CN111882614A - KNN-ICP algorithm-based free-form surface positioning method - Google Patents

KNN-ICP algorithm-based free-form surface positioning method Download PDF

Info

Publication number
CN111882614A
CN111882614A CN202010748896.7A CN202010748896A CN111882614A CN 111882614 A CN111882614 A CN 111882614A CN 202010748896 A CN202010748896 A CN 202010748896A CN 111882614 A CN111882614 A CN 111882614A
Authority
CN
China
Prior art keywords
point cloud
matrix
knn
robot
form surface
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202010748896.7A
Other languages
Chinese (zh)
Inventor
闵康
戴振东
段晋军
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing Lihang Bionic Industry Research Institute Co ltd
Original Assignee
Nanjing Lihang Bionic Industry Research Institute Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing Lihang Bionic Industry Research Institute Co ltd filed Critical Nanjing Lihang Bionic Industry Research Institute Co ltd
Priority to CN202010748896.7A priority Critical patent/CN111882614A/en
Priority to PCT/CN2020/108784 priority patent/WO2022021479A1/en
Publication of CN111882614A publication Critical patent/CN111882614A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10028—Range image; Depth image; 3D point clouds

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Pure & Applied Mathematics (AREA)
  • Software Systems (AREA)
  • Evolutionary Computation (AREA)
  • Mathematical Optimization (AREA)
  • Mathematical Analysis (AREA)
  • Computing Systems (AREA)
  • Computational Mathematics (AREA)
  • Biophysics (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Computational Linguistics (AREA)
  • Molecular Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Algebra (AREA)
  • Databases & Information Systems (AREA)
  • Computer Hardware Design (AREA)
  • Geometry (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Numerical Control (AREA)

Abstract

The invention discloses a KNN-ICP algorithm-based free-form surface positioning method, which comprises the following steps: (1) using a robot to perform point collection on the curved surface to obtain actual measurement point cloud data; (2) dispersing the CAD model of the curved surface according to the machining tolerance to generate theoretical point cloud data; (3) calculating the closest point and the minimum distance of each data point in the actual measurement point cloud in the theoretical point cloud by using a KNN algorithm; judging whether an iteration termination condition is met, if so, turning to the step (5), and if not, turning to the step (4); (4) calculating a rotation matrix and a translation matrix by using a quaternion method to enable the current measurement point set to approach the nearest point set, obtaining the position of new measurement point cloud data, and returning to the step (3); (5) the iteration is terminated and the object coordinate system of the object relative to the robot is output. The free-form surface positioning method provided by the invention searches the closest point through the KNN algorithm, is simple to realize and has high calculation efficiency.

