CN114905521A - Robot origin position calibration method and device, electronic equipment and storage medium - Google Patents

Robot origin position calibration method and device, electronic equipment and storage medium Download PDF

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Publication number
CN114905521A
CN114905521A CN202210838418.4A CN202210838418A CN114905521A CN 114905521 A CN114905521 A CN 114905521A CN 202210838418 A CN202210838418 A CN 202210838418A CN 114905521 A CN114905521 A CN 114905521A
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target point
robot
calibration
module
value
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CN114905521B (en
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查文斌
丁磊
姚庭
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Faoyiwei Suzhou Robot System Co ltd
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Faoyiwei Suzhou Robot System Co ltd
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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
    • 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
    • B25J9/1602—Programme controls characterised by the control system, structure, architecture
    • B25J9/161—Hardware, e.g. neural networks, fuzzy logic, interfaces, processor

Abstract

The invention relates to the technical field of robots, and discloses a method and a device for calibrating an original point position of a robot, electronic equipment and a storage medium, wherein the method comprises the following steps: configuring an end effector tool of the robot and establishing a target point tool; moving the end effector to the target point at 20 different poses of the robot on 4 planes, respectively; obtaining the coordinates of the end tool by using a least square method; obtaining a virtual target point; taking the virtual target point as an initial value, performing theoretical calculation of a random method, obtaining a weight, and obtaining a new virtual target after resampling by adopting resampling; iterating and calibrating an equation by combining Jacobian pseudo-inverse with a damping value; and calculating the matching degree of the joint angle under each posture and the target point joint angle until the matching degree is greater than or equal to a set threshold value. The robot origin position calibration method provided by the invention has the advantages of more stable calibration, stronger anti-interference performance and capability of searching for the target point more quickly.

Description

Robot origin position calibration method and device, electronic equipment and storage medium
Technical Field
The invention relates to the technical field of robots, in particular to a method and a device for calibrating an original point position of a robot, electronic equipment and a storage medium.
Background
With the rapid development of the robot technology, the functional requirements of people on the robot are continuously expanded, the motion controller is the premise for realizing various functions of the robot, and the calibration of the origin position of the robot is the basic condition of the motion controller, so the calibration and development of the origin position of the robot face huge challenges.
However, in the conventional origin position calibration technology, a manual calibration method occupies a main position, but the origin position calibration error is greatly influenced by manual assembly, and the efficiency is low. Therefore, although the origin position calibration method using a measuring tool such as a laser tracker or a calibration plate has been developed, the cost is high and is limited by the configuration of the robot arm. In addition, the traditional origin position calibration method is weak in stability and the anti-interference performance needs to be improved. In addition, in the actual operation process, it often takes a long time to find the calibration target point.
Therefore, how to improve the stability of the robot origin position calibration method with low cost and quickly find out the calibration target point is a problem to be solved.
Disclosure of Invention
The embodiment of the invention aims to provide a method and a device for calibrating an origin position of a robot, which can improve the stability of the method for calibrating the origin position of the robot at low cost and quickly find out a calibration target point.
In a first aspect, an embodiment of the present invention provides a method for calibrating an origin position of a robot, including:
step S1, configuring the end effector tool of the robot, establishing a target point tool and fixing;
step S2, moving the end effector to the target point on 4 planes of the target point respectively in 20 different postures of the robot;
step S3, recording the information of each joint aiming at the target point under the 20 different postures;
step S4, acquiring the pose of each joint state by using the positive kinematics of the robot; obtaining the coordinate of the end tool by using a least square method according to the pose of each joint state;
step S5, obtaining the position of the target point under each posture through the tool coordinate; obtaining a virtual target point by combining the target point positions obtained under all postures with a mean value theory;
step S6, taking the virtual target point as an initial value, performing theoretical calculation of a random method, obtaining a weight, and obtaining a new virtual target point after resampling by adopting resampling;
step S7, establishing 20 calibration equations of different postures below the reference target point by taking the resampled new virtual target point as the reference target point; performing joint angle iteration by combining the Jacobian pseudo-inverse with a damping value;
step S8, calculating the matching degree of the joint angle under each posture and the target point joint angle, and finishing calibration if the matching degree is larger than or equal to a set threshold value; if the matching degree is less than the set threshold, repeating steps S4-S7 until the matching degree is greater than or equal to the set threshold.
