scholarly journals Research of Calibration Method for Industrial Robot Based on Error Model of Position

2021 ◽  
Vol 11 (3) ◽  
pp. 1287
Author(s):  
Tianyan Chen ◽  
Jinsong Lin ◽  
Deyu Wu ◽  
Haibin Wu
Keyword(s):  
Coordinate System ◽  
Industrial Robot ◽  
Calibration Method ◽  
Error Model ◽  
Position Error ◽  
Industrial Robots ◽  
Positioning Error ◽  
Robot Position ◽  
General Measurement ◽  
Parameter Error

Based on the current situation of high precision and comparatively low APA (absolute positioning accuracy) in industrial robots, a calibration method to enhance the APA of industrial robots is proposed. In view of the "hidden" characteristics of the RBCS (robot base coordinate system) and the FCS (flange coordinate system) in the measurement process, a comparatively general measurement and calibration method of the RBCS and the FCS is proposed, and the source of the robot terminal position error is classified into three aspects: positioning error of industrial RBCS, kinematics parameter error of manipulator, and positioning error of industrial robot end FCS. The robot position error model is established, and the relation equation of the robot end position error and the industrial robot model parameter error is deduced. By solving the equation, the parameter error identification and the supplementary results are obtained, and the method of compensating the error by using the robot joint angle is realized. The Leica laser tracker is used to verify the calibration method on ABB IRB120 industrial robot. The experimental results show that the calibration method can effectively enhance the APA of the robot.

scholarly journals Dynamic Simulation Modeling of Industrial Robot Kinematics in Industry 4.0

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Guang Jin ◽  
Shuai Ma ◽  
Zhenghui Li
Keyword(s):  
Dynamic Simulation ◽  
Trajectory Planning ◽  
Simulation Modeling ◽  
Industry 4.0 ◽  
Industrial Robot ◽  
Error Model ◽  
Position Error ◽  
Industrial Robots ◽  
Robot Kinematics ◽  
Kinetic Characteristics

This paper studies the kinematic dynamic simulation modeling of industrial robots in the Industry 4.0 environment and guides the kinematic dynamic simulation modeling of industrial robots in the Industry 4.0 environment in the context of the research. To address the problem that each parameter error has different degrees of influence on the end position error, a method is proposed to calculate the influence weight of each parameter error on the end position error based on the MD-H error model. The error model is established based on the MD-H method and the principle of differential transformation, and then the function of uniform variation of six joint angles with time t is constructed to ensure that each linkage geometric parameter is involved in the motion causing error accumulation. Through the analysis of the robot marking process, the inverse solution is optimized for multiple solutions, and a unique engineering solution is obtained. Linear interpolation, parabolic interpolation, polynomial interpolation, and spline curve interpolation are performed on the results after multisolution optimization in the joint angle, and the pros and cons of various interpolation results are analyzed. The trajectory planning and simulation of industrial robots in the Industry 4.0 environment are carried out by using a special toolbox. The advantages and disadvantages of the two planning methods are compared, and the joint space trajectory planning method is selected to study the planning of its third and fifth polynomials. The kinetic characteristics of the robot were simulated and tested by experimental methods, and the reliability of the simulation results of the kinetic characteristics was verified. The kinematic solutions of industrial robots and the results of multisolution optimization are simulated. The methods, theories, and strategies studied in this paper are slightly modified to provide theoretical and practical support for another dynamic simulation modeling of industrial robot kinematics with various geometries.

A calibration method of kinematic parameters for serial industrial robots

2014 ◽  
Vol 41 (2) ◽  
pp. 157-165 ◽  
Author(s):  
Wei Wang ◽  
Gang Wang ◽  
Chao Yun
Keyword(s):  
Calibration Method ◽  
Error Model ◽  
Industrial Robots ◽  
Reference Configuration ◽  
Calibration Model ◽  
Positioning Error ◽  
Kinematic Parameters ◽  
Rigid Displacement ◽  
Content Type ◽  
Zero Position

Purpose – Calibrating kinematic parameters is one of the efficient ways to improve the robot's positioning accuracy. A method based on the product-of-exponential (POE) formula to calibrate the kinematic parameters of serial industrial robots is proposed. The paper aims to discuss these issues. Design/methodology/approach – The forward kinematics is established, and the general positioning error model is deduced in an explicit expression. A simplified model of robot's positioning error is established as both the error of reference configuration and the error of rigid displacement of the base coordinating system with respect to the measuring coordinating system are equivalently transferred to the zero position errors of the robot's joints. A practical calibration model is forwarded only requiring 3D measuring based on least-squares algorithm. The calibration system and strategy for calibrating kinematic parameters are designed. Findings – By the two geometrical constrains between the twist coordinates, each joint twist only has four independent coordinates. Due to the equivalent error model, the zero position error of each joint can cover the error of reference configuration and rigid displacement of the robot base coordinating system with respect to the measuring coordinating system. The appropriate number of independent kinematic parameters of each joint to be calibrated is five. Originality/value – It is proved by a group of calibration experiments that the calibration method is well conditioned and can be used to promote the level of absolute error of end effector of industrial robot to 2.2 mm.