Description

KNN-ICP algorithm-based free-form surface positioning method
Technical Field
The invention belongs to the technical field of computer-aided manufacturing and robot machining, and particularly relates to a KNN-ICP algorithm-based free-form surface positioning method.
Background
Off-line programmed robot polishing is the most suitable robot polishing mode at present. The specific idea is that the working conditions around the robot are simulated on a computer through software, in a virtual three-dimensional working condition, the motion trail of the robot is directly generated in the computer according to the operation of an operator on the basis of the matching of the materials, the size and the shape of a machined part, a corresponding program is generated, and the program is led into a robot system to operate.
During robot grinding, the positioning of the workpiece coordinate system (establishing the coordinate system of the workpiece relative to the robot) is very important. The workpiece self-positioning means that a computer and a sensor measuring system are utilized, and accurate positioning of the workpiece under actual working conditions is realized through a pose solving algorithm, and the essence of the workpiece self-positioning means that accurate registration of three-dimensional point cloud is realized. In recent years, researchers at home and abroad develop systematic research around the three-dimensional Point cloud matching problem, the most representative of which is the iterative closest Point algorithm (ICP) proposed by Besl and McKay, and the algorithm is simple to implement, high in precision and low in calculation efficiency.
Disclosure of Invention
Aiming at the defects or improvement requirements of the prior art, in order to more efficiently and accurately establish a workpiece coordinate system of a workpiece relative to a robot, the invention adopts a robot system to obtain actual measurement point cloud data of the whole curved surface and provides a KNN-ICP algorithm-based free-form surface positioning method; the method is simple to implement and high in calculation efficiency, and the workpiece coordinate system can be accurately calculated.
The invention is realized by the following technical scheme:
a KNN-ICP algorithm-based free-form surface positioning method is characterized by comprising the following steps:
(1) using a robot to perform point collection on the curved surface to obtain actual measurement point cloud data
Figure DEST_PATH_IMAGE001
；
(2) CAD of curved surfaces based on machining tolerancesThe model is dispersed to generate theoretical target point cloud data
Figure 25613DEST_PATH_IMAGE002
；
(3) Calculating actual measurement point cloud by using KNN algorithm
Figure DEST_PATH_IMAGE003
Each data point in
Figure 851487DEST_PATH_IMAGE002
Closest point of (3)
Figure 403560DEST_PATH_IMAGE004
And corresponding shortest distance
Figure DEST_PATH_IMAGE005
Wherein
Figure 213253DEST_PATH_IMAGE006
Indicating number of iterations, initial case
Figure DEST_PATH_IMAGE007
,
Figure 439966DEST_PATH_IMAGE008
(ii) a Is provided with
Figure DEST_PATH_IMAGE009
For a given iteration end precision, if
Figure 449508DEST_PATH_IMAGE010
If yes, turning to the step (5), otherwise, turning to the step (4);
(4) calculating a rotation matrix by using a quaternion method
Figure DEST_PATH_IMAGE011
And translation matrix
Figure 736264DEST_PATH_IMAGE012
Obtaining new measuring point cloud position
Figure DEST_PATH_IMAGE013
=
Figure 16942DEST_PATH_IMAGE014
，
Figure DEST_PATH_IMAGE015
So that the current measurement point cloud
Figure 781767DEST_PATH_IMAGE016
Approximation
Figure 51074DEST_PATH_IMAGE004
And returning to the step (3);
(5) the iteration is terminated and the object coordinate system of the object relative to the robot is output.
In step (1), the robot collects points of the free-form surface, and the number of point cloud data obtained is further optimized
Figure DEST_PATH_IMAGE017
And is and
Figure 741687DEST_PATH_IMAGE018
。
in the further optimization of the technical scheme of the invention, a quaternion method is adopted in the step (4) to calculate the rotation matrix
Figure 463656DEST_PATH_IMAGE011
And translation matrix
Figure 484701DEST_PATH_IMAGE012
Suppose that the actual measurement point set and the theoretical target point set are respectively
Figure DEST_PATH_IMAGE019
And
Figure 155985DEST_PATH_IMAGE020
the rotation matrix is
Figure 486341DEST_PATH_IMAGE011
The translation matrix is
Figure 695606DEST_PATH_IMAGE012
The specific calculation steps are as follows:
(4.1) computing the Point set
Figure 254763DEST_PATH_IMAGE019
And
Figure 249395DEST_PATH_IMAGE020
of (2) center
Figure DEST_PATH_IMAGE021
And
Figure 553249DEST_PATH_IMAGE022
and performing centralization treatment:
Figure DEST_PATH_IMAGE023
,
Figure 46547DEST_PATH_IMAGE024
,
Figure DEST_PATH_IMAGE025
，
(4.2) calculating a covariance matrix according to the centralized data point set
Figure 222445DEST_PATH_IMAGE026
And constructing a positive definite matrix through the covariance matrix
Figure DEST_PATH_IMAGE027
：
Figure 412862DEST_PATH_IMAGE028
Figure DEST_PATH_IMAGE029
(4.3) calculating the eigenvalue of the positive definite matrix N, wherein the eigenvector corresponding to the maximum eigenvalue corresponds to the rotation quaternion as follows:
Figure 694807DEST_PATH_IMAGE030
(4.4) rotational quaternion
Figure DEST_PATH_IMAGE031
The rotation matrix R is represented as:
Figure 222872DEST_PATH_IMAGE032
(4.5) according to
Figure DEST_PATH_IMAGE033
Computing a translation matrix