Further, in step S6, the resampling method specifically includes:
step S61 is calculating a plurality of new virtual random target points,
Figure 100002_DEST_PATH_IMAGE001
in the formula (I), wherein,
Figure 709477DEST_PATH_IMAGE002
as a new virtual random target point,iis 1,2, …num，
Figure 100002_DEST_PATH_IMAGE003
As a virtual target point, the target point,randin the form of a random function,numis the number of random values;
step S62, difference value weighting is carried out:
Figure 232862DEST_PATH_IMAGE004
，
Figure 100002_DEST_PATH_IMAGE005
as an initial weight value, the weight value,expin order to be an exponential function of the,jthe number of the channels being 1,2, … 20,
Figure 538073DEST_PATH_IMAGE006
is the corresponding target point position under the 1 st to 20 th postures under the base coordinate system; computing
Figure 100002_DEST_PATH_IMAGE007
，
Figure 389485DEST_PATH_IMAGE008
Is the weight of the virtual target point;
step S63, normalization processing:
Figure 100002_DEST_PATH_IMAGE009
，
Figure 805554DEST_PATH_IMAGE010
is a normalized weight value;
step S64, weight sorting:
Figure 100002_DEST_PATH_IMAGE011
whereinIFor the weight corresponding to the gesture sequence, usesortThe function sorts the weight sequence from small to large;
step S65, calculating the gesture sequence corresponding to the maximum weight
Figure 449025DEST_PATH_IMAGE012
SelectingI0.8 th in the sequencenumTonumA numerical value;
step S66, resampling calculation:
Figure 100002_DEST_PATH_IMAGE013
wherein, in the step (A),
Figure 659558DEST_PATH_IMAGE014
for the new virtual target point after resampling,
Figure 100002_DEST_PATH_IMAGE015
for the summation of the new maximum random virtual target points,
Figure 263846DEST_PATH_IMAGE016
is composed of
Figure 342660DEST_PATH_IMAGE012
The number of virtual target point sequences in (c).
In a second aspect, an embodiment of the present invention provides a device for calibrating an origin position of a robot, including:
a configuration module for configuring the end effector tool of the robot, establishing a target point tool and fixing;
a pose module moving the end effector to the target point at 20 different poses of the robot on 4 planes of the target point, respectively;
the alignment module is used for recording the information of each joint aligning the target point under the 20 different postures;
the coordinate module is used for acquiring the pose of each joint state by using the positive kinematics of the robot; obtaining the coordinate of the end tool by using a least square method according to the pose of each joint state;
the virtual target point module is used for obtaining the position of a target point under each posture through the tool coordinates; obtaining a virtual target point by combining the target point positions obtained under all postures with a mean value theory;
the resampling module is used for carrying out theoretical calculation of a random method by taking the virtual target point as an initial value, obtaining a weight and obtaining a new virtual target point after resampling by adopting resampling;
the iteration module is used for establishing 20 calibration equations with different postures below a reference target point by taking the resampled new virtual target point as the reference target point; performing joint angle iteration by combining Jacobian pseudo-inverse with a damping value;
the matching module is used for calculating the matching degree of the joint angle under each posture and the target point joint angle, and finishing calibration if the matching degree is greater than or equal to a set threshold value; if the matching degree is smaller than the set threshold, repeating the coordinate module to the iteration module until the matching degree is larger than or equal to the set threshold.
Further, the resampling module specifically includes:
a new random target point module calculates a plurality of new virtual random target points,
Figure 100002_DEST_PATH_IMAGE017
in the formula (I), wherein,
Figure 716004DEST_PATH_IMAGE018
as a new virtual random target point,iis 1,2, …num，
Figure 100002_DEST_PATH_IMAGE019
As a virtual target point, the target point,randin the form of a random function,numis the number of random values;
the right solving module performs difference right solving:
Figure 222072DEST_PATH_IMAGE020
，
Figure 100002_DEST_PATH_IMAGE021
as an initial weight value, the weight value,expin order to be an exponential function of the,jthe number of the channels being 1,2, … 20,
Figure 313655DEST_PATH_IMAGE022
the corresponding target point positions under the 1 st to 20 th postures under the base coordinate system; computing
Figure 100002_DEST_PATH_IMAGE023
，
Figure 71527DEST_PATH_IMAGE024
Is the weight of the virtual target point;
normalization module for normalization:
Figure 100002_DEST_PATH_IMAGE025
，
Figure 424011DEST_PATH_IMAGE010
is a normalized weight;
a sorting module, wherein the weight sorting comprises the following steps:
Figure 241925DEST_PATH_IMAGE026
whereinIFor the weight corresponding to the gesture sequence, usesortThe function sorts the weight sequence from small to large;
attitude sequence module for calculating maximum weight value corresponding attitude sequence
Figure 555226DEST_PATH_IMAGE012
SelectingI0.8 in the sequencenumTonumA numerical value;
a virtual target point resampling module:
Figure 100002_DEST_PATH_IMAGE027
wherein, in the process,
Figure 975843DEST_PATH_IMAGE028
for the new virtual target point after resampling,
Figure 100002_DEST_PATH_IMAGE029
for the summation of the new maximum random virtual target points,
Figure 323779DEST_PATH_IMAGE030
is composed of
Figure 171649DEST_PATH_IMAGE012
The number of virtual target point sequences in (c).
