning Download PDFInfo
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- CN110039528A CN110039528A CN201910197609.5A CN201910197609A CN110039528A CN 110039528 A CN110039528 A CN 110039528A CN 201910197609 A CN201910197609 A CN 201910197609A CN 110039528 A CN110039528 A CN 110039528A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/10—Programme-controlled manipulators characterised by positioning means for manipulator elements
- B25J9/1005—Programme-controlled manipulators characterised by positioning means for manipulator elements comprising adjusting means
- B25J9/1015—Programme-controlled manipulators characterised by positioning means for manipulator elements comprising adjusting means using additional, e.g. microadjustment of the end effector
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1679—Programme controls characterised by the tasks executed
- B25J9/1692—Calibration of manipulator
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Abstract
The present invention relates to a kind of industrial robot Zero calibration method based on dynamic learning, comprising: using multiple attitude datas of easy calibration tip acquisition industrial robot, record the joint angles of each posture and the XYZ distance of tool coordinates system；The terminal position expression formula of each posture is obtained by joint angles and XYZ distance, and the minimum distance error under multiple postures is acquired according to terminal position；Ask minimum distance error to the partial differential of joint angles deviator and XYZ deviator；Both of the aforesaid step is repeated, partial differential is iterated according to dynamical learning rate, until minimum distance error is less than 1mm；Joint angles deviator and the compensation of XYZ deviator are completed into Zero calibration to robot.This method acquires multiple attitude datas of industrial robot using easy calibration tip, and carries out iteratively faster processing to data using dynamic learning algorithm, makes nominal time control within 30 minutes, and stated accuracy is within 1mm.
Description
Technical field
The present invention relates to the scaling method technical field of industrial robot more particularly to a kind of industry based on dynamic learning
Robot Zero calibration method.
Background technique
Zero point is the benchmark that industrial robot judges self-position.Before robot factory, producer can be according to zero-bit mark
The zero point of manual setting robot.However, leading to the true zero of every robot due to having differences property of processing of robots process
Point position is not identical, so also needing to carry out accurate Zero calibration to robot.Current main Zero calibration method has
Laser tracker standardization, producer's Special assisting tool standardization etc..Such technology has the disadvantage that:
(1) device needed is expensive, and calibration process is cumbersome.By taking laser tracker as an example, although stated accuracy is very high,
But the price of device is generally 2,000,000 or more, and technical professional is needed to demarcate, the nominal time is average 1
Hour or so.
(2) existing all kinds of scaling methods are only applicable in the robot of single kind substantially.Such as producer's Special assisting tool
Scaling method, generally just for the method for certain robot special setting (such as SCARA demarcate, six axis welding calibration etc.), this
Kind method does not have generality, is also not necessarily suitable the robot of other producers.
Summary of the invention
The present invention proposes a kind of industrial robot Zero calibration method based on dynamic learning, utilizes easy calibration tip
Multiple attitude datas of industrial robot are acquired, and iteratively faster processing is carried out to data using dynamic learning algorithm, make to demarcate
Time controlled within 30 minutes, and stated accuracy solves existing scaling method device valuableness and calibration process within 1mm
Complicated disadvantage.
In order to solve the above technical problems, the technical scheme is that
A kind of industrial robot Zero calibration method based on dynamic learning comprising the steps of:
1) using multiple attitude datas of calibration tip acquisition industrial robot, the joint angles and work of each posture are recorded
Has the XYZ distance of coordinate system；
2) the terminal position expression formula of each posture is obtained by joint angles and XYZ distance, and is acquired according to terminal position
Minimum distance error under multiple postures；
3) ask minimum distance error to the partial differential of joint angles deviator and XYZ deviator；
4) step 2) and step 3) are repeated, partial differential is iterated according to dynamical learning rate, until minimum distance error
Less than 1mm；
5) joint angles deviator and the compensation of XYZ deviator are completed into Zero calibration to robot.
Further, in above-mentioned technical proposal, the step 1) the following steps are included:
11) prepare the demarcate bar of two single-ended points, a demarcate bar is installed to robot end, another is installed to machine
Fixed position outside people；
12) it allows robot end's demarcate bar to be directed at external demarcate bar, rotate industrial robot and acquires multiple industrial machines
The attitude data of people records the joint angles of each posture and the XYZ distance of tool coordinates system.
