The parameter identification model considering both geometric parameters and joint stiffness

2019 ◽  
Vol 47 (1) ◽  
pp. 76-81
Author(s):  
Jing Bai ◽  
Le Fan ◽  
Shuyang Zhang ◽  
Zengcui Wang ◽  
Xiansheng Qin
Keyword(s):  
Parameter Identification ◽  
Industrial Robot ◽  
Joint Stiffness ◽  
Geometric Parameters ◽  
Least Square ◽  
Experimental Result ◽  
Positional Accuracy ◽  
Content Type ◽  
The Absolute ◽  
Identification Model

Purpose Both geometric and non-geometric parameters have noticeable influence on the absolute positional accuracy of 6-dof articulated industrial robot. This paper aims to enhance it and improve the applicability in the field of flexible assembling processing and parts fabrication by developing a more practical parameter identification model. Design/methodology/approach The model is developed by considering both geometric parameters and joint stiffness; geometric parameters contain 27 parameters and the parallelism problem between axes 2 and 3 is involved by introducing a new parameter. The joint stiffness, as the non-geometric parameter considered in this paper, is considered by regarding the industrial robot as a rigid linkage and flexible joint model and adds six parameters. The model is formulated as the form of error via linearization. Findings The performance of the proposed model is validated by an experiment which is developed on KUKA KR500-3 robot. An experiment is implemented by measuring 20 positions in the work space of this robot, obtaining least-square solution of measured positions by the software MATLAB and comparing the result with the solution without considering joint stiffness. It illustrates that the identification model considering both joint stiffness and geometric parameters can modify the theoretical position of robots more accurately, where the error is within 0.5 mm in this case, and the volatility is also reduced. Originality/value A new parameter identification model is proposed and verified. According to the experimental result, the absolute positional accuracy can be remarkably enhanced and the stability of the results can be improved, which provide more accurate parameter identification for calibration and further application.

A distance error based industrial robot kinematic calibration method

2014 ◽  
Vol 41 (5) ◽  
pp. 439-446 ◽  
Author(s):  
Wang Zhenhua ◽  
Xu Hui ◽  
Chen Guodong ◽  
Sun Rongchuan ◽  
Lining Sun
Keyword(s):  
Industrial Robot ◽  
Calibration Method ◽  
Calibration Model ◽  
Kinematic Calibration ◽  
Absolute Position ◽  
Positioning Accuracy ◽  
Content Type ◽  
Distance Error ◽  
Position Accuracy ◽  
The Absolute

Purpose – The purpose of this paper is to present a distance accuracy-based industrial robot kinematic calibration model. Nowadays, the repeatability of the industrial robot is high, while the absolute positioning accuracy and distance accuracy are low. Many factors affect the absolute positioning accuracy and distance accuracy, and the calibration method of the industrial robot is an important factor. When the traditional calibration methods are applied on the industrial robot, the accumulative error will be involved according to the transformation between the measurement coordinate and the robot base coordinate. Design/methodology/approach – In this manuscript, a distance accuracy-based industrial robot kinematic calibration model is proposed. First, a simplified kinematic model of the robot by using the modified Denavit–Hartenberg (MDH) method is introduced, then the proposed distance error-based calibration model is presented; the experiment is set up in the next section. Findings – The experimental results show that the proposed calibration model based on MDH and distance error can improve the distance accuracy and absolute position accuracy dramatically. Originality/value – The proposed calibration model based on MDH and distance error can improve the distance accuracy and absolute position accuracy dramatically.

Optimal trajectory planning of industrial robot for improving positional accuracy

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Amruta Rout ◽  
Deepak BBVL ◽  
Bibhuti B. Biswal ◽  
Golak Bihari Mahanta
Keyword(s):  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Industrial Robot ◽  
Optimization Technique ◽  
Dynamic Parameter ◽  
Optimal Time ◽  
Positional Error ◽  
Positional Accuracy ◽  
Nsga Ii ◽  
Content Type