Figure 960715DEST_PATH_IMAGE034
。
Compared with the prior art, the invention has the following beneficial effects: the free-form surface positioning method searches the closest point through a KNN algorithm, is simple to implement and has high calculation efficiency.
Drawings
Fig. 1 is a general flowchart of a KNN-ICP algorithm based free-form surface localization method.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to fig. 1 and an embodiment. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
As shown in fig. 1, a method for locating a free-form surface based on a KNN-ICP algorithm includes the following steps:
(1) using a robot to align curved surfacesSampling points to obtain actual measurement point cloud data
Figure 913628DEST_PATH_IMAGE001
；
(2) Dispersing the CAD model of the curved surface according to the processing tolerance to generate theoretical target point cloud data
Figure 258152DEST_PATH_IMAGE002
；
(3) Calculating actual measurement point cloud by using KNN algorithm
Figure 460464DEST_PATH_IMAGE003
Each data point in
Figure 679961DEST_PATH_IMAGE002
Closest point of (3)
Figure 752959DEST_PATH_IMAGE004
And corresponding shortest distance
Figure 268386DEST_PATH_IMAGE005
Wherein
Figure 161255DEST_PATH_IMAGE006
Indicating number of iterations, initial case
Figure 404018DEST_PATH_IMAGE007
,
Figure 580790DEST_PATH_IMAGE008
(ii) a Is provided with
Figure 516385DEST_PATH_IMAGE009
For a given iteration end precision, if
Figure 630971DEST_PATH_IMAGE010
If yes, turning to the step (5), otherwise, turning to the step (4);
(4) calculating a rotation matrix by using a quaternion method
Figure 428157DEST_PATH_IMAGE011
And translation matrix
Figure 210169DEST_PATH_IMAGE012
Obtaining new measuring point cloud position
Figure 565932DEST_PATH_IMAGE013
=
Figure 433394DEST_PATH_IMAGE014
，
Figure 283539DEST_PATH_IMAGE015
So that the current measurement point cloud
Figure 670789DEST_PATH_IMAGE016
Approximation
Figure 948186DEST_PATH_IMAGE004
And returning to the step (3);
(5) the iteration is terminated and the object coordinate system of the object relative to the robot is output.
In this embodiment, in step (1), the robot performs point acquisition on the free-form surface, and the number of point cloud data obtained is
Figure 771786DEST_PATH_IMAGE017
And is and
Figure 946327DEST_PATH_IMAGE018
。
in this embodiment, in step (4), a quaternion method is used to calculate the rotation matrix
Figure 171772DEST_PATH_IMAGE011
And translation matrix
Figure 636383DEST_PATH_IMAGE012
Suppose that the actual measurement point set and the theoretical target point set are respectively
Figure 478437DEST_PATH_IMAGE019
And
Figure 670384DEST_PATH_IMAGE020
the rotation matrix is
Figure 265182DEST_PATH_IMAGE011
The translation matrix is
Figure 149961DEST_PATH_IMAGE012
The specific calculation steps are as follows:
(4.1) computing the Point set
Figure 213732DEST_PATH_IMAGE019
And
Figure 694523DEST_PATH_IMAGE020
of (2) center
Figure 160140DEST_PATH_IMAGE021
And
Figure 465088DEST_PATH_IMAGE022
and performing centralization treatment:
Figure 750576DEST_PATH_IMAGE023
,
Figure 549905DEST_PATH_IMAGE024
,
Figure 620760DEST_PATH_IMAGE025
，
(4.2) calculating a covariance matrix according to the centralized data point set
Figure 581763DEST_PATH_IMAGE026
And constructing a positive definite matrix through the covariance matrix
Figure 620126DEST_PATH_IMAGE027
：
Figure 206834DEST_PATH_IMAGE028
Figure 381463DEST_PATH_IMAGE029
(4.3) calculating the eigenvalue of the positive definite matrix N, wherein the eigenvector corresponding to the maximum eigenvalue corresponds to the rotation quaternion as follows:
Figure 529679DEST_PATH_IMAGE030
(4.4) rotational quaternion
Figure 789759DEST_PATH_IMAGE031
The rotation matrix R is represented as:
Figure 930890DEST_PATH_IMAGE032
(4.5) according to
Figure 804951DEST_PATH_IMAGE033
Computing a translation matrix
Figure 107756DEST_PATH_IMAGE034
。
Example 1
Off-line programmed robot polishing is the most suitable robot polishing mode at present. The specific idea is that the working conditions around the robot are simulated on a computer through software, the motion trail of the robot is directly generated inside the computer in the virtual three-dimensional working conditions, and a corresponding program is generated and is led into a robot system for engineering operation.
The embodiment further describes the method of the present embodiment by taking the robot off-line programming hub grinding as an example.
The specification of the present embodiment is (550 mm)
Figure DEST_PATH_IMAGE035
550mm
Figure 668182DEST_PATH_IMAGE035
250 mm) of a wheel hub CAD model. In the robot off-line programming polishing scheme, a robot is utilized to collect points on a curved surface to obtain actual measurement point cloud data
Figure 347425DEST_PATH_IMAGE001
(ii) a Dispersing the CAD model of the curved surface according to the processing tolerance to generate theoretical point cloud data
Figure 745914DEST_PATH_IMAGE002
(ii) a In the embodiment, actual measurement point cloud data (12 points) are obtained, and theoretically, the more the actual measurement point cloud data is, the more accurate the calculation is; and (4) dispersing into theoretical point cloud data (2303316).
Given iteration termination accuracy in this embodiment
Figure 219621DEST_PATH_IMAGE009
0.01mm, algorithm run time: 15.4288s, the calculated attitude transformation matrix is:
Figure 454293DEST_PATH_IMAGE036
Figure 687959DEST_PATH_IMAGE038
it will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (3)

