The Parameter Identification and Error Compensation of Robot Based on Dynacal System

2014 ◽  
Vol 701-702 ◽  
pp. 788-792 ◽  
Author(s):  
Fei Qi ◽  
Xue Liang Ping ◽  
Jie Liu ◽  
Yi Jiang
Keyword(s):  
Parameter Identification ◽  
Error Compensation ◽  
Compensation Method ◽  
Positioning Accuracy ◽  
Mean Error ◽  
Robot Positioning ◽  
The Mean

According to the robot positioning accuracy, this paper proposed an error compensation method after updating the controller parameters based on the D-H parameters model and Dynacal system. The proposed method is effectiveand was validated on the developed robot of which the mean error was reduced to 0.092mm. The method can greatly improve the positioning accuracy of the robot.

An Immediate Error Compensation Method for PCB Alignment Based on Image Measurement

2014 ◽  
Vol 602-605 ◽  
pp. 1693-1697
Author(s):  
Qi Zhang ◽  
Hong Lin Ma ◽  
Yong Ting Zhao ◽  
Jie Yang ◽  
Bin Zheng
Keyword(s):  
Error Compensation ◽  
Compensation Method ◽  
Alignment Method ◽  
Image Measurement ◽  
Motion Direction ◽  
Positioning Accuracy ◽  
Experimental Prototype ◽  
Initial Design

The parallelism between an industrial camera and a servo motion direction is corrected with the help of image measurement to shadowed geometric contours. Then a nearly orthogonal angle between XY servo motion directions is obtained according to an inherent geometry relationship in contours. The installation error of a PCB in platform is compensated based on automatic multi-spot imaging finally. An experimental prototype was built while the PCB alignment was implemented on a lot of samples according to the method introduced above. It proves that the developed immediate alignment method as well as its specific embodiment fulfills the requirement of positioning accuracy in the initial design.

scholarly journals Positioning Error Compensation for Industrial Robots Based on Stiffness Modelling

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yingjie Li ◽  
Guanbin Gao ◽  
Fei Liu
Keyword(s):  
Error Compensation ◽  
Compensation Method ◽  
Industrial Robots ◽  
Radial Deformation ◽  
Positioning Error ◽  
Axial Deformation ◽  
Element Analysis ◽  
Positioning Accuracy ◽  
Positioning Errors ◽  
Deformation Error

Insufficient stiffness of industrial robots is a significant factor which affects its positioning accuracy. To improve the positioning accuracy, a novel positioning error compensation method based on the stiffness modelling is proposed in this paper. First, the positioning errors considering the end load and gravity of industrial robots due to stiffness are analyzed. Based on the results of analysis, it is found that the positioning errors can be described by two kinds of deformation errors at joints: the axial deformation error and the radial deformation error. Then, the axial deformation error is modelled by the differential relationship of kinematics equations. The model of radial deformation error is deduced through the recurrence method and rotation transformation between joints. Finally, these two models are transformed into a Cartesian coordinate system, and a positioning error compensation method based on these two models is presented. Simulations based on the finite element analysis are implemented to verify the positioning error compensation method. The results show that the suggested method can efficiently predict the positioning error according to the gravity and loads, so that the positioning accuracy of industrial robots can be improved with the proposed method.

Study of error compensations and sensitivity analysis for 6-Dof serial robot

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xi Luo ◽  
Yingjie Zhang ◽  
Lin Zhang
Keyword(s):  
Sensitivity Analysis ◽  
Error Compensation ◽  
Error Model ◽  
Error Modeling ◽  
Compensation Method ◽  
Joint Clearance ◽  
Positioning Accuracy ◽  
Content Type ◽  
Model Based ◽  
Serial Robot

Purpose The purpose of this paper is to improve the positioning accuracy of 6-Dof serial robot by the way of error compensation and sensitivity analysis. Design/methodology/approach In this paper, the Denavit–Hartenberg matrix is used to construct the kinematics models of the robot; the effects from individual joint and several joints on the end effector are estimated by simulation. Then, an error model based on joint clearance is proposed so that the positioning accuracy at any position of joints can be predicted for compensation. Through the simulation of the curve path, the validity of the error compensation model is verified. Finally, the experimental results show that the error compensation method can improve the positioning accuracy of a two joint exoskeleton robot by nearly 76.46%. Findings Through the analysis of joint error sensitivity, it is found that the first three joints, especially joint 2, contribute a lot to the positioning accuracy of the robot, which provides guidance for the accuracy allocation of the robot. In addition, this paper creatively puts forward the error model based on joint clearance, and the error compensation method which decouples the positioning accuracy into joint errors. Originality/value It provides a new idea for error modeling and error compensation of 6-Dof serial robot. Combining sensitivity analysis results with error compensation can effectively improve the positioning accuracy of the robot, and provide convenience for welding robot and other robots that need high positioning accuracy.

