scholarly journals Positioning error compensation of an industrial robot using neural networks and experimental study

2021 ◽  
Author(s):  
Bo Li ◽  
Wei Tian ◽  
Chufan Zhang ◽  
Fangfang Hua ◽  
Guangyu Cui ◽  
...  
Keyword(s):  
Neural Networks ◽  
Experimental Study ◽  
Error Compensation ◽  
Industrial Robot ◽  
Positioning Error

scholarly journals Influence of Drift on Robot Repeatability and Its Compensation

2021 ◽  
Vol 11 (22) ◽  
pp. 10813
Author(s):  
Michal Vocetka ◽  
Zdenko Bobovský ◽  
Jan Babjak ◽  
Jiří Suder ◽  
Stefan Grushko ◽  
...  
Keyword(s):  
Thermal Expansion ◽  
Error Compensation ◽  
Entire Range ◽  
Industrial Robot ◽  
Positioning Error ◽  
Absolute Accuracy ◽  
End Effector ◽  
Repeatability Error ◽  
Compensation Principle ◽  
The Impact

This paper presents an approach to compensate for the effect of thermal expansion on the structure of an industrial robot and thus to reduce the repeatability difference of the robot in cold and warm conditions. In contrast to previous research in this area that deals with absolute accuracy, this article is focused on determining achievable repeatability. To unify and to increase the robot repeatability, the measurements with highly accurate sensors were performed under different conditions on an industrial robot ABB IRB1200, which was equipped with thermal sensors, mounted on a pre-defined position around joints. The performed measurements allowed to implement a temperature-based prediction model of the end effector positioning error. Subsequent tests have shown that the implemented model used for the error compensation proved to be highly effective. Using the methodology presented in this article, the impact of drift can be reduced by up to 89.9%. A robot upgraded with a compensation principle described in this article does not have to be warmed up as it works with the same low repeatability error in the entire range of the achievable temperatures.

scholarly journals GPS Positioning Error Compensation Based on Kalman Filtering

2021 ◽  
Vol 1920 (1) ◽  
pp. 012088
Author(s):  
Wenjiu Zhu ◽  
Jie Hou ◽  
Zhengqiong Liu ◽  
Zhizhong Ding
Keyword(s):  
Kalman Filtering ◽  
Error Compensation ◽  
Positioning Error ◽  
Gps Positioning

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.

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.

Research on slipping-pattern recognition and positioning error compensation of wheeled robot

2021 ◽  
Vol 92 (8) ◽  
pp. 085107
Author(s):  
Yanchao Chen ◽  
Chunhua Ren ◽  
Hao Liu ◽  
Chenyang Gu
Keyword(s):  
Pattern Recognition ◽  
Error Compensation ◽  
Positioning Error ◽  
Wheeled Robot
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