Further, in above-mentioned technical proposal, the step 2) the following steps are included:
21) terminal position of each posture is calculated in robot according to each DH in the DH coordinate system for establishing robot
Expression formula 1 under basis coordinates system,
P=f (a0,d0,a1,θ1,...,an,dn,an,θn, X, Y, Z) and formula 1
Wherein, ai,diIt is the link parameters of robot, θiIt is each axis joint angle, XYZ is tool coordinates system distance；
22) due to robot end always with fixed-point contact, so the XYZ value of each posture is identical, it is assumed that posture number
For N, then minimum distance error can indicate are as follows:
In above-mentioned technical proposal, " ask minimum distance error to the inclined of joint angles deviator and XYZ deviator in the step 3)
Differential " is specially to calculate minimum distance error to the partial differential Δ of joint angles deviator and XYZ deviator according to above-mentioned formula 1 and formula 2
Φ。
In above-mentioned technical proposal, the step 4) is specifically included:
41) to joint angles and XYZ setting difference dynamical learning rate k, M is the number of iterations, then:
43) it is constantly updated iteration, until minimum distance error is less than 1mm.
In conclusion the present invention provides a kind of industrial robot Zero calibration method based on dynamic learning, utilizes letter
Multiple attitude datas of easy calibration tip acquisition industrial robot, and iteratively faster is carried out to data using dynamic learning algorithm
Processing can make nominal time control within 30 minutes, and stated accuracy is within 1mm.This kind of scaling method does not need to hold high
Expensive apparatus does not need complicated demarcating steps yet, enormously simplifies demarcation flow, shorten the nominal time, improve
Stated accuracy.Meanwhile method proposed by the present invention is suitable for various four axis and the vertical multi-joint industrial robot of six axis, has general
All over applicability.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention without any creative labor, may be used also for those of ordinary skill in the art
To obtain other drawings based on these drawings.
Fig. 1 is the step flow chart of the method for the present invention；
Fig. 2 is industrial robot rotation attitude schematic diagram of the invention.
Specific embodiment
In order to preferably introduce method provided by the invention, this is introduced below in conjunction with attached drawing and table and specific embodiment
The technical solution of invention.
As shown in Figure 1, the present invention provides a kind of industrial robot Zero calibration method based on dynamic learning, comprising with
Lower step:
1) using multiple attitude datas of easy calibration tip acquisition industrial robot, the joint angle of each posture is recorded
The XYZ distance of degree and tool coordinates system；
2) the terminal position expression formula of each posture is obtained by joint angles and XYZ distance, and is acquired according to terminal position
Minimum distance error under multiple postures；
3) ask minimum distance error to the partial differential of joint angles deviator and XYZ deviator；
4) step 2) and step 3) are repeated, partial differential is iterated according to dynamical learning rate, until minimum distance error
Less than 1mm；
5) joint angles deviator and the compensation of XYZ deviator are completed into Zero calibration to robot.
Further, in above-mentioned technical proposal, the step 1) the following steps are included:
11) prepare the demarcate bar of two single-ended points, a demarcate bar is installed to robot end, another is installed to machine
Fixed position outside people；
12) it allows robot end's demarcate bar to be directed at external demarcate bar, rotate industrial robot and acquires multiple industrial machines
The attitude data of people records the joint angles of each posture and the XYZ distance of tool coordinates system.
Further, in above-mentioned technical proposal, the step 2) the following steps are included:
21) terminal position of each posture is calculated in robot according to each DH in the DH coordinate system for establishing robot
Expression formula 1 under basis coordinates system,
P=f (a0,d0,a1,θ1,...,an,dn,an,θn, X, Y, Z) and formula 1
Wherein, ai,diIt is the link parameters of robot, θiIt is each axis joint angle, XYZ is tool coordinates system distance；
22) due to robot end always with fixed-point contact, so the XYZ value of each posture is identical, it is assumed that posture number
For N, then minimum distance error can indicate are as follows:
In above-mentioned technical proposal, " ask minimum distance error to the inclined of joint angles deviator and XYZ deviator in the step 3)
Differential " is specially to calculate minimum distance error to the partial differential Δ of joint angles deviator and XYZ deviator according to above-mentioned formula 1 and formula 2
Φ。
In above-mentioned technical proposal, the step 4) is specifically included:
41) to joint angles and XYZ setting difference dynamical learning rate k, M is the number of iterations, then:
44) it is constantly updated iteration, until minimum distance error is less than 1mm.
