IP Library › Granted Patent US 11,642,789
Granted Patent B2
US 11,642,789 · App. 17/772,074 · Granted May 9, 2023

Automatic calibration method and device for robot vision system

Inventors: Jong Hoon Park (Namyangju-si, KR); Jun Su Kang (Gangjin-gun, KR)
Assignee: NEUROMEKA
B25J9/1692B25J9/1648B25J9/1653B25J9/1664B25J9/1697
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Quick Facts
Patent No.
US 11,642,789
App. No.
17/772,074
Granted
May 9, 2023
Kind
B2
Abstract

According to one aspect of the present invention, disclosed is an automatic calibration method for a calibration device connected to a camera that is disposed the end effector of a robot and to a robot controller for controlling the robot. The method comprises the steps of: acquiring, from the camera and the robot controller, a robot-based coordinate system and an image of a marker marked in the work area of the robot (wherein the acquired image and robot-based coordinate system are recorded while the end effector is moved to a plurality of sample coordinates); and estimating the position of a robot coordinate system-based marker by using the acquired image and robot-based coordinate system.

Claims (195)

1. An automatic calibration method of a calibration device connected to a camera provided in an end effector of a robot and to a robot controller controlling the robot, the method comprising:

obtaining an image of a marker marked in a work area of the robot from the camera and a robot-based coordinate system from the robot controller, the obtained image and robot-based coordinate system being recorded while moving the end effector to a plurality of sample coordinates; and

estimating a position of the robot coordinate system-based marker based on the obtained image and robot-based coordinate system.

2. The automatic calibration method of claim 1 , wherein the estimating the position of the robot coordinate system-based marker based on the obtained image and robot-based coordinate system comprises

estimating a camera-robot offset T off by setting the camera-robot offset T off as an assumed value and minimizing a coordinate error rate in converting a camera-based marker position T mj in a plurality of postures into the robot-based coordinate system by a nonlinear optimization algorithm.

3. The automatic calibration method of claim 2 , wherein the camera-robot offset T off is calculated based on a function of variables comprising x-y-z coordinates and z-y-x Euler angles.

4. The automatic calibration method of claim 3 , wherein the camera-robot offset T off represents a degree of deviation between a first sample coordinate and a second sample coordinate by an error of a rotation matrix R and an error of a position P, and is estimated by the nonlinear optimization algorithm that minimizes the error of a rotation matrix R and the error of a position P.

5. The automatic calibration method of claim 4 , wherein

the camera-robot offset T off is calculated by

arg

⁢

min

T

off

⁢

∑

j

log

⁡

(

Δ

⁢

R

j

)

F

+

γ

⁢

Δ

⁢

P

j

⁢

[

Δ

⁢

R

j

Δ

⁢

P

j

0

1

]

=

(

T

j

-

1

⁢

T

off

⁢

T

m

,

j

-

1

)

-

1

⁢

T

j

⁢

T

off

⁢

T

m

,

j

where, j is an index of sample coordinates, T j is a transformation matrix of the sample coordinates j, T mj is the camera-based marker position in the sample coordinate j, R is a variable related to the rotation matrix, and P is a variable related to the error of the position.

6. The automatic calibration method of claim 1 , wherein

the end effector is provided with a taught movable area, and

the movable area is defined by a plurality of reference points as a reference area for calibration.

7. The automatic calibration method of claim 6 , wherein the plurality of sample coordinates is disposed in an area surrounded by the plurality of reference points.

8. The automatic calibration method of claim 1 , wherein the plurality of sample coordinates is extracted by:

recording a transformation matrix from the robot-based coordinate system to a robot distal end in a plurality of reference points;

recording variables, which determines positions of the plurality of reference coordinates based on the set operation center point, and extracting a minimum value and a maximum value of the variables; and

extracting the plurality of sample coordinates through randomly weighted sums between the plurality of reference points.

9. The automatic calibration method of claim 8 , wherein the variables comprise at least two of an azimuth angle, an elevation angle, a roll and a distance from the operation center point.

10. The automatic calibration method of claim 8 , wherein the transformation matrix T j of each sample coordinate system is calculated from the variables of the plurality of extracted sample coordinates.

11. An automatic calibration method of a calibration device connected to a camera positioned as separated from a robot and to a robot controller controlling the robot, the method comprising:

obtaining an image of a marker provided in an end effector of the robot from the camera and a robot-based coordinate system from the robot controller, the obtained image and robot-based coordinate system being recorded while moving the end effector to a plurality of sample coordinates; and

estimating a robot base-based camera position from the obtained image.

12. The automatic calibration method of claim 11 , wherein the estimating the robot base-based camera position based on the obtained image and robot-based coordinate system comprises

estimating a robot end-marker offset T em by setting the robot end-marker offset T em as an assumed value and minimizing a coordinate error rate in converting a robot base-based camera position in a plurality of postures into the robot-based coordinate system by a nonlinear optimization algorithm.

13. The automatic calibration method of claim 12 , wherein the robot end-marker offset T em is calculated based on a function of variables comprising x-y-z coordinates and z-y-x Euler angles.

14. The automatic calibration method of claim 13 , wherein the robot end-marker offset T em represents a degree of deviation between a first sample coordinate and a second sample coordinate by an error of a rotation matrix and an error of a position, and is estimated by the nonlinear optimization algorithm that minimizes the error of a rotation matrix and the error of a position.

15. The automatic calibration method of claim 14 , wherein the robot end-marker offset T em is calculated by

T

ftx

=

T

1

(

arg

⁢

min

T

em

⁢

∑

j

log

⁡

(

Δ

⁢

R

j

)

F

+

γ

⁢

Δ

⁢

P

j

)

⁢

T

m

,

1

-

1

⁢

[

Δ

⁢

R

j

Δ

⁢

P

j

0

1

]

=

(

T

j

-

1

⁢

T

em

⁢

T

m

,

j

-

1

-

1

)

-

1

⁢

T

j

⁢

T

em

⁢

T

m

,

j

-

1

where, j is an index of sample coordinates, T j is a transformation matrix of the sample coordinates j, T em is an offset estimation value from a distal end of the robot to the marker, T mj is a position of the marker from the camera, R is a variable related to the rotation matrix, and P is a variable related to the error of the position.

16. A calibration device connected to a camera provided in an end effector of a robot and to a robot controller controlling the robot, the calibration device comprising:

an input unit configured to obtain an image of a marker marked in a work area of the robot from the camera and a robot-based coordinate system from the robot controller, the obtained image and robot-based coordinate system being recorded while moving the end effector to a plurality of sample coordinates; and

a processor configured to estimate a camera-based marker position based on the obtained image and robot-based coordinate system.

17. A calibration device connected to a camera positioned as separated from a robot and to a robot controller controlling the robot, the calibration device comprising:

an input unit configured to obtain an image of a marker from the camera and a robot-based coordinate system from the robot controller, the obtained image and robot-based coordinate system being recorded while moving an end effector to a plurality of sample coordinates; and

a processor configured to estimate a robot base-based camera position based on the obtained image.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 27, 2022
From: PARK, JONG HOON; KANG, JUN SU
To: NEUROMEKA
Reel/Frame 059806/0861 →
Priority Claims (2)
KR 10-2019-0138460 · Nov 1, 2019 · national
KR 10-2020-0055963 · Nov 1, 2019 · national
Continuity (1)
Related Publication 20220379483A1 · Dec 1, 2022
Cited By (1)
US 12,390,932