A flexible calibration method connecting the joint space and the working space of industrial robots

2018 ◽  
Vol 45 (3) ◽  
pp. 407-415 ◽  
Author(s):  
Ying Cai ◽  
Peijiang Yuan ◽  
Dongdong Chen
Keyword(s):  
Industrial Robot ◽  
Calibration Method ◽  
Joint Space ◽  
Position Error ◽  
Industrial Robots ◽  
Content Type ◽  
Position Errors ◽  
Working Space ◽  
Calibration Experiment ◽  
Control Command

Purpose To improve the accuracy of the industrial robots’ absolute positioning, a Kriging calibration is proposed. Design/methodology/approach This method particularly designs a semivariogram for connecting the joint space and the working space. After that, Kriging equations are determined and solved to predict the position errors of targets. Subsequently, a simple and convenient error compensation, which can be implemented on the control command, is proposed. Findings The verification experiment of the position-error multiplicity and the Kriging calibration experiment are done in the KUKA R210 R2700 industrial robot. The position-error multiplicity experiment reveals that the position error of the industrial robot varies with the joint angle sets. Besides, the Kriging calibration experiment shows that the maximum of the spatial position errors is reduced from 1.2906 to 0.2484 mm, which reveals the validity of the Kriging calibration. Originality/value The special designed semivariation allows this method to be flexible and practical. It can be used in various fields where the angle solutions of industrial robots should be adapted according to the optimal demand and the environment, such as the optimal trajectory planning and the obstacle avoidance. Besides, this method can provide accuracy positioning results.

Finding Features of Positioning Error for Large Industrial Robots Based on Convolutional Neural Network

2021 ◽  
Author(s):  
Daiki Kato ◽  
Kenya Yoshitugu ◽  
Naoki Maeda ◽  
Toshiki Hirogaki ◽  
Eiichi Aoyama ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Production Systems ◽  
Industrial Robot ◽  
Three Dimensional ◽  
Industrial Robots ◽  
Numerical Control ◽  
Robot Motion ◽  
Positioning Error ◽  
End Effector

Abstract Most industrial robots are taught using the teaching playback method; therefore, they are unsuitable for use in variable production systems. Although offline teaching methods have been developed, they have not been practiced because of the low accuracy of the position and posture of the end-effector. Therefore, many studies have attempted to calibrate the position and posture but have not reached a practical level, as such methods consider the joint angle when the robot is stationary rather than the features during robot motion. Currently, it is easy to obtain servo information under numerical control operations owing to the Internet of Things technologies. In this study, we propose a method for obtaining servo information during robot motion and converting it into images to find features using a convolutional neural network (CNN). Herein, a large industrial robot was used. The three-dimensional coordinates of the end-effector were obtained using a laser tracker. The positioning error of the robot was accurately learned by the CNN. We extracted the features of the points where the positioning error was extremely large. By extracting the features of the X-axis positioning error using the CNN, the joint 1 current is a feature. This indicates that the vibration current in joint 1 is a factor in the X-axis positioning error.

scholarly journals Compensation for absolute positioning error of industrial robot considering the optimized measurement space

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142092164
Author(s):  
Junde Qi ◽  
Bing Chen ◽  
Dinghua Zhang
Keyword(s):  
Industrial Robot ◽  
Measurement Data ◽  
Joint Space ◽  
Industrial Robots ◽  
Positioning Error ◽  
Positioning Accuracy ◽  
Decoupling Method ◽  
Absolute Positioning ◽  
Motion Characteristics ◽  
High Flexibility

Industrial robots are getting widely applied due to their low use-cost and high flexibility. However, the low absolute positioning accuracy limits their expansion in the area of high-precision manufacturing. Aiming to improve the positioning accuracy, a compensation method for the positioning error is put forward in terms of the optimization of the experimental measurement space and accurate modelling of the positioning error. Firstly, the influence of robot kinematic performance on the measurement accuracy is analysed, and a quantitative index describing the performance is adopted. On this basis and combined with the joints motion characteristics, the optimized measurement space in joint space as well as Cartesian space is obtained respectively, which can provide accurate measurement data to the error model. Then the overall model of the positioning error is constructed based on modified Denavit–Hartenberg method, in which the geometric errors and compliance errors are considered comprehensively, and an error decoupling method between them is carried out based on the error-feature analyses. Experiments on the KUKA KR210 robot are carried out finally. The mean absolute positioning accuracy of the robot increases from 1.179 mm to 0.093 mm, which verifies the effectiveness of the compensation methodology in this article.