The Installation Eccentricity Error Compensation Technology for Gear Based on CNC Gear Measuring Center

2011 ◽  
Vol 189-193 ◽  
pp. 4116-4120
Author(s):  
Lian Fu Han ◽  
Qiang Ma ◽  
Wen Yan Tang ◽  
Chang Feng Fu
Keyword(s):  
Parameter Identification ◽  
Error Compensation ◽  
Measurement Range ◽  
Compensation Method ◽  
Error Evaluation ◽  
Compensation Model ◽  
Identification Method ◽  
Compensation Algorithm ◽  
Involute Gear ◽  
Eccentricity Error

In order to decrease the influence of installation eccentricity on gear error evaluation and to raise its accuracy, a compensation algorithm for installation eccentricity is proposed and a compensation model of installation eccentricity is established. A new installation eccentricity parameter identification method determined with involute equations is also raised. Installation Eccentricity parameters are gained through measuring points on involute profile with the method of unconstrained parameter identification. Taking involute profile evaluation as an example, the eccentricity compensation model is introduced into the error evaluation of CNC Gear Measuring Center. The experiment shows that the compensation model raised in this paper can compensate for evaluation results of the involute gear whose eccentricity value is within 2/3 of the total measurement range of the probe and lead it into the non-eccentricity condition, and the most installation eccentricity value that can be compensated in the experiment is 300 μm. The proposition of this compensation method not only improves the accuracy of gear evaluation, but also lowers the requirements on installation accuracy of the gear.

scholarly journals An ANN-Based Precision Compensation Method for Industrial Manipulators via Optimization of Point Selection

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhirong Wang ◽  
Zhangwei Chen ◽  
Chentao Mao ◽  
Xiang Zhang
Keyword(s):  
Error Compensation ◽  
Random Sequence ◽  
Joint Stiffness ◽  
Compensation Method ◽  
Training Data ◽  
Point Selection ◽  
Positioning Accuracy ◽  
Absolute Positioning ◽  
Large Joint ◽  
Industrial Manipulators

Industrial manipulators are widely used in the manufacture of products due to their high flexibility and low costs. High absolute positioning accuracy is the key to guarantee the product quality, which is commonly improved through the error compensation technology. Due to the variety, complexity, and unpredictability of the error sources, the influence of the nongeometric errors on the absolute positioning accuracy of manipulators is uncertain. In result, the existing error compensation methods are difficult to obtain satisfying results, especially for manipulators with large joint flexibility that need to work in different task scenarios. In this paper, an artificial neural network- (ANN-) based precision compensation method via optimization of point selection is proposed, which deals with the kinematic errors and joint stiffness errors in different task scenarios. Firstly, the quasi-random sequence (QRS) method and the product of exponentials (POE) model are combined to identify and compensate the geometric parameters. The QRS method can select points evenly in the workspace. And the POE model can avoid the singularity problem of Denavit–Hartenberg (DH) model. Secondly, a continuous joint stiffness compensation model in the whole workspace is established through ANN. In order to get better compensation results for the current task scenario, the point selection method based on trajectory similarity is adopted to determine the training data of ANN. Finally, the experiments are conducted on a 6-DOF industrial manipulator to demonstrate the validity of the proposed method. The results show that the ANN-based method via optimization of point selection could be an effective solution for the precision compensation.

Measurements of the excess volume of benzene-methanol, benzene-acetonitrile and methanol-acetonitrile mixtures by a vibrating-tube densimeter

1979 ◽  
Vol 44 (2) ◽  
pp. 295-306 ◽  
Author(s):  
Ivan Cibulka ◽  
Vladimír Hynek ◽  
Robert Holub ◽  
Jiří Pick
Keyword(s):  
Excess Volume ◽  
Density Measurement ◽  
Indirect Measurement ◽  
Measuring System ◽  
Mean Error ◽  
Shaped Tube ◽  
Underlying Principle ◽  
The Mean ◽  
Vibrating Tube Densimeter

A digital vibrating-tube densimeter was constructed for measuring the density of liquids at several temperatures. The underlying principle of the apparatus is the measurement of the period of eigen-vibrations of a V-shaped tube; the second power of the period of the vibrations is proportional to the density of the liquid in the tube. The temperature of the measuring system is controlled by an electronic regulator. The mean error in the density measurement is approximately ±1 . 10-5 g cm-3 at 25 °C and ±2 . 10-5 g cm-3 at 40 °C. The apparatus was used for an indirect measurement of the excess volume, tested with the benzene-cyclohexane system and further used for determining the excess volume of the benzene-methanol, benzene-acetonitrile and methanol-acetonitrile systems at 25 and 40 °C.

scholarly journals New Methodology for Computing the Aircraft’s Position Based on the PPP Method in GPS and GLONASS Systems