In a third aspect, an embodiment of the present invention provides an electronic device, including: the system comprises a processor, a memory and a bus, wherein the processor is connected with the memory through the bus, and the memory stores computer readable instructions which are used for realizing the steps of the method provided by the first aspect and any one of the implementation modes of the first aspect when being executed by the processor.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored, to implement the steps in the method provided in any one of the foregoing first aspect and the first embodiment of the first aspect.
The method has the following advantages that: (1) compared with the traditional calibration tool, the 20-point calibration tool utilizing the 4 planes is more stable, the anti-interference performance is stronger, and when a certain deviation exists in the position of the origin, the coordinates of the calibration tool are more accurate, so that the target point is more favorably found. (2) Reducing the iteration times by combining a resampling method and searching a target point more quickly; (3) the improved Jacobian inverse solution method can more effectively approach a true value without reselecting the configuration of the mechanical arm, so that the stability of the algorithm is improved; (4) and in case of non-ideal conditions, fine calibration can be performed again after manual fine adjustment is performed according to the first result.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments of the present invention will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic diagram of a robot origin position calibration method according to an embodiment of the present invention;
fig. 2 is a layout diagram of 4 planes in a robot origin position calibration method according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a robot origin position calibration apparatus according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
In the existing robot calibration technology, a manual calibration mode occupies a main position, but the origin position calibration error is greatly influenced by manual assembly, and the efficiency is low. Therefore, the origin position calibration method using a measuring tool such as a laser tracker or a calibration plate has been developed, but the cost is high and is limited by the configuration of the robot arm. In addition, the stability of the traditional calibration method is weak, and the anti-interference performance needs to be improved. In addition, in the actual operation process, it often takes a long time to find the calibration target point.
The embodiment provides a method for calibrating an origin position of a robot, which comprises the following steps:
step S1, configuring the end effector tool of the robot, establishing a target point tool and fixing; as shown in fig. 1, the left drawing is the fixed target point, and the right drawing is the robot end effector located on the robot arm.
Step S2, moving the end effector to the target point on 4 planes of the target point respectively in 20 different postures of the robot; the process of setting the plane is specifically shown in fig. 1 and 2, and the specific process is as follows:
the plane distribution method can select a comfortable plane according to the configuration of the robot, and the plane has calibration points with larger configuration difference, so that the required main features can be extracted more stably. As shown in fig. 1, there are planes 1 intersecting in 45 degrees, respectively, where the attitude motion of the planar robot is affected to some extent, and 5 fixed marks are marked as (1, 5); parallel to the tip plane 2, because the robot has wide attitude motion space, 9 calibration points are marked fixedly and are marked as (2, 9); intersecting the 45-degree direction plane 3, the plane is marked with 5, and is marked as (3, 5); and (4) because the motion attitude of the plane robot is only related to six joints, the influence of a plurality of attitudes on the solution under the plane is small, and only 1 calibration point needs to be marked and is marked as (4, 1).
And moving the end effector to a target point under a first plane in 5 different postures of the cooperative robot, ensuring that the amplitude of each posture is large, and recording joint information under the 5 postures through software setting points 1,2, 3, 4 and 5.
The end effector was moved to the target point in 9 different poses of the cooperative robot under the second plane, and joint information in 9 poses was recorded by software setting points 6, 7, 8, 9, 10, 11, 12, 13, 14.
The end effector is moved to the target point in 5 different poses of the cooperative robot under the third plane, and joint information in 5 poses is recorded by software set points 15, 16, 17, 18, 19.
Moving the end effector to a target point under the fourth plane in 1 posture of the cooperative robot, recording joint information under the current state through a software set point 20, and calculating;
step S3, recording the information of each joint aiming at the target point under the 20 different postures;
step S4, acquiring the pose of each joint state by using the positive kinematics of the robot; obtaining the coordinate of the end tool by using a least square method according to the pose of each joint state; the specific process is as follows:
formula for calculation
Figure 100002_DEST_PATH_IMAGE031
In the formula (I), the reaction is carried out,Fthe pose is the pose in the joint state,qin order to input the information on the joints,
Figure 972246DEST_PATH_IMAGE032
since a 6-joint robot is used as the initial origin angle value of the robot, the initial origin angle value is generally assumed to be [0,0,0,0]，RBased on the posture of the robot under the base coordinate system,Pbased on the position in the base coordinate system.
By means of a plurality of different robot attitude information, a least square method formula is utilized
Figure 196554DEST_PATH_IMAGE033
The end-tool coordinates are obtained, where,
Figure DEST_PATH_IMAGE034
in the form of end-of-line tool coordinates,
Figure 398997DEST_PATH_IMAGE035
is a set of 20 attitude differences,
Figure 683347DEST_PATH_IMAGE036
is a set of 20 position differences.