The Method of the Computer Vision System Coordinate Transformation for an Industrial Robot for a Laser Welding Operation

2020 ◽  
Vol 21 (3) ◽  
pp. 166-173
Author(s):  
A. Y. Polivanov ◽  
Y. V. Ivanov ◽  
D. V. Kholin
Keyword(s):  
Computer Vision ◽  
Laser Welding ◽  
Coordinate System ◽  
Coordinate Transformation ◽  
Industrial Robot ◽  
Three Dimensional ◽  
Industrial Robots ◽  
Main Task ◽  
Welding Joint ◽  
Video Sensor

In this article, the authors consider the problem of coordinate transformation in computer vision systems (CVS) of robotic system (RS) for laser welding. Laser welding is a highly efficient technological operation in many respects superior to common types of welding due to the high concentration of energy at the welding point. However, laser welding has a number of requirements, including a high requirement for the accuracy of positioning the laser head relative to the welding joint. Adaptive control systems based on CVS allow to provide the required accuracy. The main task of CVS is to determine the three-dimensional coordinates of the welding joint using a video sensor, convert the received coordinates into a coordinate system in which the RS is controlled, and the converted coordinates are transferred to the control system. Note, the accuracy and determination of coordinates are important factors. To accomplish this task, it is necessary to consider the coordinate transformation as a set of actions performed taking into account the specifics of using CVS as part of an RS for laser welding. For this purpose, the article analyzes typical schemes for placing CVS on industrial robots and proposes the most suitable configuration for laser welding. A methodology was also developed for measuring the three-dimensional coordinates of the welding joint using the triangulation method. The authors carried out a comparative analysis of the main existing methods for calibrating CVS video sensors and proposed an original method for calibrating videosensors taking into account the specifics of the functioning of the RS for laser welding. As a result, the article presents the rationale for the need to consider coordinate conversion to CVS as part of an RS for laser welding, as well as a set of methods that allows to perform conversions from a virtual coordinate system of a video sensor to a coordinate system of a robot, which allows direct control based on CVS data. In conclusion, the authors give a method for calibrating a video sensor, which allows achieving the requirements specified in the article for the accuracy of determining the coordinates of the welding joint.

scholarly journals Predicting Positioning Error and Finding Features for Large Industrial Robots Based on Deep Learning

2021 ◽  
Vol 15 (2) ◽  
pp. 206-214
Author(s):  
Daiki Kato ◽  
Kenya Yoshitsugu ◽  
Toshiki Hirogaki ◽  
Eiichi Aoyama ◽  
Kenichi Takahashi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Computer Aided Design ◽  
Joint Angle ◽  
Industrial Robot ◽  
Movement Time ◽  
Vertical Axis ◽  
Industrial Robots ◽  
Positioning Error ◽  
Design Data ◽  
End Effector

In this study, we evaluated the motion accuracy of a large industrial robot and its compensation method and constructed an off-line teaching operation based on three-dimensional computer aided design data. In this experiment, we used a laser tracker to measure the coordinates of the end effector of the robot. Simultaneously, the end-effector coordinates, each joint angle, the maximum current of the motors attached to each joint, and rotation speed of each joint were measured. This servo information was converted into image data as visible information. For each robot movement path, an image was created; the horizontal axis represented the movement time of the robot and the vertical axis represented the servo information. A convolutional neural network (CNN), a type of deep learning, was used to predict the positioning error with high accuracy. Subsequently, to identify the features of the positioning error, the image was divided into several analysis areas, one of which was filled with various colors and analyzed by the CNN. If the prediction accuracy of the CNN decreased, then the analysis area would be identified as a feature. Thus, the features of the Y-axis positioning error were observed for teaching each joint angle in the opposite direction just after the start of the motion, overshoot of the rotational joint current, and the change in the swivel joint current.

scholarly journals Positioning Error Calibration of Industrial Robots Based on Random Forest

2021 ◽  
Vol 15 (5) ◽  
pp. 581-589
Author(s):  
Daiki Kato ◽  
Kenya Yoshitsugu ◽  
Naoki Maeda ◽  
Toshiki Hirogaki ◽  
Eiichi Aoyama ◽  
...  
Keyword(s):  
Random Forest ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Target Point ◽  
Calibration Model ◽  
Positioning Error ◽  
End Effector ◽  
Positioning Errors ◽  
Random Forest Method ◽  
Kinematic Errors