1. A KNN-ICP algorithm-based free-form surface positioning method is characterized by comprising the following steps:
(1) using a robot to perform point collection on the curved surface to obtain actual measurement point cloud data
Figure 184760DEST_PATH_IMAGE001
；
(2) Dispersing the CAD model of the curved surface according to the processing tolerance to generate theoretical target point cloud data
Figure 344215DEST_PATH_IMAGE002
；
(3) Calculating actual measurement point cloud by using KNN algorithm
Figure 66183DEST_PATH_IMAGE003
Each data point in
Figure 87229DEST_PATH_IMAGE002
Closest point of (3)
Figure 961775DEST_PATH_IMAGE004
And corresponding shortest distance
Figure 42863DEST_PATH_IMAGE005
Wherein
Figure 235816DEST_PATH_IMAGE006
Indicating number of iterations, initial case
Figure 60553DEST_PATH_IMAGE007
,
Figure 38873DEST_PATH_IMAGE008
(ii) a Is provided with
Figure 776016DEST_PATH_IMAGE009
For a given iteration end precision, if
Figure 472577DEST_PATH_IMAGE010
If yes, turning to the step (5), otherwise, turning to the step (4);
(4) using quaternion method to calculate rotationRotating matrix
Figure 835425DEST_PATH_IMAGE011
And translation matrix
Figure 200677DEST_PATH_IMAGE012
Obtaining new measuring point cloud position
Figure 357988DEST_PATH_IMAGE013
=
Figure 276266DEST_PATH_IMAGE014
，
Figure 193537DEST_PATH_IMAGE015
So that the current measurement point cloud
Figure 146450DEST_PATH_IMAGE016
Approximation
Figure 989510DEST_PATH_IMAGE004
And returning to the step (3);
(5) the iteration is terminated and the object coordinate system of the object relative to the robot is output.
2. The KNN-ICP algorithm-based free-form surface positioning method according to claim 1, wherein in the step (1), the robot acquires points of the free-form surface, and the number of point cloud data is obtained
Figure 395083DEST_PATH_IMAGE017
And is and
Figure 99734DEST_PATH_IMAGE018
。
3. the KNN-ICP algorithm-based free-form surface positioning method according to claim 1, wherein a quaternion method is adopted in the step (4) to calculate the rotation matrix
Figure 657886DEST_PATH_IMAGE011
And translation matrix
Figure 422579DEST_PATH_IMAGE012
Suppose that the actual measurement point set and the theoretical target point set are respectively
Figure 315449DEST_PATH_IMAGE019
And
Figure 73058DEST_PATH_IMAGE020
the rotation matrix is
Figure 563DEST_PATH_IMAGE011
The translation matrix is
Figure 686890DEST_PATH_IMAGE012
The specific calculation steps are as follows:
(4.1) computing the Point set
Figure 67056DEST_PATH_IMAGE019
And
Figure 113510DEST_PATH_IMAGE020
of (2) center
Figure 144788DEST_PATH_IMAGE021
And
Figure 251285DEST_PATH_IMAGE022
and performing centralization treatment:
Figure 869479DEST_PATH_IMAGE023
,
Figure 719623DEST_PATH_IMAGE024
,
Figure 356141DEST_PATH_IMAGE025
，
(4.2) calculating a covariance matrix according to the centralized data point set
Figure 888666DEST_PATH_IMAGE026
And constructing a positive definite matrix through the covariance matrix
Figure 243424DEST_PATH_IMAGE027
：
Figure 631680DEST_PATH_IMAGE028
Figure 139015DEST_PATH_IMAGE029
(4.3) calculating the eigenvalue of the positive definite matrix N, wherein the eigenvector corresponding to the maximum eigenvalue corresponds to the rotation quaternion as follows:
Figure 587314DEST_PATH_IMAGE030
(4.4) rotational quaternion
Figure 413057DEST_PATH_IMAGE031
The rotation matrix R is represented as:
Figure 605004DEST_PATH_IMAGE032
(4.5) according to
Figure 216114DEST_PATH_IMAGE033
Computing a translation matrix
Figure 586046DEST_PATH_IMAGE034
。
CN202010748896.7A 2020-07-30 2020-07-30 KNN-ICP algorithm-based free-form surface positioning method Pending CN111882614A (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
CN202010748896.7A CN111882614A (en) 2020-07-30 2020-07-30 KNN-ICP algorithm-based free-form surface positioning method
PCT/CN2020/108784 WO2022021479A1 (en) 2020-07-30 2020-08-13 Freeform surface positioning method based on knn-icp algorithm