Step S5, obtaining the position of the target point under each posture through the tool coordinate; obtaining a virtual target point by combining the target point positions obtained under all postures with a mean value theory;
computing
Figure 971240DEST_PATH_IMAGE037
In the formula (I), wherein,
Figure 733660DEST_PATH_IMAGE038
is the corresponding target point position under 20 postures of the base coordinate system,jis 1,2, … 20.
The obtained 20 target points are summed and averaged:
Figure 915243DEST_PATH_IMAGE039
，sumthe sum formula is obtained, so that the virtual target point closest to 20 points is obtained, and is regarded as the target tip target point.
Step S6, taking the virtual target point as an initial value, performing theoretical calculation of a random method, obtaining a weight, and obtaining a new virtual target point after resampling by adopting resampling; the method specifically comprises the following steps:
step S61 is calculating a plurality of new virtual random target points,
Figure 245861DEST_PATH_IMAGE001
in the formula (I), wherein,
Figure 145684DEST_PATH_IMAGE040
as a new virtual random target point,iis 1,2, …num，
Figure 711794DEST_PATH_IMAGE003
As a virtual target point, the target point,randin the form of a random function,numis the number of random values;
step S62, difference value weighting is carried out:
Figure 623250DEST_PATH_IMAGE004
，
Figure 249403DEST_PATH_IMAGE005
as an initial weight value, the weight value,expin order to be an exponential function of the,jthe number of the channels being 1,2, … 20,
Figure 636522DEST_PATH_IMAGE022
is the corresponding target point position under the 1 st to 20 th postures under the base coordinate system; computing
Figure 740744DEST_PATH_IMAGE007
，
Figure 772285DEST_PATH_IMAGE008
Is the weight of the virtual target point;
step S63, normalization processing:
Figure 569340DEST_PATH_IMAGE041
，
Figure 178176DEST_PATH_IMAGE010
is a normalized weight value;
step S64, weight sorting:
Figure DEST_PATH_IMAGE042
whereinIFor the weight corresponding to the gesture sequence, usesortThe function sorts the weight sequence from small to large;
step S65, calculating the gesture sequence corresponding to the maximum weight
Figure 227035DEST_PATH_IMAGE012
SelectingI0.8 th in the sequencenumTonumA numerical value;
step S66 resampling calculation：
Figure 237716DEST_PATH_IMAGE043
Wherein, in the step (A),
Figure 81038DEST_PATH_IMAGE028
for the new virtual target point after resampling,
Figure 177170DEST_PATH_IMAGE044
for the summation of the new maximum random virtual target points,
Figure 623195DEST_PATH_IMAGE030
is composed of
Figure 629328DEST_PATH_IMAGE012
The number of virtual target point sequences in (c).
Step S7, establishing 20 calibration equations of different postures below the reference target point by taking the resampled new virtual target point as the reference target point; iteration is carried out by combining the Jacobian pseudo-inverse with a damping value;
calculating out
Figure 768185DEST_PATH_IMAGE045
In the formula (I), wherein,
Figure DEST_PATH_IMAGE046
the difference between the new virtual target point obtained by resampling and the target point position in each posture,Jthe matrix of the Jacobian is obtained,
Figure 351614DEST_PATH_IMAGE047
is the origin angle compensation value.
Therefore, the temperature of the molten metal is controlled,
Figure 476696DEST_PATH_IMAGE048
in the formula (I), wherein,
Figure 461969DEST_PATH_IMAGE049
is a pseudo-inverse matrix of Jacobian.
The pseudo-inverse matrix of Jacobian is easy to generate singular valuesTherefore, an improved jacobian pseudo-inverse is needed to iterate in conjunction with the damping values:
Figure DEST_PATH_IMAGE050
in the formula (I), wherein,kthe value is usually 0.001. Because the traditional Jacobian generally needs to use a pseudo-inverse matrix
Figure 647094DEST_PATH_IMAGE051
Combining the damping value, svd decomposition is used to obtain singular value, and the damping value is judged in real timekAnd the Jacobian inverse matrix improved by the method is selected due to the damping value, the calculation is complex and time-consuming, and the real-time approximation of the Jacobian inverse matrix cannot be realizedkA small value can be taken to approximate the inverse jacobian matrix in real time.
Thus, it is possible to prevent the occurrence of,
Figure 717818DEST_PATH_IMAGE047
can be changed into:
Figure DEST_PATH_IMAGE052
。
step S8, calculating the matching degree of the joint angle under each posture and the target point joint angle, and finishing calibration if the matching degree is more than or equal to a set threshold value; if the matching degree is less than the set threshold, repeating steps S4-S7 until the matching degree is greater than or equal to the set threshold. The specific process is as follows:
will be provided with
Figure 646591DEST_PATH_IMAGE047
Compensating to the 20 sets of joint angles of step S4 to obtain a new initial origin angle value
Figure 486371DEST_PATH_IMAGE032
. And repeating the steps S4-S7 until the matching degree reaches a threshold value, indicating that the target points corresponding to all the postures are the same target point, and meeting the jumping-out condition, thereby obtaining an accurate origin position angle value and obtaining a final origin position compensation angle.
