WO2024113216A1 - High-precision grasping method of industrial mold intelligent manufacturing robot - Google Patents

High-precision grasping method of industrial mold intelligent manufacturing robot Download PDF

Info

Publication number
WO2024113216A1
WO2024113216A1 PCT/CN2022/135360 CN2022135360W WO2024113216A1 WO 2024113216 A1 WO2024113216 A1 WO 2024113216A1 CN 2022135360 W CN2022135360 W CN 2022135360W WO 2024113216 A1 WO2024113216 A1 WO 2024113216A1
Authority
WO
WIPO (PCT)
Prior art keywords
grasping
robot
intelligent manufacturing
camera
precision
Prior art date
Application number
PCT/CN2022/135360
Other languages
French (fr)
Chinese (zh)
Inventor
武宁宁
李杨
Original Assignee
青岛理工大学（临沂）
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 青岛理工大学（临沂） filed Critical 青岛理工大学（临沂）
Priority to PCT/CN2022/135360 priority Critical patent/WO2024113216A1/en
Publication of WO2024113216A1 publication Critical patent/WO2024113216A1/en

Links

Images

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators

Definitions

  • the invention belongs to the technical field of robot grasping, and in particular relates to a high-precision grasping method of an industrial mold intelligent manufacturing robot.
  • Robots have been widely used in welding, palletizing, packaging and other industrial industries, which can not only improve labor productivity but also reduce labor costs.
  • Industrial mold intelligent manufacturing robots have the advantages of precision, high flexibility, high speed, high rigidity, and light weight, and are suitable for grasping work. Labor costs continue to increase with economic development. In order to reduce labor costs, more and more companies are beginning to use robots. In the context of continuous progress and development of science and technology, industrial mold intelligent manufacturing robots have become an indispensable part of the manufacturing industry. Therefore, it is of great significance to study the high-precision grasping method of robots.
  • Zou Yu et al. proposed a robot grasping control method based on the combination of 3D point cloud depth information and centroid distance.
  • the data was collected through the Kinect sensor, denoised and segmented to obtain the boundary point cloud of the grasped target, the angle between the grasping point and the normal vector of the boundary point cloud in the straight line was obtained, and a comprehensive evaluation function was constructed to obtain the optimal grasping position and complete the robot grasping control.
  • this method controls the robot to grasp, it cannot accurately track the changes in the robot joints, the tracking error is large, and there is a problem of low grasping accuracy.
  • Ge Junyan [4] et al. proposed a robot grasping method based on a 3D detection network.
  • the acquired image is input into a convolutional neural network to identify the object information, and the 3D bounding box of the target is constructed based on the acquired information.
  • the center point of the target is obtained, and the grasping strategy is designed to realize the robot grasping control.
  • this method plans the robot's grasping path, the shortest grasping path and the average grasping path are both long, which increases the time taken by the robot to grasp and has the problem of low grasping efficiency.
  • the purpose of the present invention is to provide a high-precision grasping method for an industrial mold intelligent manufacturing robot to solve the problems existing in the above-mentioned prior art.
  • the present invention provides a high-precision grasping method for an industrial mold intelligent manufacturing robot, comprising:
  • the process of calibrating the camera includes: converting the world coordinate system to the camera coordinate system, obtaining the imaging plane coordinate system based on the camera coordinate system, processing the imaging plane coordinate system based on the radial distortion value to obtain corrected coordinates, and obtaining image coordinates corresponding to the projection center based on the corrected coordinates, the image sensor pixel size and the vertical projection of the projection center in the imaging plane coordinate system.
  • the process of obtaining robot information and target point information includes: obtaining images of the robot and the target workpiece, image acquisition times, and positions of the robot and the target workpiece based on an industrial smart camera, a preset acquisition interval, and a speed encoder, wherein the industrial smart camera and the speed encoder are installed on a conveyor belt, and the industrial smart camera is a camera for calibration processing.
  • the acquisition interval is obtained based on a maximum length of a camera field of view and a target workpiece.
  • the process of acquiring the target workpiece information further includes: constructing sets of two adjacent images respectively, and determining whether there is duplication by comparing elements in the sets pairwise, wherein the elements in the sets are coordinate information of the images.
  • the process of obtaining the grasping path includes: determining the coordinates of the target workpiece grasping point and the target workpiece control point, obtaining the transition point on the conveyor belt when it is lifted to a preset height, obtaining the remaining passing points based on the transition radius, and obtaining the grasping path based on the principles of path smoothness and arc transition corners, combining the obtained points, wherein the transition radius is obtained based on the algorithm of inserting the transition arc radius into the transfer point.
  • the process of obtaining the grasping position includes: obtaining the grasping position based on the position corresponding to the target workpiece and the conveyor belt speed and using a binary search method.
  • an adaptive coordination controller model is designed based on the grasping trajectory, the grasping position and the master-slave multi-technology.
  • the present invention proposes a high-precision grasping method for an industrial mold intelligent manufacturing robot.
  • the grasping trajectory and grasping points are determined according to the acquired robot position information, and an adaptive coordination controller is designed to achieve high-precision grasping of the industrial mold intelligent manufacturing robot, providing a reliable research basis for multi-target grasping of the industrial mold intelligent manufacturing robot and other research in the field.
  • FIG1 is a schematic diagram of a grabbing path in an embodiment of the present invention.