Specifically it is exemplified below:
By taking 6 axis robots as an example.
Step 1):
Prepare the demarcate bar of two single-ended points, a demarcate bar is installed to robot end, and one is installed to outside robot
Fixed position, allows robot end's demarcate bar to be directed at external demarcate bar, rotates industrial robot and acquires several industrial machines
The attitude data of people acquires 10 data instances herein.Record each posture joint angles θ and tool coordinates system XYZ away from
From.
Industrial robot rotation attitude example is as shown in Figure 2.
Step 2):
The DH coordinate system of robot is established, is generated such as the parameter list in table 1.
Table 1
| i | αi-1(°) | ai-1(mm) | di(mm) | θi | θiInitial value | Reduction ratio |
| 1 | 0° | 0 | 0 | θ1 | 0° | 121 |
| 2 | α1=-90 ° | a1=180 | 0 | θ2 | -90° | 121 |
| 3 | 0° | a2=570 | d3=0 | θ3 | 0° | 121 |
| 4 | α3=-90 ° | a3=155 | d4=730 | θ4 | 0° | 100 |
| 5 | α4=90 ° | 0 | 0 | θ5 | 0° | 80 |
| 6 | α5=-90 ° | 0 | 0 | θ6 | 0° | 80 |
Expression formula 1 of the terminal position of each posture under robot basis coordinates system is calculated according to each DH,
px=c1(a1+a2c2+a3c23-d4s23)-d3s1
py=s1(a1+a2c2+a3c23-d4s23)+d3c1
pz=-a3s23-a2s2-d4c23
Wherein, c1 indicates that cos θ 1, s1 indicate that sin θ 1, c12 indicate cos (θ 1+ θ 2), and s12 indicates sin (θ 1+ θ 2).
Due to robot end always with fixed-point contact, so the XYZ value of each posture is identical, it is assumed that posture number is
10, then minimum distance error can indicate are as follows:
Step 3):
Ask minimum distance error to the partial differential of joint angles deviator and XYZ deviator.
Specifically, minimum distance error is calculated to the partial differential Δ of joint angles deviator and XYZ deviator according to formula 1 and formula 2
Φ。
Step 4):
Repeat abovementioned steps 2) and step 3), partial differential is iterated according to dynamical learning rate, until minimum range is missed
Difference is less than 1mm.
Specifically, to joint angles and XYZ setting difference dynamical learning rate k, then:
Wherein M is the number of iterations, is constantly updated iteration, until minimum distance error is less than 1mm.
Step 5):
Joint angles deviator and the compensation of XYZ deviator are completed into Zero calibration to robot.
In conclusion a kind of industrial robot Zero calibration method based on dynamic learning provided by the invention, utilizes letter
Multiple attitude datas of easy calibration tip acquisition industrial robot, and iteratively faster is carried out to data using dynamic learning algorithm
Processing can make nominal time control within 30 minutes, and stated accuracy is within 1mm.This kind of scaling method does not need to hold high
Expensive apparatus does not need complicated demarcating steps yet, enormously simplifies demarcation flow, shorten the nominal time, improve
Stated accuracy.Meanwhile method proposed by the present invention is suitable for various four axis and the vertical multi-joint industrial robot of six axis, has general
All over applicability.
The above shows and describes the basic principle, main features and advantages of the invention.The technology of the industry
Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and the above embodiments and description only describe this
The principle of invention, without departing from the spirit and scope of the present invention, various changes and improvements may be made to the invention, these changes
Change and improvement all fall within the protetion scope of the claimed invention.The claimed scope of the invention by appended claims and its
Equivalent thereof.
Claims (5)
1. a kind of industrial robot Zero calibration method based on dynamic learning, which comprises the following steps:
1) using multiple attitude datas of calibration tip acquisition industrial robot, the joint angles and tool for recording each posture are sat
Mark the XYZ distance of system；
2) the terminal position expression formula of each posture is obtained by joint angles and XYZ distance, and is acquired according to terminal position multiple
Minimum distance error under posture；
3) ask minimum distance error to the partial differential of joint angles deviator and XYZ deviator；
4) step 2) and step 3) are repeated, partial differential is iterated according to dynamical learning rate, until minimum distance error is less than
1mm；
5) joint angles deviator and the compensation of XYZ deviator are completed into Zero calibration to robot.