If the calibration is not ideal, the calibration method can be implemented automatically by storing the 6 joint positions of 20 robots at the first calibration according to the result of the first 20-point calibration, performing manual fine adjustment and correction, and then automatically performing the fine calibration again by using the stored 6 joint positions of 20 robots.
By the technical scheme, the robot origin position calibration method has the following advantages: (1) compared with the traditional calibration tool, the 20-point calibration tool utilizing the 4 planes is more stable, the anti-interference performance is stronger, and when a certain deviation exists in the position of the origin, the coordinates of the calibration tool are more accurate, so that the target point is more favorably found. (2) Reducing the iteration times by combining a resampling method and searching a target point more quickly; (3) the improved Jacobian inverse solution method can more effectively approach a true value without reselecting the configuration of the mechanical arm, so that the stability of the algorithm is improved; (4) and in case of non-ideal conditions, fine calibration can be performed again after manual fine adjustment is performed according to the first result.
According to an embodiment of the present invention, there is also provided an origin position calibration apparatus corresponding to a robot, as shown in fig. 3, the origin position calibration apparatus includes the following modules:
a configuration module for configuring the end effector tool of the robot, establishing a target point tool and fixing; as shown in fig. 1, the left drawing is the fixed target point, and the right drawing is the robot end effector located on the robot arm.
A pose module moving the end effector to the target point at 20 different poses of the robot on 4 planes of the target point, respectively; the process of setting the plane is specifically shown in fig. 1 and 2, and the specific process is as follows:
the plane distribution method can select a comfortable plane according to the configuration of the robot, and the plane has calibration points with larger configuration difference, so that the required main features can be extracted more stably. As shown in fig. 1, there are planes 1 intersecting in 45 degrees, respectively, where the attitude motion of the planar robot is affected to some extent, and 5 fixed marks are marked as (1, 5); parallel to the tip plane 2, because the robot has wide attitude motion space, 9 calibration points are marked fixedly and are marked as (2, 9); intersecting the 45-degree direction plane 3, the plane is marked with 5, and is marked as (3, 5); and (4) because the motion attitude of the plane robot is only related to six joints, the influence of a plurality of attitudes on the solution under the plane is small, and only 1 calibration point needs to be marked and is marked as (4, 1).
And moving the end effector to a target point under a first plane in 5 different postures of the cooperative robot, ensuring that the amplitude of each posture is large, and recording joint information under the 5 postures through software setting points 1,2, 3, 4 and 5.
The end effector was moved to the target point in 9 different poses of the cooperative robot in the second plane and the joint information in 9 poses was recorded by software set points 6, 7, 8, 9, 10, 11, 12, 13, 14.
The end effector is moved to the target point in 5 different poses of the cooperative robot under the third plane, and joint information in 5 poses is recorded by software set points 15, 16, 17, 18, 19.
Moving the end effector to a target point under the fourth plane in 1 posture of the cooperative robot, recording joint information under the current state through a software set point 20, and calculating;
the alignment module is used for recording the information of each joint aligned to the target point under the 20 different postures;
the coordinate module is used for acquiring the pose of each joint state by using the positive kinematics of the robot; obtaining the coordinate of the end tool by using a least square method according to the pose of each joint state; the specific process is as follows:
formula for calculation
Figure 967031DEST_PATH_IMAGE031
In the formula (I), wherein,Fthe pose is the pose in the joint state,qin order to input the information on the joints,
Figure 111401DEST_PATH_IMAGE032
since a 6-joint robot is used as the initial origin angle value of the robot, the initial origin angle value is generally assumed to be [0,0,0,0]，RBased on the posture of the robot under the base coordinate system,Pbased on a base coordinate systemLocation.
By means of a plurality of different robot attitude information, a least square method formula is utilized
Figure 968498DEST_PATH_IMAGE033
The end-tool coordinates are obtained, where,
Figure 662785DEST_PATH_IMAGE053
in the form of end-of-line tool coordinates,
Figure 189712DEST_PATH_IMAGE035
is a set of 20 attitude differences,
Figure 235029DEST_PATH_IMAGE036
is a set of 20 position differences.
The virtual target point module is used for obtaining the position of a target point under each posture through the tool coordinates; obtaining a virtual target point by combining the target point positions obtained under all postures with a mean value theory;
computing
Figure DEST_PATH_IMAGE054
In the formula (I), the reaction is carried out,
Figure 505604DEST_PATH_IMAGE038
for the corresponding target point positions in 20 poses of the base coordinate system,j1,2, … 20.
The obtained 20 target points are summed and averaged:
Figure 54397DEST_PATH_IMAGE055
，sumthe sum formula is obtained, so that the virtual target point closest to 20 points is obtained, and is regarded as the target tip target point.