  • FIG. 2 is a schematic diagram of a method flow in an embodiment of the present invention.
  • this embodiment provides a high-precision grasping method of an industrial mold intelligent manufacturing robot, including:
  • the camera In order to obtain the position of the industrial mold intelligent manufacturing robot and the target point, the camera needs to be calibrated and processed as follows:
  • a c (x c , y c , z c ) T and A w (x w , y w , z w ) T are used to represent the points in the camera coordinate system and the world coordinate system respectively.
  • the above transformation process is described by the following formula:
  • T ( ⁇ , ⁇ , ⁇ ) represents the rotation matrix, where ⁇ , ⁇ , and ⁇ represent the rotation angles around the x-axis, y-axis, and z-axis in the camera coordinate system, respectively;
  • U (u x , u y , u z ) T represents the translation vector.
  • the calculated imaging plane coordinates usually have errors.
  • the main reason for this phenomenon is the existence of lens distortion. Therefore, the high-precision grasping method of the industrial mold intelligent manufacturing robot corrects the lens distortion through the following formula:
  • (c, d) represents the coordinates corresponding to the projection center; D y and D x both represent the pixel size of the image sensor; V y and V x both represent the vertical projection of the projection center in the imaging plane.
  • the camera calibration is completed to solve the lens distortion problem.
  • the calibrated camera is used to obtain relevant information between the industrial mold intelligent manufacturing robot and the target point. This section comprehensively plans the grasping strategy.
  • the high-precision grasping method of the industrial mold intelligent manufacturing robot sets the speed encoder in the conveyor belt, and obtains the position coordinates ( xn , yn , Mn ) of the target workpiece at any time according to the number of feedback pulses obtained.
  • the position coordinates (x, y) of the target during the operation of the conveyor belt can be obtained at any time:
  • Lc represents the distance traveled per unit pulse number
  • Mn represents the pulse value of the speed encoder at the time of image acquisition
  • M represents the pulse value of the speed encoder at the current moment.
  • the conveyor belt usually works continuously, so the target workpiece will constantly enter the camera's field of view.
  • the camera In order to obtain the position of the target workpiece, the camera needs to continuously collect images of the target workpiece.
  • the collection interval needs to be determined. If the interval is too short, the number of image processing will increase. If the interval is too long, it is easy to miss the target.
  • the camera's field of view is represented by a ⁇ b.
  • the high-precision grasping method of the industrial mold intelligent manufacturing robot uses the speed encoder feedback to complete the control, and the acquisition interval of the industrial intelligent camera is set to the transmission distance ds per movement.
  • the conveyor belt and the target workpiece are in a stationary state, so the repeatedly photographed target workpiece position information coordinate y remains consistent after image processing, and the x coordinate changes, with a difference of ds.
  • the maximum length of the target workpiece in the x direction is l, and at this time:
  • the phenomenon of repeated workpiece position information may appear in two adjacent images.
  • the above sets are composed of the coordinate information in the first and second images respectively. By comparing the elements one by one, it is determined whether there are duplicate coordinates in set S and set B:
  • ⁇ represents the pairwise comparison matrix; when the value of the parameter ⁇ i,j is zero, it indicates that repeated acquisition has occurred and the two coordinate points are repeated.
  • the main goal of high-precision grasping of industrial mold intelligent manufacturing robots is to accurately control the robot to reach the placement point and grasping point, reduce the robot's movement time, and ensure the smoothness of the robot's movement trajectory.
  • T represents the time it takes the robot to move from (x 0 ,y 0 ) to (x d ,y d ).
  • the high-precision grasping method of the industrial mold intelligent manufacturing robot combines the conveyor belt speed on the basis of solving the equation by dichotomy to obtain the position corresponding to the grasping point:
  • xn0 represents the position of the target workpiece when the robot starts moving.
  • the high-precision grasping method of the industrial mold intelligent manufacturing robot is designed based on a one-master-multiple-slave adaptive collaborative controller model according to the planned grasping trajectory and grasping point position obtained in the above process to achieve high-precision grasping work of the industrial mold intelligent manufacturing robot.
  • G N represents the force generated by the manufacturing environment
  • a N represents the velocity of the joint of the industrial mold intelligent manufacturing robot
  • Q N (a N ) represents the positive definite inertia matrix
  • ⁇ N represents the robot torque
  • h N (a N ) represents the gravity term
  • G h represents the force applied to the robot by the operator.
  • the high-precision grasping method of the industrial mold intelligent manufacturing robot establishes the adaptive collaborative controller model of the tool mold intelligent manufacturing robot, and the expression is as follows:
  • ⁇ m represents the damping coefficient corresponding to the main manufacturing robot
  • k represents the proportional index
  • fim (t) represents the delay time of the manufacturing robot
  • ⁇ z represents the following structure
  • ⁇ z [N] represents the number of adjacent industrial mold intelligent manufacturing robots
  • tfim (t) represents the delay time between adjacent robots, all of which are measured values
  • ⁇ m represents the torque of the main manufacturing robot and am represents the velocity of the main manufacturing robot.
  • ⁇ s represents the damping coefficient corresponding to the manufacturing robot.
  • ⁇ g represents the leadership structure from which the robot is manufactured.
  • the high-precision grasping method of the industrial mold intelligent manufacturing robot uses the adaptive collaborative controller constructed by the above process to complete the high-precision grasping control of the industrial mold intelligent manufacturing robot.