Purpose The purpose of this paper is to improve the positional accuracy, smoothness on motion and productivity of industrial robot through the proposed optimal joint trajectory planning method. Also a new improved algorithm, i.e. non-dominated sorting genetic algorithm-II (NSGA-II) with achievement scalarizing function (ASF) has been proposed to obtain better optimal results compared to previously used optimization methods. Design/methodology/approach The end effector positional errors can be reduced by limiting the uncertainties of dynamic parameter variations like torque rate of joints. The jerk induced in robot joints due to acceleration variations are need to be minimized which otherwise induces vibrations in the manipulator that causes deviation in the encoders. But these lead to a vast increase in total travel time which affects the cost function of trajectory planning. Therefore, these three objectives need to be minimized individually so that an optimal trajectory path can be achieved with minimum positional error. Findings The simulation results have been obtained by running the proposed hybrid NSGA-II with ASF in MATLAB R2017a software. The optimal time intervals have been used to calculate jerk, acceleration and torque values for consecutive points on the trajectory path. From the simulation and experimental results, it can be concluded that the optimization technique could be used effectively for the trajectory planning of six-axis industrial manipulator in the joint space on the basis of minimum time-jerk-torque rate criteria. Originality/value In this paper, a new approach based on hybrid multi-objective optimization technique by combining NSGA-II with ASF has been applied to find the minimal time-jerk- torque rate joint trajectory of a six-axis industrial robot for obtaining higher positional accuracy. The results obtained from the execution of algorithm have been validated through experimentation using Kawasaki RS06L industrial robot for a particular defined path.

A novel LLSDPso method for nonlinear dynamic parameter identification

2017 ◽  
Vol 37 (4) ◽  
pp. 490-498 ◽  
Author(s):  
Jian-jun Yuan ◽  
Weiwei Wan ◽  
Xiajun Fu ◽  
Shuai Wang ◽  
Ning Wang
Keyword(s):  
Parameter Identification ◽  
High Efficiency ◽  
Linear Part ◽  
Force Sensor ◽  
Least Square ◽  
Measurement Unit ◽  
Flexible Robot ◽  
Friction Coefficients ◽  
Content Type ◽  
Non Linear

Purpose This paper aims to propose a novel method to identify the parameters of robotic manipulators using the torque exerted by the robot joint motors (measured by current sensors). Design/methodology/approach Previous studies used additional sensors like force sensor and inertia measurement unit, or additional payload mounted on the end-effector to perform parameter identification. The settings of these previous works were complicated. They could only identify part of the parameters. This paper uses the torque exerted by each joint while performing Fourier periodic excited trajectories. It divides the parameters into a linear part and a non-linear part, and uses linear least square (LLS) parameter estimation and dual-swarm-based particle swarm optimization (DPso) to compute the linear and non-linear parts, respectively. Findings The settings are simpler and can identify the dynamic parameters, the viscous friction coefficients and the Coulomb friction coefficients of two joints at the same time. A SIASUN 7-Axis Flexible Robot is used to experimentally validate the proposal. Comparison between the predicted torque values and ground-truth values of the joints confirms the effectiveness of the method. Originality/value The proposed method identifies two joints at the same time with satisfying precision and high efficiency. The identification errors of joints do not accumulate.

Calibration and Compensation of Rotary Axis Angular Positioning Deviations on a SCARA-Type Industrial Robot Using a Laser Tracker

2020 ◽  
Author(s):  
Nan Zhao ◽  
Soichi Ibaraki
Keyword(s):  
Rotation Axis ◽  
Industrial Robot ◽  
Kinematic Model ◽  
Industrial Robots ◽  
Laser Tracker ◽  
Link Length ◽  
Positional Accuracy ◽  
Positioning Accuracy ◽  
Rotary Axis ◽  
The Absolute

Abstract In general, the “absolute” positioning accuracy of industrial robots is significantly lower than its repeatability. In the past research, in order to improve a robot’s positioning accuracy over the entire workspace, the compensation for the link length errors and the rotation axis angle offsets are often employed. However, the positioning error of the compensated industrial robot is still much higher than that of a typical machine tool. The purpose of this study is to propose a new kinematic model and its calibration scheme to further improve the absolute positional accuracy of an industrial robot over the entire workspace. In order to simplify the problem, this study only targets the 2D positioning accuracy of a SCARA-type robot. The proposed model includes not only link length errors and rotary axis angular offsets but also the “error map” of the angular positioning deviation of each rotary axis. The angular error deviation of each rotary axis is identified by measuring the robot’s end-effector position by a laser tracker at many positions. To verify the validity of the identified model, the effectiveness of the compensation based on it is also investigated.