The resampling module is used for carrying out theoretical calculation of a random method by taking the virtual target point as an initial value, obtaining a weight and obtaining a new virtual target point after resampling by adopting resampling; the method specifically comprises the following steps:
a new random target point module calculates a plurality of new virtual random target points,
Figure 142439DEST_PATH_IMAGE017
in the formula (I), wherein,
Figure 409472DEST_PATH_IMAGE018
as a new virtual random target point,iis 1,2, …num，
Figure 218159DEST_PATH_IMAGE019
As a virtual target point, the target point,randin the form of a random function,numis the number of random values;
the right solving module performs difference right solving:
Figure DEST_PATH_IMAGE056
，
Figure 887038DEST_PATH_IMAGE021
as an initial weight value, the weight value,expin order to be an exponential function of the,jthe number of the channels being 1,2, … 20,
Figure 21347DEST_PATH_IMAGE022
is the corresponding target point position under the 1 st to 20 th postures under the base coordinate system; computing
Figure 510097DEST_PATH_IMAGE023
，
Figure 512688DEST_PATH_IMAGE024
Is the weight of the virtual target point;
normalization module for normalization:
Figure 911440DEST_PATH_IMAGE057
，
Figure 75705DEST_PATH_IMAGE010
is a normalized weight value;
a sorting module, wherein the weight sorting comprises the following steps:
Figure 317330DEST_PATH_IMAGE011
whereinIFor the weight corresponding to the gesture sequence, usesortFunction pair weight sequenceSorting from small to large;
attitude sequence module for calculating maximum weight value corresponding attitude sequence
Figure 733399DEST_PATH_IMAGE012
SelectingI0.8 th in the sequencenumTonumA numerical value;
a virtual target point resampling module:
Figure DEST_PATH_IMAGE058
wherein, in the step (A),
Figure 111291DEST_PATH_IMAGE028
for the new virtual target point after resampling,
Figure 587403DEST_PATH_IMAGE059
for the summation of the new maximum random virtual target points,
Figure DEST_PATH_IMAGE060
is composed of
Figure 50745DEST_PATH_IMAGE012
The number of virtual target point sequences in (c).
The iteration module is used for establishing 20 calibration equations with different postures below a reference target point by taking the resampled new virtual target point as the reference target point; iteration is carried out by combining the Jacobian pseudo-inverse with a damping value;
computing
Figure 270505DEST_PATH_IMAGE061
In the formula (I), wherein,
Figure DEST_PATH_IMAGE062
the difference between the new virtual target point obtained by resampling and the target point position in each posture,Jis a matrix of the Jacobian and the Jacobian,
Figure 768483DEST_PATH_IMAGE047
is the origin angle compensation value.
Therefore, the temperature of the molten metal is controlled,
Figure 274550DEST_PATH_IMAGE048
in the formula (I), wherein,
Figure 366134DEST_PATH_IMAGE049
is a pseudo-inverse matrix of Jacobian.
Since the pseudo-inverse matrix of Jacobian easily generates singular values, improved Jacobian pseudo-inverses are needed to iterate in combination with the damping values:
Figure 248640DEST_PATH_IMAGE063
in the formula (I), the reaction is carried out,kthe value is usually 0.001. Because the traditional Jacobian generally needs to use a pseudo-inverse matrix
Figure 601124DEST_PATH_IMAGE051
In combination with the damping value, svd decomposition is used to obtain singular value, and the damping value is judged in real timekAnd the Jacobian inverse matrix improved by the method is selected due to the damping value, the calculation is complex and time-consuming, and the real-time approximation of the Jacobian inverse matrix cannot be realizedkA small value can be taken to approximate the inverse jacobian matrix in real time.
Thus, it is possible to prevent the occurrence of,
Figure DEST_PATH_IMAGE064
can be changed into:
Figure 153459DEST_PATH_IMAGE065
。
the matching module is used for calculating the matching degree of the joint angle under each posture and the target point joint angle, and finishing calibration if the matching degree is greater than or equal to a set threshold value; if the matching degree is smaller than the set threshold, repeating the coordinate module to the iteration module until the matching degree is larger than or equal to the set threshold. The specific process is as follows:
will be provided with
Figure DEST_PATH_IMAGE066
Compensating to 20 groups of joint angles of the coordinate module to obtain a new initial origin angle value
Figure 466760DEST_PATH_IMAGE032
. And repeating the coordinate module to the iteration module until the matching degree reaches a threshold value, wherein the target points corresponding to all the postures are the same target point, and the jumping-out condition is met, so that an accurate original point position angle value is obtained, and a final original point position compensation angle is obtained.
If the calibration is not ideal, the calibration method can be implemented automatically by storing the 6 joint positions of 20 robots at the first calibration according to the result of the first 20-point calibration, performing manual fine adjustment and correction, and then automatically performing the fine calibration again by using the stored 6 joint positions of 20 robots.