Landscapes

  • Engineering & Computer Science (AREA)
  • Robotics (AREA)
  • Mechanical Engineering (AREA)
  • Moulds For Moulding Plastics Or The Like (AREA)

Abstract

A high-precision grasping method of an industrial mold intelligent manufacturing robot, comprising: calibrating a camera, obtaining information of an industrial mold intelligent manufacturing robot and target point information by correcting a lens of the camera, planning a grasping trajectory of the industrial mold intelligent manufacturing robot, and determining a grasping point position; and on this basis, constructing an adaptive cooperative controller, and constructing an adaptive cooperative control model by combining the grasping trajectory and a target grasping point, so as to implement high-precision grasping of the industrial mold intelligent manufacturing robot. The grasping trajectory and the grasping point are determined according to obtained robot position information, and the adaptive cooperative controller is designed, such that high-precision grasping of the industrial mold intelligent manufacturing robot is implemented, thereby providing a reliable exploration foundation for multi-target grasping of industrial mold intelligent manufacturing robots and other research in the field.

Description

一种工业模具智能制造机器人高精度抓取方法A high-precision grasping method for industrial mold intelligent manufacturing robots 技术领域Technical Field
本发明属于机器人抓取技术领域，特别是涉及一种工业模具智能制造机器人高精度抓取方法。The invention belongs to the technical field of robot grasping, and in particular relates to a high-precision grasping method of an industrial mold intelligent manufacturing robot.
背景技术Background technique
在焊接、码垛、包装等工业行业中机器人已经得到了广泛的应用，不仅可以提高劳动生产率，还能降低劳动成本。工业模具智能制造机器人具有精准、高柔性、高速、高刚性、质量轻等优点，适用于抓取工作。劳动成本随着经济的发展不断提高，为了降低劳动成本，越来越多的企业开始使用机器人，在科技水平不断进步和发展的背景下，工业模具智能制造机器人成为制造业中不可或缺的一部分，因此研究机器人高精度抓取方法具有重要意义。Robots have been widely used in welding, palletizing, packaging and other industrial industries, which can not only improve labor productivity but also reduce labor costs. Industrial mold intelligent manufacturing robots have the advantages of precision, high flexibility, high speed, high rigidity, and light weight, and are suitable for grasping work. Labor costs continue to increase with economic development. In order to reduce labor costs, more and more companies are beginning to use robots. In the context of continuous progress and development of science and technology, industrial mold intelligent manufacturing robots have become an indispensable part of the manufacturing industry. Therefore, it is of great significance to study the high-precision grasping method of robots.
邹遇等人提出基于3维点云深度信息和质心距相结合的机器人抓取控制方法。通过Kinect传感器采集数据，对数据实行去噪和分割处理，得到抓取目标的边界点云，获取直线中抓取点与边界点云法向量之间存在的夹角，构建综合评价函数，获取最优抓取位置，完成机器人的抓取控制，该方法控制机器人实行抓取工作时，无法准确跟踪机器人关节的变化情况，跟踪误差较大，存在抓取精度低的问题。葛俊彦 [4]等人提出基于三维检测网络的机器人抓取方法。将获取的图像输入卷积神经网络中，对物体信息识别，根据获取的信息构建目标的三维包围盒，获取目标的中心点，以此设计抓取策略，实现机器人的抓取控制，该方法规划机器人的抓取路径时，获得的最短抓取路径和平均抓取路径均较长，增加了机器人抓取所用的时 间，存在抓取效率低的问题。 Zou Yu et al. proposed a robot grasping control method based on the combination of 3D point cloud depth information and centroid distance. The data was collected through the Kinect sensor, denoised and segmented to obtain the boundary point cloud of the grasped target, the angle between the grasping point and the normal vector of the boundary point cloud in the straight line was obtained, and a comprehensive evaluation function was constructed to obtain the optimal grasping position and complete the robot grasping control. When this method controls the robot to grasp, it cannot accurately track the changes in the robot joints, the tracking error is large, and there is a problem of low grasping accuracy. Ge Junyan [4] et al. proposed a robot grasping method based on a 3D detection network. The acquired image is input into a convolutional neural network to identify the object information, and the 3D bounding box of the target is constructed based on the acquired information. The center point of the target is obtained, and the grasping strategy is designed to realize the robot grasping control. When this method plans the robot's grasping path, the shortest grasping path and the average grasping path are both long, which increases the time taken by the robot to grasp and has the problem of low grasping efficiency.
发明内容Summary of the invention
本发明的目的是提供一种工业模具智能制造机器人高精度抓取方法，以解决上述现有技术存在的问题。The purpose of the present invention is to provide a high-precision grasping method for an industrial mold intelligent manufacturing robot to solve the problems existing in the above-mentioned prior art.
为实现上述目的，本发明提供了一种工业模具智能制造机器人高精度抓取方法，包括：To achieve the above object, the present invention provides a high-precision grasping method for an industrial mold intelligent manufacturing robot, comprising:
对相机进行标定处理，通过标定后的相机获得机器人信息与目标工件信息；Calibrate the camera and obtain the robot information and target workpiece information through the calibrated camera;
基于所述机器人信息与所述目标点信息获得抓取路径与抓取位置；Obtaining a grasping path and a grasping position based on the robot information and the target point information;
构建自适应协同控制器模型；Construct an adaptive collaborative controller model;
基于所述抓取路径、所述抓取位置及所述自适应协同控制器模型，实现机器人高精度抓取。Based on the grasping path, the grasping position and the adaptive collaborative controller model, high-precision grasping of the robot is achieved.
可选的，对相机进行标定处理的过程包括：进行对世界坐标系到相机坐标系的转换，基于所述相机坐标系获得成像平面坐标系，基于径向扭曲值对所述成像平面坐标系进行处理，获得校正后的坐标，基于校正后的坐标、图像传感器像元大小与成像平面坐标系内投影中心的垂直投影获得投影中心对应的图像坐标。Optionally, the process of calibrating the camera includes: converting the world coordinate system to the camera coordinate system, obtaining the imaging plane coordinate system based on the camera coordinate system, processing the imaging plane coordinate system based on the radial distortion value to obtain corrected coordinates, and obtaining image coordinates corresponding to the projection center based on the corrected coordinates, the image sensor pixel size and the vertical projection of the projection center in the imaging plane coordinate system.