Proprietary information and the cost of bank debt

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
HyunJun Na
Keyword(s):  
Least Square ◽  
Bank Loan ◽  
Content Type ◽  
Loan Contracts ◽  
Proprietary Information ◽  
Banking Deregulation ◽  
Interstate Banking ◽  
Innovative Firms ◽  
Bank Loan Contracts ◽  
The Impact

PurposeThis study explores how the firm’s proprietary information has an impact on the bank loan contracts. It explains the propensity of using the competitive bid option (CBO) in the syndicate loans to solicit the best bid for innovative firms and how it changes based on industry competition and the degree of innovations. This research also examines how the interstate banking deregulation (Interstate Banking and Branching Efficiency Act) in 1994 affected the private loan contracts for innovative borrowers.Design/methodology/approachThe study uses various econometric analyses. First, it uses the propensity score matching analysis to see the impact of patents on pricing terms. Second, it uses the two-stage least square (2SLS) analysis by implementing the litigation and non-NYSE variables. Finally, it studies the impact of the policy change of the Interstate Banking and Branching Efficiency Act of 1994 on the bank loan contracts.FindingsFirms with more proprietary information pays more annual facility fees but less other fees. The patents are the primary determinants of the usage of CBO in the syndicate loans to solicit the best bid. While innovative firms can have better contract conditions by the CBO, firms with more proprietary information will less likely to use the CBO option to minimize the leakage of private information and the severe monitoring from the banks. Finally, more proprietary information lowered the loan spread for firms dependent on the external capital after the interstate banking deregulation.Originality/valueThe findings of this research will help senior executives with responsibility for financing their innovative projects. In addition, these findings should prove helpful for the lawmakers to boost economies.

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.

An improved nonlinear innovation-based parameter identification algorithm for ship models

2021 ◽  
pp. 1-9
Author(s):  
Baigang Zhao ◽  
Xianku Zhang
Keyword(s):  
Parameter Identification ◽  
Test Data ◽  
Stochastic Gradient ◽  
Least Square ◽  
Gradient Algorithm ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Process Innovations ◽  
Stochastic Gradient Algorithm ◽  
Tangent Function

Abstract To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

Human skill integrated motion planning of assembly manipulation for 6R industrial robot

2019 ◽  
Vol 46 (1) ◽  
pp. 171-180
Author(s):  
Yi Liu ◽  
Ming Cong ◽  
Hang Dong ◽  
Dong Liu
Keyword(s):  
Deep Learning ◽  
Motion Planning ◽  
Industrial Robot ◽  
Robotic Assembly ◽  
3D Vision ◽  
Content Type ◽  
Human Skill ◽  
Depth Images ◽  
Positioning Method ◽  
Assembly Technology

Purpose The purpose of this paper is to propose a new method based on three-dimensional (3D) vision technologies and human skill integrated deep learning to solve assembly positioning task such as peg-in-hole. Design/methodology/approach Hybrid camera configuration was used to provide the global and local views. Eye-in-hand mode guided the peg to be in contact with the hole plate using 3D vision in global view. When the peg was in contact with the workpiece surface, eye-to-hand mode provided the local view to accomplish peg-hole positioning based on trained CNN. Findings The results of assembly positioning experiments proved that the proposed method successfully distinguished the target hole from the other same size holes according to the CNN. The robot planned the motion according to the depth images and human skill guide line. The final positioning precision was good enough for the robot to carry out force controlled assembly. Practical implications The developed framework can have an important impact on robotic assembly positioning process, which combine with the existing force-guidance assembly technology as to build a whole set of autonomous assembly technology. Originality/value This paper proposed a new approach to the robotic assembly positioning based on 3D visual technologies and human skill integrated deep learning. Dual cameras swapping mode was used to provide visual feedback for the entire assembly motion planning process. The proposed workpiece positioning method provided an effective disturbance rejection, autonomous motion planning and increased overall performance with depth images feedback. The proposed peg-hole positioning method with human skill integrated provided the capability of target perceptual aliasing avoiding and successive motion decision for the robotic assembly manipulation.

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