Through the technical scheme, the robot origin position calibration device has the following advantages: (1) compared with the traditional calibration tool, the 20-point calibration tool utilizing the 4 planes is more stable, the anti-interference performance is stronger, and when a certain deviation exists in the position of the origin, the coordinates of the calibration tool are more accurate, so that the target point is more favorably found. (2) Reducing the iteration times by combining a resampling method and searching a target point more quickly; (3) the improved Jacobian inverse solution method can more effectively approach a true value without reselecting the configuration of the mechanical arm, so that the stability of the algorithm is improved; (4) and in case of non-ideal conditions, fine calibration can be performed again after manual fine adjustment is performed according to the first result.
According to an embodiment of the present invention, there is also provided an electronic device corresponding to a robot origin position calibration method, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the method steps of the origin position calibration method when executing the computer program.
According to an embodiment of the present invention, there is also provided a computer-readable storage medium corresponding to the method for calibrating an origin position of a robot, the computer-readable storage medium storing a computer program which, when executed by a processor, implements the method steps of the above-described method for calibrating an origin position.
An embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and the computer program is executed by a server to perform the method steps of the above-mentioned origin position calibration method.
In the embodiments provided in the present invention, it should be understood that the disclosed system and method can be implemented in other ways. The above-described system embodiments are merely illustrative, and for example, the division of the system apparatus into only one logical functional division may be implemented in other ways, and for example, a plurality of apparatuses or components may be combined or integrated into another system, or some features may be omitted, or not implemented.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The above description is only an example of the present invention, and is not intended to limit the scope of the present invention, and various modifications and changes may be made to the present invention by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A method for calibrating the position of an origin of a robot is characterized by comprising the following steps:
step S1, configuring the end effector tool of the robot and fixing the target point tool;
step S2, moving the end effector to the target point on 4 planes of the target point respectively in 20 different postures of the robot;
step S3, recording the information of each joint aiming at the target point under the 20 different postures;
step S4, acquiring the pose of each joint state by using the positive kinematics of the robot; obtaining the coordinate of the end tool by using a least square method according to the pose of each joint state;
step S5, obtaining the position of the target point under each posture through the tool coordinate; obtaining a virtual target point by combining the target point positions obtained under all postures with a mean value theory;
step S6, taking the virtual target point as an initial value, performing theoretical calculation of a random method, obtaining a weight, and obtaining a new virtual target point after resampling by adopting resampling;
step S7, establishing 20 calibration equations of different postures below the reference target point by taking the resampled new virtual target point as the reference target point; performing joint angle iteration by combining Jacobian pseudo-inverse with a damping value;
step S8, calculating the matching degree of the joint angle under each posture and the target point joint angle, and finishing calibration if the matching degree is larger than or equal to a set threshold value; if the matching degree is less than the set threshold, repeating steps S4-S7 until the matching degree is greater than or equal to the set threshold.
2. The method for calibrating the origin position of the robot according to claim 1, wherein in the step S6, the resampling method specifically comprises:
step S61-calculating a plurality of new virtual random target points,
Figure DEST_PATH_IMAGE001
in the formula (I), the reaction is carried out,
Figure 952522DEST_PATH_IMAGE002
as a new virtual random target point,iis 1,2, …num，
Figure DEST_PATH_IMAGE003
As a virtual target point, the target point,randin the form of a random function,numis the number of random values;
step S62, difference value weighting is carried out:
Figure 693076DEST_PATH_IMAGE004
，
Figure DEST_PATH_IMAGE005
as an initial weight value, the weight value,expin order to be an exponential function of the,jthe number of the channels being 1,2, … 20,
Figure 730737DEST_PATH_IMAGE006
is the corresponding target point position under the 1 st to 20 th postures under the base coordinate system; computing
Figure DEST_PATH_IMAGE007
，
Figure 314165DEST_PATH_IMAGE008
Is the weight of the virtual target point;
step S63, normalization processing:
Figure DEST_PATH_IMAGE009
，
Figure 439247DEST_PATH_IMAGE010
is a normalized weight value;
step S64, weight sorting:
Figure DEST_PATH_IMAGE011
whereinIFor the weight corresponding to the gesture sequence, usesortThe function sorts the weight sequence from small to large;
step S65, calculating the gesture sequence corresponding to the maximum weight
Figure 299886DEST_PATH_IMAGE012
SelectingI0.8 th in the sequencenumTonumA numerical value;
step S66, resampling calculation:
Figure DEST_PATH_IMAGE013
wherein, in the step (A),
Figure 609645DEST_PATH_IMAGE014
for the new virtual target point after resampling,
Figure DEST_PATH_IMAGE015
for the summation of the new maximum random virtual target points,
Figure 555735DEST_PATH_IMAGE016
is composed of
Figure 609142DEST_PATH_IMAGE012
The number of virtual target point sequences in (c).