Because most industrial robots are taught using the direct teaching and playback method, they are unsuitable for variable production systems. Alternatively, the offline teaching method has limited applications because of the low accuracy of the position and posture of the end-effector. Therefore, many studies have been conducted to calibrate the position and posture. Positioning errors of robots can be divided into kinematic and non-kinematic errors. In some studies, kinematic errors are calibrated by kinematic models, and non-kinematic errors are calibrated by neural networks. However, the factor of the positioning errors has not been identified because the neural network is a black box. In another machine learning method, a random forest is constructed from decision trees, and its structure can be visualized. Therefore, we used a random forest method to construct a calibration model for the positioning errors and to identify the positioning error factors. The proposed calibration method is based on a simulation of many candidate points centered on the target point. A large industrial robot was used, and the 3D coordinates of the end-effector were obtained using a laser tracker. The model predicted the positioning error from end-effector coordinates, joint angles, and joint torques using the random forest method. As a result, the positioning error was predicted with a high accuracy. The random forest analysis showed that joint 2 was the primary factor of the X- and Z-axis errors. This suggests that the air cylinder used as an auxiliary to the servo motor of joint 2, which is unique to large industrial robots, is the error factor. With the proposed calibration, the positioning error norm was reduced at all points.

scholarly journals New Method and Portable Measurement Device for the Calibration of Industrial Robots

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5919
Author(s):  
Caglar Icli ◽  
Oleksandr Stepanenko ◽  
Ilian Bonev
Keyword(s):  
Industrial Robot ◽  
Calibration Method ◽  
Industrial Robots ◽  
Laser Tracker ◽  
Calibration Model ◽  
Measuring Device ◽  
End Effector ◽  
New Device ◽  
Position Errors ◽  
Measuring Machine

This paper presents an automated calibration method for industrial robots, based on the use of (1) a novel, low-cost, wireless, 3D measuring device mounted on the robot end-effector and (2) a portable 3D ball artifact fixed with respect to the robot base. The new device, called TriCal, is essentially a fixture holding three digital indicators (plunger style), the axes of which are orthogonal and intersect at one point, considered to be the robot tool center point (TCP). The artifact contains four 1-inch datum balls, each mounted on a stem, with precisely known relative positions measured on a Coordinate Measuring Machine (CMM). The measurement procedure with the TriCal is fully automated and consists of the robot moving its end-effector in such as a way as to perfectly align its TCP with the center of each of the four datum balls, with multiple end-effector orientations. The calibration method and hardware were tested on a six-axis industrial robot (KUKA KR6 R700 sixx). The calibration model included all kinematic and joint stiffness parameters, which were identified using the least-squares method. The efficiency of the new calibration system was validated by measuring the accuracy of the robot after calibration in 500 nearly random end-effector poses using a laser tracker. The same validation was performed after the robot was calibrated using measurements from the laser tracker only. Results show that both measurement methods lead to similar accuracy improvements, with the TriCal yielding maximum position errors of 0.624 mm and mean position errors of 0.326 mm.

scholarly journals Kinematic Calibration of Industrial Robots Based on Distance Information Using a Hybrid Identification Method

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Guanbin Gao ◽  
Yuan Li ◽  
Fei Liu ◽  
Shichang Han
Keyword(s):  
Industrial Robot ◽  
Kinematic Model ◽  
Error Model ◽  
Industrial Robots ◽  
Kinematic Calibration ◽  
Kinematic Parameters ◽  
Distance Information ◽  
End Effector ◽  
Hybrid Identification ◽  
Identification Method

To improve the positioning accuracy of industrial robots and avoid using the coordinates of the end effector, a novel kinematic calibration method based on the distance information is proposed. The kinematic model of an industrial robot is established. The relationship between the moving distance of the end effector and the kinematic parameters is analyzed. Based on the results of the analysis and the kinematic model of the robot, the error model with displacements as the reference is built, which is linearized for the convenience of the following identification. The singular value decomposition (SVD) is used to eliminate the redundant parameters of the error model. To solve the problem that traditional optimization algorithms are easily affected by data noise in high dimension identification, a novel extended Kalman filter (EKF) and regularized particle filter (RPF) hybrid identification method is presented. EKF is used in the preidentification of the linearized error model. With the preidentification results as the initial parameters, RPF is used to identify the kinematic parameters of the linearized error model. Simulations are carried out to validate the effectiveness of the proposed method, which shows that the method can identify the error of the parameters and after compensation the accuracy of the robot is improved.

Sign in / Sign up

Export Citation Format

Share Document