可选的，获取机器人信息与目标点信息的过程包括：基于工业智能相机、预设定采集间隔及测速编码器获得机器人与目标工件的图像、图像采集时刻及机器人与目标工件位置，其中，所述工业智能相机与测速编码器安装在传送带上，所述工业智能相机为进行标定处理的相机。Optionally, the process of obtaining robot information and target point information includes: obtaining images of the robot and the target workpiece, image acquisition times, and positions of the robot and the target workpiece based on an industrial smart camera, a preset acquisition interval, and a speed encoder, wherein the industrial smart camera and the speed encoder are installed on a conveyor belt, and the industrial smart camera is a camera for calibration processing.
可选的，基于相机视野与目标工件的最大长度获得所述采集间隔。Optionally, the acquisition interval is obtained based on a maximum length of a camera field of view and a target workpiece.
可选的，目标工件信息的获取过程还包括：分别构建相邻两幅图像的集合，通过集合中的元素两两对比的方式判断是否存在重复，其中，集合中的元素为图像的坐标信息。Optionally, the process of acquiring the target workpiece information further includes: constructing sets of two adjacent images respectively, and determining whether there is duplication by comparing elements in the sets pairwise, wherein the elements in the sets are coordinate information of the images.
可选的，获得抓取路径的过程包括：确定目标工件抓取点与目标工件控制点的坐标，获取在抬起预设高度时传送带上的过渡点，基于过渡半径获得其余经过点，基于路径平滑的原则与弧线过度拐角处的原则，结合获得的点获得抓取路径，其中，过渡半径依据转接点***过渡圆弧半径算法获得。Optionally, the process of obtaining the grasping path includes: determining the coordinates of the target workpiece grasping point and the target workpiece control point, obtaining the transition point on the conveyor belt when it is lifted to a preset height, obtaining the remaining passing points based on the transition radius, and obtaining the grasping path based on the principles of path smoothness and arc transition corners, combining the obtained points, wherein the transition radius is obtained based on the algorithm of inserting the transition arc radius into the transfer point.
可选的，获取抓取位置的过程包括：基于目标工件对应的位置、传送带速度，并采用二分法获得抓取位置。Optionally, the process of obtaining the grasping position includes: obtaining the grasping position based on the position corresponding to the target workpiece and the conveyor belt speed and using a binary search method.
可选的，基于所述抓取轨迹、所述抓取位置及主从多技术设计自适应协调控制器模型。Optionally, an adaptive coordination controller model is designed based on the grasping trajectory, the grasping position and the master-slave multi-technology.
本发明的技术效果为：The technical effects of the present invention are:
本发明提出一种工业模具智能制造机器人高精度抓取方法，根据获取的机器人位置信息确定抓取轨迹和抓取点，设计自适应协调控制器，实现工业模具智能制造机器人的高精度抓取，为工业模具智能制造机器人的多目标抓取及领域内其他的研究提供可靠的探究基础。The present invention proposes a high-precision grasping method for an industrial mold intelligent manufacturing robot. The grasping trajectory and grasping points are determined according to the acquired robot position information, and an adaptive coordination controller is designed to achieve high-precision grasping of the industrial mold intelligent manufacturing robot, providing a reliable research basis for multi-target grasping of the industrial mold intelligent manufacturing robot and other research in the field.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
构成本申请的一部分的附图用来提供对本申请的进一步理解，本申请的示意性实施例及其说明用于解释本申请，并不构成对本申请的不当限定。在附图中：The drawings constituting a part of this application are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an improper limitation on this application. In the drawings:
图1为本发明实施例中的抓取路径示意图；FIG1 is a schematic diagram of a grabbing path in an embodiment of the present invention;
图2为本发明实施例中的方法流程示意图。FIG. 2 is a schematic diagram of a method flow in an embodiment of the present invention.
具体实施方式Detailed ways
需要说明的是，在不冲突的情况下，本申请中的实施例及实施例中的特征可以相互组合。下面将参考附图并结合实施例来详细说明本申请。It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.
需要说明的是，在附图的流程图示出的步骤可以在诸如一组计算机可执行指令的计算机***中执行，并且，虽然在流程图中示出了逻辑顺序，但是在某些情况下，可以以不同于此处的顺序执行所示出或描述的步骤。It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.
实施例一Embodiment 1
如图1-2所示，本实施例中提供一种工业模具智能制造机器人高精度抓取方法，包括：As shown in FIG1-2, this embodiment provides a high-precision grasping method of an industrial mold intelligent manufacturing robot, including:
S1.相机标定S1. Camera calibration
在相机成像过程中镜头容易产生切向畸变和径向畸变，为了获取工业模具智能制造机器人和目标点的位置，需要对相机实行标定处理体过程如下：During the camera imaging process, the lens is prone to tangential distortion and radial distortion. In order to obtain the position of the industrial mold intelligent manufacturing robot and the target point, the camera needs to be calibrated and processed as follows:
(1)世界坐标系与相机坐标系之间的转变(1) Transformation between world coordinate system and camera coordinate system
可用刚性变换描述两者之间的转变过程，分别用A c(x c,y c,z c) T、A w(x w,y w,z w) T表示相机坐标系和世界坐标系中存在的点，通过下述公式描述上述转变过程： The transformation process between the two can be described by rigid transformation. A c (x c , y c , z c ) T and A w (x w , y w , z w ) T are used to represent the points in the camera coordinate system and the world coordinate system respectively. The above transformation process is described by the following formula:
A c＝TA w+U                         (1) A c =TA w +U (1)
式中，T＝(α,β,γ)表示旋转矩阵，其中α、β、γ分别表示在摄像机坐标系中围绕x轴、y轴和z轴的旋转角度；U＝(u x,u y,u z) T表示平移向量。 In the formula, T = (α, β, γ) represents the rotation matrix, where α, β, and γ represent the rotation angles around the x-axis, y-axis, and z-axis in the camera coordinate system, respectively; U = (u x , u y , u z ) T represents the translation vector.
计算获取的成像平面坐标通常存在误差，造成这种现象的主要原因是 存在镜头畸变，因此，工业模具智能制造机器人高精度抓取方法通过下式校正镜头畸变：The calculated imaging plane coordinates usually have errors. The main reason for this phenomenon is the existence of lens distortion. Therefore, the high-precision grasping method of the industrial mold intelligent manufacturing robot corrects the lens distortion through the following formula:
Figure PCTCN2022135360-appb-000001
Figure PCTCN2022135360-appb-000001
式中，
Figure PCTCN2022135360-appb-000002
代表校正后的坐标；l代表径向扭曲的大小。 In the formula,
