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.

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.

Comparison of two calibration methods for a small industrial robot based on an optical CMM and a laser tracker

Robotica ◽  
2013 ◽  
Vol 32 (3) ◽  
pp. 447-466 ◽  
Author(s):  
Albert Nubiola ◽  
Mohamed Slamani ◽  
Ahmed Joubair ◽  
Ilian A. Bonev
Keyword(s):  
Industrial Robot ◽  
Laser Tracker ◽  
Calibration Model ◽  
Kinematic Calibration ◽  
Absolute Accuracy ◽  
Parameter Calibration ◽  
End Effector ◽  
Coordinate Measurement ◽  
Measurement Systems ◽  
Calibration Methods

SUMMARYThe absolute accuracy of a small industrial robot is improved using a 30-parameter calibration model. The error model takes into account a full kinematic calibration and five compliance parameters related to the stiffness in joints 2, 3, 4, 5, and 6. The linearization of the Jacobian is performed to iteratively find the modeled error parameters. Two coordinate measurement systems are used independently: a laser tracker and an optical CMM. An optimized end-effector is developed specifically for each measurement system. The robot is calibrated using fewer than 50 configurations and the calibration efficiency validated in 1000 configurations using either the laser tracker or the optical CMM. A telescopic ballbar is also used for validation. The results show that the optical CMM yields slightly better results, even when used with the simple triangular plate end-effector that was developed mainly for the laser tracker.

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.

Industrial Robot Error Compensation Using Laser Tracker System

2008 ◽  
Vol 381-382 ◽  
pp. 579-582
Author(s):  
Jian Fei Ouyang ◽  
W. Liu ◽  
Xing Hua Qu ◽  
Y. Yan
Keyword(s):  
Real Time ◽  
Error Compensation ◽  
Industrial Robot ◽  
Laser Tracker ◽  
Geometric Errors ◽  
End Effector ◽  
Geometry Error

A technique to compensate the geometry errors of industrial robot using Laser Tracker System (LTS) has been presented in this paper. A Spherically Mounted Retro-reflector (SMR) is mounted on the end-effector of industrial robot. The positions of SMR are measured by LTS and compared with the nominal value of industrial robot to get geometry error database of robot. The updated error database, together with real-time measuring of the positions on the robot’s end-effector can be used to compensate the geometric errors of the robot. Using this technique to compensate the industrial robot, the geometry errors can be decreased from 0.1mm to 0.04mm.

Design of spline surface vacuum gripper for pick and place robotic arms

2020 ◽  
Vol 4 (2) ◽  
pp. 48-55
Author(s):  
A. S. Jamaludin ◽  
M. N. M. Razali ◽  
N. Jasman ◽  
A. N. A. Ghafar ◽  
M. A. Hadi
Keyword(s):  
Cycle Time ◽  
Industrial Robot ◽  
Annealing Process ◽  
Vacuum Suction ◽  
Robotic Arms ◽  
Custom Made ◽  
Pick And Place ◽  
Manufacturing Procedure ◽  
Production Flexibility ◽  
The Impact

The gripper is the most important part in an industrial robot. It is related with the environment around the robot. Today, the industrial robot grippers have to be tuned and custom made for each application by engineers, by searching to get the desired repeatability and behaviour. Vacuum suction is one of the grippers in Watch Case Press Production (WCPP) and a mechanism to improve the efficiency of the manufacturing procedure. Pick and place are the important process for the annealing process. Thus, by implementing vacuum suction gripper, the process of pick and place can be improved. The purpose of vacuum gripper other than design vacuum suction mechanism is to compare the effectiveness of vacuum suction gripper with the conventional pick and place gripper. Vacuum suction gripper is a mechanism to transport part and which later sequencing, eliminating and reducing the activities required to complete the process. Throughout this study, the process pick and place became more effective, the impact on the production of annealing process is faster. The vacuum suction gripper can pick all part at the production which will lower the loss of the productivity. In conclusion, vacuum suction gripper reduces the cycle time about 20%. Vacuum suction gripper can help lower the cycle time of a machine and allow more frequent process in order to increase the production flexibility.

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 Unravelling thermal stress due to thermal expansion mismatch in metal–organic frameworks for methane storage

2021 ◽  
Author(s):  
Jelle Wieme ◽  
Veronique Van Speybroeck
Keyword(s):  
Thermal Expansion ◽  
Thermal Stress ◽  
Pressure Coefficient ◽  
Metal Organic Frameworks ◽  
Methane Storage ◽  
Thermal Expansion Mismatch ◽  
Thermal Pressure ◽  
Temperature Changes ◽  
Metal Organic ◽  
The Impact

Thermal stress is present in metal–organic frameworks undergoing temperature changes during adsorption and desorption. We computed the thermal pressure coefficient as a proxy for this phenomenon and discuss the impact of thermal expansion mismatch.

Sign in / Sign up

Export Citation Format

Share Document