3. The method for calibrating an origin position of a robot according to claim 1, wherein in step S7, the jacobian pseudo-inverse is
Figure DEST_PATH_IMAGE017
Wherein
Figure 324288DEST_PATH_IMAGE018
Is the pseudo-inverse of Jacobian,Jthe matrix of the Jacobian is obtained,
Figure DEST_PATH_IMAGE019
is the transpose of the Jacobian matrix,kis a positive number.
4. The method for calibrating the origin position of the robot according to claim 1, wherein if the calibration is not qualified, the positions of 6 joints of 20 robots in the first calibration are saved according to the result of the first 20-point calibration, and after the manual fine adjustment correction, the saved positions of 6 joints of 20 robots are automatically used for the first fine calibration.
5. A robot origin position calibration device is characterized by comprising the following modules:
the configuration module is used for configuring an end effector tool of the robot and fixing a target point tool;
a pose module moving the end effector to the target point at 20 different poses of the robot on 4 planes of the target point, respectively;
the alignment module is used for recording the information of each joint aligned to the target point under the 20 different postures;
the coordinate module is used for acquiring the pose of each joint state by using the positive kinematics of the robot; obtaining the coordinate of the end tool by using a least square method according to the pose of each joint state;
the virtual target point module is used for obtaining the position of a target point under each posture through the tool coordinates; obtaining a virtual target point by combining the target point positions obtained under all postures with a mean value theory;
the resampling module is used for carrying out theoretical calculation of a random method by taking the virtual target point as an initial value, obtaining a weight and obtaining a new virtual target point after resampling by adopting resampling;
the iteration module is used for establishing 20 calibration equations with different postures below a reference target point by taking the resampled new virtual target point as the reference target point; performing joint angle iteration by combining Jacobian pseudo-inverse with a damping value;
the matching module is used for calculating the matching degree of the joint angle under each posture and the target point joint angle, and finishing calibration if the matching degree is greater than or equal to a set threshold value; if the matching degree is smaller than the set threshold, repeating the coordinate module to the iteration module until the matching degree is larger than or equal to the set threshold.
6. The robot origin position calibration device according to claim 5, wherein the resampling module is specifically:
a new random target point module calculates a plurality of new virtual random target points,
Figure 680314DEST_PATH_IMAGE020
in the formula (I), wherein,
Figure DEST_PATH_IMAGE021
as a new virtual random target point,iis 1,2, …num，
Figure 972755DEST_PATH_IMAGE022
As a virtual target point, the target point,randin the form of a random function,numis the number of random values;
the right solving module is used for solving the right of the difference value:
Figure DEST_PATH_IMAGE023
，
Figure 705219DEST_PATH_IMAGE005
as an initial weight value, the weight value,expin order to be an exponential function of the,jthe number of the channels being 1,2, … 20,
Figure 399506DEST_PATH_IMAGE024
is the corresponding target point position under the 1 st to 20 th postures under the base coordinate system; computing
Figure DEST_PATH_IMAGE025
，
Figure 926433DEST_PATH_IMAGE026
Is the weight of the virtual target point;
normalization module for normalization:
Figure DEST_PATH_IMAGE027
，
Figure 847116DEST_PATH_IMAGE010
is a normalized weight value;
a sorting module, wherein the weight sorting comprises the following steps:
Figure 242325DEST_PATH_IMAGE028
whereinIFor the weight corresponding to the gesture sequence, usesortThe function sorts the weight sequence from small to large;
attitude sequence module for calculating maximum weight value corresponding attitude sequence
Figure 791118DEST_PATH_IMAGE012
SelectingI0.8 th in the sequencenumTonumA numerical value;
a virtual target point resampling module:
Figure DEST_PATH_IMAGE029
wherein, in the step (A),
Figure 754526DEST_PATH_IMAGE014
for the new virtual target point after resampling,
Figure 21559DEST_PATH_IMAGE030
for the summation of the new maximum random virtual target points,
Figure DEST_PATH_IMAGE031
is composed of
Figure 95826DEST_PATH_IMAGE012
The number of virtual target point sequences in (c).
7. The robot origin position calibration device according to claim 5, wherein in the iteration module, Jacobian pseudo-inverse is
Figure 499125DEST_PATH_IMAGE017
Wherein
Figure 758068DEST_PATH_IMAGE018
Is the pseudo-inverse of Jacobian,Jthe matrix of the Jacobian is obtained,
Figure 263130DEST_PATH_IMAGE019
is the transpose of the Jacobian matrix,kis a positive number.
8. The robot origin position calibration device according to claim 5, wherein if the calibration is not qualified, the positions of 6 joints of 20 robots in the first calibration are simultaneously saved according to the result of the first 20-point calibration, and after the manual fine adjustment correction, the saved positions of 6 joints of 20 robots are automatically used for the first fine calibration.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for calibrating the location of the origin of the robot according to any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, implements the method for calibrating an origin position of a robot according to any one of claims 1 to 4.
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