2. the industrial robot Zero calibration method according to claim 1 based on dynamic learning, which is characterized in that described
Step 1) the following steps are included:
11) prepare the demarcate bar of two single-ended points, a demarcate bar is installed to robot end, another is installed to outside robot
Fixed position；
12) it allows robot end's demarcate bar to be directed at external demarcate bar, rotate industrial robot and acquires multiple industrial robots
Attitude data records the joint angles of each posture and the XYZ distance of tool coordinates system.
3. the industrial robot Zero calibration method according to claim 2 based on dynamic learning, which is characterized in that described
Step 2) the following steps are included:
21) terminal position of each posture is calculated in robot base according to each DH in the DH coordinate system for establishing robot
Expression formula 1 under mark system,
P=f (a0,d0,a1,θ1,...,an,dn,an,θn, X, Y, Z) and formula 1
Wherein, ai,diIt is the link parameters of robot, θiIt is each axis joint angle, XYZ is tool coordinates system distance；
22) due to robot end always with fixed-point contact, so the XYZ value of each posture is identical, it is assumed that posture number be N,
Then minimum distance error can indicate are as follows:
4. the industrial robot Zero calibration method according to claim 3 based on dynamic learning, it is characterised in that:
" asking minimum distance error to the partial differential of joint angles deviator and XYZ deviator " is specially according to above-mentioned in the step 3)
Formula 1 and formula 2 calculate minimum distance error to the partial differential ΔΦ of joint angles deviator and XYZ deviator.
5. the industrial robot Zero calibration method according to claim 1 based on dynamic learning, which is characterized in that described
Step 4) specifically includes:
41) to joint angles and XYZ setting difference dynamical learning rate k, M is the number of iterations, then:
42) it is constantly updated iteration, until minimum distance error is less than 1mm.
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| CN110782550A (en) * | 2019-09-20 | 2020-02-11 | 腾讯科技（深圳）有限公司 | Data acquisition method, device and equipment |
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| CN112238340A (en) * | 2020-10-26 | 2021-01-19 | 广东三扬机器人有限公司 | Calibration method of three-axis screw machine |
| CN112277009A (en) * | 2020-09-15 | 2021-01-29 | 唐山英莱科技有限公司 | Robot positioning method and computer readable storage medium |
| CN112815887A (en) * | 2020-12-30 | 2021-05-18 | 廊坊市亿创科技有限公司 | Industrial robot end tool coordinate system calibration method |
| CN113459094A (en) * | 2021-06-23 | 2021-10-01 | 佛山智能装备技术研究院 | Industrial robot tool coordinate system and zero point self-calibration method |
| CN114102595A (en) * | 2021-11-29 | 2022-03-01 | 苏州艾利特机器人有限公司 | Robot calibration method, calibration assembly and storage medium |
| CN114734435A (en) * | 2022-03-24 | 2022-07-12 | 苏州艾利特机器人有限公司 | Encoder calibration method, device and system based on hypersphere |
| CN115256375A (en) * | 2022-07-08 | 2022-11-01 | 广东工业大学 | Industrial robot-based end manipulator pose calibration method and system |
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| CN112815887B (en) * | 2020-12-30 | 2022-10-21 | 廊坊市亿创科技有限公司 | Industrial robot end tool coordinate system calibration method |
| CN112815887A (en) * | 2020-12-30 | 2021-05-18 | 廊坊市亿创科技有限公司 | Industrial robot end tool coordinate system calibration method |
| CN113459094B (en) * | 2021-06-23 | 2022-06-14 | 佛山智能装备技术研究院 | Industrial robot tool coordinate system and zero point self-calibration method |
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| CN114102595A (en) * | 2021-11-29 | 2022-03-01 | 苏州艾利特机器人有限公司 | Robot calibration method, calibration assembly and storage medium |
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| CN114734435A (en) * | 2022-03-24 | 2022-07-12 | 苏州艾利特机器人有限公司 | Encoder calibration method, device and system based on hypersphere |
| CN114734435B (en) * | 2022-03-24 | 2023-09-19 | 苏州艾利特机器人有限公司 | Method, device and system for calibrating encoder based on hypersphere |
| CN115256375A (en) * | 2022-07-08 | 2022-11-01 | 广东工业大学 | Industrial robot-based end manipulator pose calibration method and system |
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