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010748896.7A CN111882614A (en) 2020-07-30 2020-07-30 KNN-ICP algorithm-based free-form surface positioning method

Publications (1)

Publication Number Publication Date
CN111882614A true CN111882614A (en) 2020-11-03

Family

ID=73204247

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010748896.7A Pending CN111882614A (en) 2020-07-30 2020-07-30 KNN-ICP algorithm-based free-form surface positioning method

Country Status (2)

Country Link
CN (1) CN111882614A (en)
WO (1) WO2022021479A1 (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114972448B (en) * 2022-05-26 2024-07-16 合肥工业大学 ICP algorithm-based reduced-dimension acceleration point cloud registration method
CN116991117B (en) * 2023-09-25 2024-01-05 南京航空航天大学 Rapid programming method for personalized part machining
CN118036899B (en) * 2024-04-10 2024-06-14 山东亿昌装配式建筑科技有限公司 Building decoration intelligent management system based on BIM

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20010028247A (en) * 1999-09-20 2001-04-06 유범상 Automatic control method for mold-polishing robot
CN103955939A (en) * 2014-05-16 2014-07-30 重庆理工大学 Boundary feature point registering method for point cloud splicing in three-dimensional scanning system
CN104484508A (en) * 2014-11-26 2015-04-01 华中科技大学 Optimizing method for noncontact three-dimensional matching detection of complex curved-surface part
CN104680530A (en) * 2015-03-01 2015-06-03 江西科技学院 ICP algorithm

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20010028247A (en) * 1999-09-20 2001-04-06 유범상 Automatic control method for mold-polishing robot
CN103955939A (en) * 2014-05-16 2014-07-30 重庆理工大学 Boundary feature point registering method for point cloud splicing in three-dimensional scanning system
CN104484508A (en) * 2014-11-26 2015-04-01 华中科技大学 Optimizing method for noncontact three-dimensional matching detection of complex curved-surface part
CN104680530A (en) * 2015-03-01 2015-06-03 江西科技学院 ICP algorithm

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
夏永刚: "利用三维激光扫描数据和KNNs-ICP算法进行变形分析", 《江西科学》 *

Also Published As

Publication number Publication date
WO2022021479A1 (en) 2022-02-03

Similar Documents

Publication Publication Date Title
CN111882614A (en) KNN-ICP algorithm-based free-form surface positioning method
CN112284290B (en) Autonomous measurement method and system for aero-engine blade robot
CN109883443B (en) Line structure optical sensor spatial attitude calibration method
CN109163675B (en) Method for detecting angular pendulum shaft position accuracy based on laser tracker
Li et al. Interference-free inspection path generation for impeller blades using an on-machine probe
CN102183205A (en) Method for matching optimal assembly poses of large-sized parts
CN114055255B (en) Large-scale complex component surface polishing path planning method based on real-time point cloud
CN113421291B (en) Workpiece position alignment method using point cloud registration technology and three-dimensional reconstruction technology
CN109323665B (en) Precise three-dimensional measurement method for line-structured light-driven holographic interference
CN113486470B (en) Assembly body pose calculation method considering non-ideal surface contact state
CN110103071B (en) Digital locating machining method for deformed complex part
Liu et al. High precision calibration for three-dimensional vision-guided robot system
CN108508848A (en) A kind of appraisal procedure of the Milling Process profile errors based on interpolation data
CN106568365B (en) A kind of detection of sphere hole system complex position degree error and assessment method
CN109343468A (en) A kind of blade multiaxis orbit generation method based on projection biasing
CN115937468A (en) Automatic generation method for machining program of countless-module robot
Wang et al. Fast forward kinematics algorithm for real-time and high-precision control of the 3-RPS parallel mechanism
Xie et al. Pose error estimation using a cylinder in scanner-based robotic belt grinding
CN113799130B (en) Robot pose calibration method in man-machine cooperation assembly
CN109773593B (en) Grinding method based on allowance constraint condition
CN112936274B (en) Robot-clamped flexible grinding wheel pose identification method
CN112784364A (en) Method for calculating machining allowance of aircraft wing body joint
CN116587268B (en) Milling precision improving method for large-area robot in space
Zhang et al. Robot automation grinding process for nuclear reactor coolant pump based on reverse engineering
CN114131605B (en) Automatic registration adjustment device and method for ship part polishing track

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication

Application publication date: 20201103

RJ01 Rejection of invention patent application after publication