Figure PCTCN2022135360-appb-000002
represents the corrected coordinate; l represents the magnitude of radial distortion.
(2)成像平面坐标与图像坐标系之间的转换可通过下式得以描述：(2) The conversion between the imaging plane coordinates and the image coordinate system can be described by the following formula:
Figure PCTCN2022135360-appb-000003
Figure PCTCN2022135360-appb-000003
式中，(c,d)代表投影中心对应的坐标；D y、D x均表示图像传感器像元大小；V y、V x均表示在成像平面内投影中心的垂直投影。 Wherein, (c, d) represents the coordinates corresponding to the projection center; D y and D x both represent the pixel size of the image sensor; V y and V x both represent the vertical projection of the projection center in the imaging plane.
S2.抓取策略规划S2. Grasping strategy planning
基于上述过程完成相机标定，解决镜头畸变问题，利用标定后的相机获取工业模具智能制造机器人与目标点的相关信息，本小节全面规划抓取策略。Based on the above process, the camera calibration is completed to solve the lens distortion problem. The calibrated camera is used to obtain relevant information between the industrial mold intelligent manufacturing robot and the target point. This section comprehensively plans the grasping strategy.
(一)单目标表示1. Single Target Representation
在传送带上安装工业智能相机，设定采集间隔，获得采集时刻以及目标位置。工业模具智能制造机器人高精度抓取方法将测速编码器设置在传送带中，根据获取的反馈脉冲数获得目标工件在任意时刻中的位置坐标(x n,y n,M n)，通过上述过程可以在任意时刻获取目标在传送带运行过程中的位置坐标(x,y)： Install an industrial smart camera on the conveyor belt, set the acquisition interval, and obtain the acquisition time and target position. The high-precision grasping method of the industrial mold intelligent manufacturing robot sets the speed encoder in the conveyor belt, and obtains the position coordinates ( xn , yn , Mn ) of the target workpiece at any time according to the number of feedback pulses obtained. Through the above process, the position coordinates (x, y) of the target during the operation of the conveyor belt can be obtained at any time:
(x,y)＝[x n+L c(M-M n),y n]           (4) (x,y)＝[ xn + Lc ( MMn ), yn ] (4)
式中，L c代表单位脉冲数的前进距离；M n代表测速编码器在图像采集 时刻的脉冲值；M代表测速编码器在当前时刻的脉冲值。 Where Lc represents the distance traveled per unit pulse number; Mn represents the pulse value of the speed encoder at the time of image acquisition; and M represents the pulse value of the speed encoder at the current moment.
(二)多目标表示2. Multi-objective representation
传送带通常情况下是持续工作的，因此目标工件会不断地进入相机视野内，为了获取目标工件的位置，相机需要一直采集目标工件的图像。在采集过程中需要确定采集间隔，间隔太短会增大图像处理数量，间隔过长，容易出现目标漏采的现象。The conveyor belt usually works continuously, so the target workpiece will constantly enter the camera's field of view. In order to obtain the position of the target workpiece, the camera needs to continuously collect images of the target workpiece. During the collection process, the collection interval needs to be determined. If the interval is too short, the number of image processing will increase. If the interval is too long, it is easy to miss the target.
用a×b表示相机的视野，针对相机采集触发信号，工业模具智能制造机器人高精度抓取方法利用测速编码器反馈完成控制，将工业智能相机的采集间隔设置为传送每移动距离ds。传送带与目标工件之间处于静止状态，因此重复拍摄的目标工件位置信息坐标y经过图像处理后保持一致，x坐标发生变化，相差ds，在x方向中目标工件的最大长度为l，此时存在：The camera's field of view is represented by a×b. For the camera's acquisition trigger signal, the high-precision grasping method of the industrial mold intelligent manufacturing robot uses the speed encoder feedback to complete the control, and the acquisition interval of the industrial intelligent camera is set to the transmission distance ds per movement. The conveyor belt and the target workpiece are in a stationary state, so the repeatedly photographed target workpiece position information coordinate y remains consistent after image processing, and the x coordinate changes, with a difference of ds. The maximum length of the target workpiece in the x direction is l, and at this time:
(1)当ds的值大于等于a时，出现漏拍的概率较大；(1) When the value of ds is greater than or equal to a, the probability of missing a shot is high;
(2)当ds的值大于等于a+l/2时，获取的目标工件图像完整度较低；(2) When the value of ds is greater than or equal to a+l/2, the integrity of the acquired target workpiece image is low;
(3)当a-l/2≥ds≥a-l时，重复拍摄概率增大。(3) When a-l/2≥ds≥a-l, the probability of repeated shooting increases.
通过上述分析工业模具智能制造机器人高精度抓取方法设定ds＝a-l。工件位置信息重复的现象可能会出现在相邻的两幅图像中，为了消除重复的位置坐标，需要逐个对比采集的位置坐标，设定集合S和集合B：Through the above analysis, the high-precision grasping method of the industrial mold intelligent manufacturing robot is set to ds = a-l. The phenomenon of repeated workpiece position information may appear in two adjacent images. In order to eliminate the repeated position coordinates, it is necessary to compare the collected position coordinates one by one, and set S and set B:
Figure PCTCN2022135360-appb-000004
Figure PCTCN2022135360-appb-000004
上述集合分别由第一幅和第二幅图像中存在的坐标信息构成，通过元素两两对比，判断集合S和集合B中是否存在重复坐标：The above sets are composed of the coordinate information in the first and second images respectively. By comparing the elements one by one, it is determined whether there are duplicate coordinates in set S and set B:
Figure PCTCN2022135360-appb-000005
Figure PCTCN2022135360-appb-000005
式中，Δ代表两两对比矩阵；当参数ε i,j的值为零时，表明出现了重复 采集现象，两个坐标点重复。 In the formula, Δ represents the pairwise comparison matrix; when the value of the parameter ε i,j is zero, it indicates that repeated acquisition has occurred and the two coordinate points are repeated.
(三)规划抓取路径(III) Planning the crawling path
工业模具智能制造机器人执行任务时，可能会受到障碍物的阻碍，“门型”路径是常用的运行路径，工业模具智能制造机器人高精度抓取方法采用弧线过渡拐角处，以提高工业模具机器人在运动过程中的平滑性和运行速度，工业模具智能制造机器人高精度抓取方法生成的笛卡尔空间末端运动路径如图1所示。When the industrial mold intelligent manufacturing robot performs tasks, it may be hindered by obstacles. The "gate-type" path is a commonly used operating path. The high-precision grasping method of the industrial mold intelligent manufacturing robot adopts arc transition corners to improve the smoothness and operating speed of the industrial mold robot during movement. The Cartesian space terminal motion path generated by the high-precision grasping method of the industrial mold intelligent manufacturing robot is shown in Figure 1.
