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.

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.

A calibration method for enhancing robot accuracy through integration of kinematic model and spatial interpolation algorithm

2021 ◽  
pp. 1-27
Author(s):  
Junde Qi ◽  
Bing Chen ◽  
Dinghua Zhang
Keyword(s):  
Spatial Interpolation ◽  
Residual Error ◽  
Compensation Method ◽  
Industrial Robots ◽  
Interpolation Algorithm ◽  
Random Errors ◽  
Spatial Error ◽  
Positioning Accuracy ◽  
Absolute Positioning ◽  
The Impact

Abstract Industrial robots are finding their niche in the field of machining due to their advantages of high flexibility, good versatility and low cost. However, limited by the low absolute positioning accuracy, there are still huge obstacles in high precision machining processes such as grinding. Aiming at this problem, a compensation method combining analytical modeling for quantitative errors with spatial interpolation algorithm for random errors is proposed based on the full consideration of the source and characteristics of positioning errors. Firstly, as for the quantitative errors, namely geometric parameter and compliance error in this paper, a kinematics-based error model is constructed taking the coupling effect of errors into consideration. Then avoiding the impact of random errors, the extended Kalman filtering algorithm (EKF) is adopted to identify the error parameters. Secondly, based on the similarity principle of spatial error, spatial interpolation algorithm is used to model the residual error caused by temperature, gear clearance etc. Based on the spatial anisotropy characteristics of robot motion performance, an adaptive mesh division algorithm was proposed to balance the accuracy and efficiency of mesh division. Then, an inverse distance weighted interpolation algorithm considering the influence degree of different joints on the end position was established to improve the approximation accuracy of residual error. Finally, the rough-fine two-stage serial error compensation method was carried out. Experimental results show the mean absolute positioning accuracy is improved from 1.165 mm to 0.106 mm, which demonstrates the effectiveness of the method in this paper.

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.

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.

A channel phase error compensation method for multi-channel synthetic aperture ladar

Optik ◽  
2019 ◽  
Vol 178 ◽  
pp. 830-840
Author(s):  
Shuai Wang ◽  
Maosheng Xiang ◽  
Bingnan Wang ◽  
Fubo Zhang ◽  
Yirong Wu
Keyword(s):  
Error Compensation ◽  
Phase Error ◽  
Compensation Method ◽  
Synthetic Aperture
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