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2525
Author(s):  
Kamil Krasuski ◽  
Damian Wierzbicki
Keyword(s):  
Linear Combination ◽  
Research Method ◽  
High Efficiency ◽  
Satellite System ◽  
Weighting Scheme ◽  
Measurement Technology ◽  
Dual Frequency ◽  
Weighted Mean ◽  
Mean Error ◽  
The Mean

In the field of air navigation, there is a constant pursuit for new navigation solutions for precise GNSS (Global Navigation Satellite System) positioning of aircraft. This study aims to present the results of research on the development of a new method for improving the performance of PPP (Precise Point Positioning) positioning in the GPS (Global Positioning System) and GLONASS (Globalnaja Nawigacionnaja Sputnikovaya Sistema) systems for air navigation. The research method is based on a linear combination of individual position solutions from the GPS and GLONASS systems. The paper shows a computational scheme based on the linear combination for geocentric XYZ coordinates of an aircraft. The algorithm of the new research method uses the weighted mean method to determine the resultant aircraft position. The research method was tested on GPS and GLONASS kinematic data from an airborne experiment carried out with a Seneca Piper PA34-200T aircraft at the Mielec airport. A dual-frequency dual-system GPS/GLONASS receiver was placed on-board the plane, which made it possible to record GNSS observations, which were then used to calculate the aircraft’s position in CSRS-PPP software. The calculated XYZ position coordinates from the CSRS-PPP software were then used in the weighted mean model’s developed optimization algorithm. The measurement weights are a function of the number of GPS and GLONASS satellites and the inverse of the mean error square. The obtained coordinates of aircraft from the research model were verified with the RTK-OTF solution. As a result of the research, the presented solution’s accuracy is better by 11–87% for the model with a weighting scheme as a function of the inverse of the mean error square. Moreover, using the XYZ position from the RTKLIB program, the research method’s accuracy increases from 45% to 82% for the model with a weighting scheme as a function of the inverse of the square of mean error. The developed method demonstrates high efficiency for improving the performance of GPS and GLONASS solutions for the PPP measurement technology in air navigation.

scholarly journals Improvement of Heavy Load Robot Positioning Accuracy by Combining a Model-Based Identification for Geometric Parameters and an Optimized Neural Network for the Compensation of Nongeometric Errors

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yuxiang Wang ◽  
Zhangwei Chen ◽  
Hongfei Zu ◽  
Xiang Zhang ◽  
Chentao Mao ◽  
...  
Keyword(s):  
Neural Network ◽  
Manufacturing Systems ◽  
Robot Manipulator ◽  
Joint Stiffness ◽  
Calibration Method ◽  
Geometric Parameters ◽  
Heavy Load ◽  
Positioning Accuracy ◽  
Robot Positioning ◽  
Residual Errors

The positioning accuracy of a robot is of great significance in advanced robotic manufacturing systems. This paper proposes a novel calibration method for improving robot positioning accuracy. First of all, geometric parameters are identified on the basis of the product of exponentials (POE) formula. The errors of the reduction ratio and the coupling ratio are identified at the same time. Then, joint stiffness identification is carried out by adding a load to the end-effector. Finally, residual errors caused by nongeometric parameters are compensated by a multilayer perceptron neural network (MLPNN) based on beetle swarm optimization algorithm. The calibration is implemented on a SIASUN SR210D robot manipulator. Results show that the proposed method possesses better performance in terms of faster convergence and higher precision.

scholarly journals Assessment of necessity of neuronavigation in localization of calvarial extra-axial lesions in the setting of limited resources

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Hussein Soffar ◽  
Mohamed F. Alsawy
Keyword(s):  
Conventional Method ◽  
Linear Measurement ◽  
Limited Resources ◽  
Linear Measurements ◽  
Surgical Interventions ◽  
Localization Method ◽  
Mean Error ◽  
Significant Difference ◽  
The Mean ◽  
Mean Time

Abstract Background Neuronavigation is a very beneficial tool in modern neurosurgical practice. However, the neuronavigation is not available in most of the hospitals in our country raising the question about its importance in localizing the calvarial extra-axial lesions and to what extent it is safe to operate without it. Methods We studied twenty patients with calvarial extra-axial lesions who underwent surgical interventions. All lesions were preoperatively located with both neuronavigation and the usual linear measurements. Both methods were compared regarding the time consumed to localize the tumor and the accuracy of each method to anticipate the actual center of the tumor. Results The mean error of distance between the planned center of the tumor and the actual was 6.50 ± 1.762 mm in conventional method, whereas the error was 3.85 ± 1.309 mm in IGS method. Much more time was consumed during the neuronavigation method including booting, registration, and positioning. A statistically significant difference was found between the mean time passed in the conventional method and IGS method (2.05 ± 0.826, 24.90 ± 1.334, respectively), P-value < 0.001. Conclusion In the setting of limited resources, the linear measurement localization method seems to have an accepted accuracy in the localization of calvarial extra-axial lesions and it saves more time than neuronavigation method.

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