首先确定目标抓取点和目标放置点A 1、A 8分别对应的坐标，在抬起高度j的基础上确定传送带中存在的过渡点A 3、A 6，根据其过渡半径r 1、r 2确定该段传送带中存在的经过点A 2、A 4、A 5、A 7，通过上述过程获得工业模具智能制造机器人的抓取放置路径： First, determine the coordinates of the target grasping point and the target placement point A 1 and A 8 respectively, determine the transition points A 3 and A 6 in the conveyor belt based on the lifting height j, and determine the passing points A 2 , A 4 , A 5 , and A 7 in the conveyor belt according to their transition radii r 1 and r 2. Through the above process, the grasping and placement path of the industrial mold intelligent manufacturing robot is obtained:
Figure PCTCN2022135360-appb-000006
Figure PCTCN2022135360-appb-000006
工业模具智能制造机器人高精度抓取的主要目标是准确的控制机器人到达放置点和抓取点的同时，减少机器人的运动时间，并且保证机器人运动轨迹的平滑性。The main goal of high-precision grasping of industrial mold intelligent manufacturing robots is to accurately control the robot to reach the placement point and grasping point, reduce the robot's movement time, and ensure the smoothness of the robot's movement trajectory.
(四)抓取位置(IV) Grasping position
设(x d,y d)代表抓取点对应的位置；(x n,y n,N n)代表的目标工件对应的位置，开始工作时，工业模具智能制造机器人末端对应的位置为(x 0,y 0)，编码器在传感器中的当前读数为N 0，从初始点开始工业模具智能制造机器人运动到抓取位置花费的时间t，可通过下式计算得到： Assume (x d , y d ) represents the position corresponding to the grasping point; (x n , yn ,N n ) represents the position corresponding to the target workpiece. When starting work, the position corresponding to the end of the industrial mold intelligent manufacturing robot is (x 0 ,y 0 ), and the current reading of the encoder in the sensor is N 0 . The time t taken by the industrial mold intelligent manufacturing robot to move from the initial point to the grasping position can be calculated by the following formula:
t＝t s+T                             (8) t＝ ts +T (8)
式中，t s代表补偿时间，T代表机器人从(x 0,y 0)运动到(x d,y d)花费的时 间。 Where ts represents the compensation time, and T represents the time it takes the robot to move from (x 0 ,y 0 ) to (x d ,y d ).
工业模具智能制造机器人高精度抓取方法在二分法解方程的基础上结合传送带速度，获得抓取点对应的位置：The high-precision grasping method of the industrial mold intelligent manufacturing robot combines the conveyor belt speed on the basis of solving the equation by dichotomy to obtain the position corresponding to the grasping point:
Figure PCTCN2022135360-appb-000007
Figure PCTCN2022135360-appb-000007
式中，x n0代表目标工件当机器人开始运动时所处的位置。 Where xn0 represents the position of the target workpiece when the robot starts moving.
S3.抓取控制S3. Grasp control
工业模具智能制造机器人高精度抓取方法根据上述过程获取的规划抓取轨迹和抓取点位置设计基于一主多从的自适应协同控制器模型，实现工业模具智能制造机器人的高精度抓取工作。The high-precision grasping method of the industrial mold intelligent manufacturing robot is designed based on a one-master-multiple-slave adaptive collaborative controller model according to the planned grasping trajectory and grasping point position obtained in the above process to achieve high-precision grasping work of the industrial mold intelligent manufacturing robot.
通过非线性动力学模型描述一主多从技术：Describe the one-master-multiple-slave technology through nonlinear dynamic model:
Figure PCTCN2022135360-appb-000008
Figure PCTCN2022135360-appb-000008
式中，G N表示制造环境产生的力；a N代表工业模具智能制造机器人关节的速率；Q N(a N)代表正定惯性矩阵；υ N代表机器人力矩；
Figure PCTCN2022135360-appb-000009
表示向心力；
Figure PCTCN2022135360-appb-000010
代表机器人的加速度向量；h N(a N)代表重力项；G h代表操作者施加到机器人上的力。 Where G N represents the force generated by the manufacturing environment; a N represents the velocity of the joint of the industrial mold intelligent manufacturing robot; Q N (a N ) represents the positive definite inertia matrix; υ N represents the robot torque;
Figure PCTCN2022135360-appb-000009
Indicates centripetal force;
Figure PCTCN2022135360-appb-000010
represents the acceleration vector of the robot; h N (a N ) represents the gravity term; G h represents the force applied to the robot by the operator.
工业模具智能制造机器人高精度抓取方法建立工具模具智能制造机器人自适应协同控制器模型，表达式如下：The high-precision grasping method of the industrial mold intelligent manufacturing robot establishes the adaptive collaborative controller model of the tool mold intelligent manufacturing robot, and the expression is as follows:
Figure PCTCN2022135360-appb-000011
Figure PCTCN2022135360-appb-000011
式中，β m代表主制造机器人对应的阻尼系数；k表示比例指数；f im(t)代表制造机器人的延迟时间；Δz表示追随结构；Δz [N]表示相邻工业模具智能制造机器人的数量；t-f im(t)表示相邻机器人之间存在的延迟时间，均为测 量值，υ m代表主制造机器人力矩和a m代表主制造机器人速率。 Wherein, βm represents the damping coefficient corresponding to the main manufacturing robot; k represents the proportional index; fim (t) represents the delay time of the manufacturing robot; Δz represents the following structure; Δz [N] represents the number of adjacent industrial mold intelligent manufacturing robots; tfim (t) represents the delay time between adjacent robots, all of which are measured values, υm represents the torque of the main manufacturing robot and am represents the velocity of the main manufacturing robot.
当i∈Δz时，存在下式：When i∈Δz, the following equation exists:
Figure PCTCN2022135360-appb-000012
Figure PCTCN2022135360-appb-000012
式中，
Figure PCTCN2022135360-appb-000013
表示相邻工业模具制造机器人的数量；β s代表从制造机器人对应的阻尼系数。 In the formula,
Figure PCTCN2022135360-appb-000013
represents the number of adjacent industrial mold manufacturing robots; βs represents the damping coefficient corresponding to the manufacturing robot.
当i∈Δg时，存在下式：When i∈Δg, the following formula exists:
Figure PCTCN2022135360-appb-000014
Figure PCTCN2022135360-appb-000014
其中，Δg代表从制造机器人的领导结构。Where Δg represents the leadership structure from which the robot is manufactured.
工业模具智能制造机器人高精度抓取方法利用上述过程构建的自适应协同控制器完成工业模具智能制造机器人的高精度抓取控制。The high-precision grasping method of the industrial mold intelligent manufacturing robot uses the adaptive collaborative controller constructed by the above process to complete the high-precision grasping control of the industrial mold intelligent manufacturing robot.
以上所述，仅为本申请较佳的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到的变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应该以权利要求的保护范围为准。The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims (8)

  1. 一种工业模具智能制造机器人高精度抓取方法，其特征在于，包括以下步骤：A high-precision grasping method for an industrial mold intelligent manufacturing robot, characterized in that it comprises the following steps:
    对相机进行标定处理，通过标定后的相机获得机器人信息与目标工件信息；Calibrate the camera and obtain the robot information and target workpiece information through the calibrated camera;
    基于所述机器人信息与所述目标点信息获得抓取路径与抓取位置；Obtaining a grasping path and a grasping position based on the robot information and the target point information;
    构建自适应协同控制器模型；Construct an adaptive collaborative controller model;
    基于所述抓取路径、所述抓取位置及所述自适应协同控制器模型，实现机器人高精度抓取。Based on the grasping path, the grasping position and the adaptive collaborative controller model, high-precision grasping of the robot is achieved.
  2. 根据权利要求1所述的工业模具智能制造机器人高精度抓取方法，其特征在于，对相机进行标定处理的过程包括：进行对世界坐标系到相机坐标系的转换，基于所述相机坐标系获得成像平面坐标系，基于径向扭曲值对所述成像平面坐标系进行处理，获得校正后的坐标，基于校正后的坐标、图像传感器像元大小与成像平面坐标系内投影中心的垂直投影获得投影中心对应的图像坐标。According to the high-precision grasping method of the industrial mold intelligent manufacturing robot according to claim 1, it is characterized in that the process of calibrating the camera includes: converting the world coordinate system to the camera coordinate system, obtaining the imaging plane coordinate system based on the camera coordinate system, processing the imaging plane coordinate system based on the radial distortion value to obtain the corrected coordinates, and obtaining the image coordinates corresponding to the projection center based on the corrected coordinates, the image sensor pixel size and the vertical projection of the projection center in the imaging plane coordinate system.
  3. 根据权利要求1所述的工业模具智能制造机器人高精度抓取方法，其特征在于，获取机器人信息与目标点信息的过程包括：基于工业智能相机、预设定采集间隔及测速编码器获得机器人与目标工件的图像、图像采集时刻及机器人与目标工件位置，其中，所述工业智能相机与测速编码器安装在传送带上，所述工业智能相机为进行标定处理的相机。According to the high-precision grasping method of the industrial mold intelligent manufacturing robot according to claim 1, it is characterized in that the process of obtaining robot information and target point information includes: obtaining images of the robot and the target workpiece, the image acquisition time and the position of the robot and the target workpiece based on an industrial smart camera, a preset acquisition interval and a speed encoder, wherein the industrial smart camera and the speed encoder are installed on a conveyor belt, and the industrial smart camera is a camera for calibration processing.
  4. 根据权利要求3所述的工业模具智能制造机器人高精度抓取方法，其特征在于，基于相机视野与目标工件的最大长度获得所述采集间隔。The high-precision grasping method for an industrial mold intelligent manufacturing robot according to claim 3 is characterized in that the acquisition interval is obtained based on the camera field of view and the maximum length of the target workpiece.
  5. 根据权利要求1所述的工业模具智能制造机器人高精度抓取方法， 其特征在于，目标工件信息的获取过程还包括：分别构建相邻两幅图像的集合，通过集合中的元素两两对比的方式判断是否存在重复，其中，集合中的元素为图像的坐标信息。According to the high-precision grasping method of the industrial mold intelligent manufacturing robot according to claim 1, it is characterized in that the process of acquiring the target workpiece information also includes: constructing sets of two adjacent images respectively, and judging whether there is duplication by comparing the elements in the sets pairwise, wherein the elements in the sets are the coordinate information of the images.
  6. 根据权利要求1所述的工业模具智能制造机器人高精度抓取方法，其特征在于，获得抓取路径的过程包括：确定目标工件抓取点与目标工件控制点的坐标，获取在抬起预设高度时传送带上的过渡点，基于过渡半径获得其余经过点，基于路径平滑的原则与弧线过度拐角处的原则，结合获得的点获得抓取路径，其中，过渡半径依据转接点***过渡圆弧半径算法获得。According to the high-precision grasping method of the industrial mold intelligent manufacturing robot described in claim 1, it is characterized in that the process of obtaining the grasping path includes: determining the coordinates of the target workpiece grasping point and the target workpiece control point, obtaining the transition point on the conveyor belt when it is lifted to a preset height, obtaining the remaining passing points based on the transition radius, and obtaining the grasping path based on the principle of path smoothness and the principle of arc transition corners, combining the obtained points, wherein the transition radius is obtained according to the algorithm of inserting the transition arc radius into the transfer point.
  7. 根据权利要求1所述的工业模具智能制造机器人高精度抓取方法，其特征在于，获取抓取位置的过程包括：基于目标工件对应的位置、传送带速度，并采用二分法获得抓取位置。According to the high-precision grasping method of the industrial mold intelligent manufacturing robot according to claim 1, it is characterized in that the process of obtaining the grasping position includes: based on the position corresponding to the target workpiece and the conveyor belt speed, the grasping position is obtained by using a dichotomy method.
  8. 根据权利要求1所述的工业模具智能制造机器人高精度抓取方法，其特征在于，基于所述抓取轨迹、所述抓取位置及主从多技术设计自适应协调控制器模型。The high-precision grasping method of the industrial mold intelligent manufacturing robot according to claim 1 is characterized in that an adaptive coordination controller model is designed based on the grasping trajectory, the grasping position and the master-slave multi-technology.
PCT/CN2022/135360 2022-11-30 2022-11-30 High-precision grasping method of industrial mold intelligent manufacturing robot WO2024113216A1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/CN2022/135360 WO2024113216A1 (en) 2022-11-30 2022-11-30 High-precision grasping method of industrial mold intelligent manufacturing robot

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2022/135360 WO2024113216A1 (en) 2022-11-30 2022-11-30 High-precision grasping method of industrial mold intelligent manufacturing robot

Publications (1)

Publication Number Publication Date
WO2024113216A1 true WO2024113216A1 (en) 2024-06-06

Family

ID=91322608

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2022/135360 WO2024113216A1 (en) 2022-11-30 2022-11-30 High-precision grasping method of industrial mold intelligent manufacturing robot

Country Status (1)

Country Link
WO (1) WO2024113216A1 (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107618030A (en) * 2016-07-16 2018-01-23 深圳市得意自动化科技有限公司 The Robotic Dynamic tracking grasping means of view-based access control model and system
CN111775154A (en) * 2020-07-20 2020-10-16 广东拓斯达科技股份有限公司 Robot vision system
CN113379849A (en) * 2021-06-10 2021-09-10 南开大学 Robot autonomous recognition intelligent grabbing method and system based on depth camera
US20210308866A1 (en) * 2020-04-03 2021-10-07 Fanuc Corporation Adaptive grasp planning for bin picking
CN114299116A (en) * 2021-12-29 2022-04-08 伯朗特机器人股份有限公司 Dynamic object grabbing method, device and storage medium
CN114463244A (en) * 2020-11-09 2022-05-10 南京理工大学 Vision robot grabbing system and control method thereof

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107618030A (en) * 2016-07-16 2018-01-23 深圳市得意自动化科技有限公司 The Robotic Dynamic tracking grasping means of view-based access control model and system
US20210308866A1 (en) * 2020-04-03 2021-10-07 Fanuc Corporation Adaptive grasp planning for bin picking
CN111775154A (en) * 2020-07-20 2020-10-16 广东拓斯达科技股份有限公司 Robot vision system
CN114463244A (en) * 2020-11-09 2022-05-10 南京理工大学 Vision robot grabbing system and control method thereof
CN113379849A (en) * 2021-06-10 2021-09-10 南开大学 Robot autonomous recognition intelligent grabbing method and system based on depth camera
CN114299116A (en) * 2021-12-29 2022-04-08 伯朗特机器人股份有限公司 Dynamic object grabbing method, device and storage medium

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
XU-LIANG LIU: "Research on multi arm adaptive cooperative control method of intelligent manufacturing robot", MANUFACTURING AUTOMATION, vol. 44, no. 1, 25 January 2022 (2022-01-25), pages 110 - 113, XP093177068 *

Similar Documents

Publication Publication Date Title
US11562552B2 (en) Detecting boxes
CN110238849B (en) Robot hand-eye calibration method and device
CN110531707B (en) Friction model improvement and dynamic parameter identification method of SCARA robot
CN107214703B (en) Robot self-calibration method based on vision-assisted positioning
CN104552341B (en) Mobile industrial robot single-point various visual angles pocket watch position and attitude error detection method
CN111243017A (en) Intelligent robot grabbing method based on 3D vision
CN111267105A (en) Kinetic parameter identification and collision detection method for six-joint robot
CN110480615A (en) Robot de-stacking positioning correction method
CN104808490A (en) Uncalibrated visual servoing control method for estimating image Jacobian matrix based on echo state network facing mold protection
CN112621746A (en) PID control method with dead zone and mechanical arm visual servo grabbing system
CN114942593A (en) Mechanical arm self-adaptive sliding mode control method based on disturbance observer compensation
CN114378827B (en) Dynamic target tracking and grabbing method based on overall control of mobile mechanical arm
Heyn et al. Application of artificial neural networks in force-controlled automated assembly of complex shaped deformable components
WO2024113216A1 (en) High-precision grasping method of industrial mold intelligent manufacturing robot
CN112099505B (en) Low-complexity visual servo formation control method for mobile robot
Luo et al. Robotic conveyor tracking with dynamic object fetching for industrial automation
CN116214516A (en) Real-time grabbing and track tracking method and system for mobile redundant mechanical arm
JPH0588721A (en) Controller for articulated robot
CN108107882A (en) Service robot automatic Calibration and detecting system based on optical motion tracking
CN107443369A (en) A kind of robotic arm of the inverse identification of view-based access control model measurement model is without demarcation method of servo-controlling
CN112123329A (en) Robot 3D vision hand-eye calibration method
Song et al. Automatic docking system of fuel filler with CAD model-based tracking and visual servoing control
Qin et al. Sensor calibration and trajectory planning in 3D vision-guided robots
CN110940351A (en) Robot precision compensation method based on parameter dimension reduction identification
CN108406773A (en) A kind of 2R drive lacking planar manipulator control methods that energy consumption is minimum