Error recognition of robot kinematics parameters based on genetic algorithms

2020 ◽  
Vol 11 (12) ◽  
pp. 6167-6176 ◽  
Author(s):  
Ying Yan
Keyword(s):  
Genetic Algorithms ◽  
Robot Kinematics ◽  
Error Recognition ◽  
Kinematics Parameters

Pose Accuracy Analysis of Robot Manipulators Based on Kinematics

2011 ◽  
Vol 201-203 ◽  
pp. 1867-1872 ◽  
Author(s):  
Jian Ye Zhang ◽  
Chen Zhao ◽  
Da Wei Zhang
Keyword(s):  
Matrix Theory ◽  
Robot Manipulators ◽  
Error Model ◽  
Robot Kinematics ◽  
Accuracy Analysis ◽  
Serial Link ◽  
End Effector ◽  
Surface Graph ◽  
Position And Orientation ◽  
Kinematics Parameters

The pose accuracy of robot manipulators has long become a major issue to be considered in its advanced application. An efficient methodology to generate the end-effector position and orientation error model of robotic manipulator has been proposed based on the differential transformation matrix theory. According to this methodology, a linear error model that described the end-effector position and orientation errors due to robot kinematics parameters errors has been presented. A computer program to generate the error model and perform the accuracy analysis on any serial link manipulator has been developed in MATLAB. This methodology and software are applied to the accuracy analysis of a Phantom Desktop manipulator. The positioning error of the manipulator in its workspace cross section (XOZ) has been plotted as 3D surface graph and discussed.

Industrial Robot Kinematics Parameter Identification

2014 ◽  
Vol 889-890 ◽  
pp. 1136-1143
Author(s):  
Yong Gui Zhang ◽  
Chen Rong Liu ◽  
Peng Liu
Keyword(s):  
Experimental Data ◽  
Genetic Algorithms ◽  
Parameter Identification ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Robot Kinematics ◽  
Kinematic Parameters ◽  
Unknown Parameters ◽  
Preliminary Measurement ◽  
Kinematics Modeling

For an industrial robots with unknown parameters, on the basis of preliminary measurement and data of the Cartesian and joints coordinates which are shown on the FlexPendant, the kinematic parameters is identified by using genetic algorithms and accurate kinematics modeling of the robot is established. Experimental data could prove the validity of this method.

Reduce Positioning Error for Robot Manipulator with Intelligent Technique

2011 ◽  
Vol 403-408 ◽  
pp. 697-706
Author(s):  
Maryam Barati ◽  
Ahmad Reza Khoogar ◽  
Mehrzad Nasiriyan
Keyword(s):  
Robot Manipulator ◽  
Coordinate Measuring Machine ◽  
Least Square ◽  
Robot Kinematics ◽  
Automatic Calibration ◽  
Intelligent Technique ◽  
Measuring Machine ◽  
Three Degree Of Freedom ◽  
Kinematics Parameters

Using robot manipulators for high accuracy applications require precise value of the kinematics parameters. Since measurement of kinematics parameters are usually corrupted with errors and their accurate measurements are usually expensive, automatic calibration of robot link parameters makes the determination of kinematic parameters much simpler. In this paper a simple and easy to use algorithm is introduced for correction and calibration of robot kinematics parameters. Actually, at several end-effecter positions, the corresponding joint variables are measured simultaneously. This information is then used in three different algorithms; the Least Square (LS), the Particle Swarm Optimization (PSO) and the Genetic algorithms (GA) for automatic calibration and correction of the kinematics parameters. This process was also tested experimentally using a three degree of freedom manipulator which was actually built as a coordinate measuring machine (CMM). The experimental Results show that the intelligent algorithms provide better results for both parameter identification and calibration of the link parameters.

Calibration Method and Experiments of Robot Kinematics Parameters Based on Error Model

ROBOT ◽  
2012 ◽  
Vol 34 (6) ◽  
pp. 680 ◽  
Author(s):  
Gang CHEN ◽  
Qingxuan JIA ◽  
Tong LI ◽  
Hanxu SUN
Keyword(s):  
Calibration Method ◽  
Error Model ◽  
Robot Kinematics ◽  
Kinematics Parameters

An Approach of Robotic Kinematics Parameters Calibration

2013 ◽  
Vol 655-657 ◽  
pp. 1023-1028 ◽  
Author(s):  
Yong Gui Zhang ◽  
Heng Zhang
Keyword(s):  
Laser Interferometer ◽  
Joint Angle ◽  
Hybrid Genetic Algorithm ◽  
Robot Kinematics ◽  
Robot Calibration ◽  
End Effector ◽  
Position Data ◽  
Recorded Data ◽  
Kinematics Parameters ◽  
System Axis

Aiming at tedious of parameters identification in the process of robot calibration, this paper proposed a method to directly establish parameter error equations based on relative distance error by using the kinematics model in which the parameters error were considered, employing a laser interferometer to measure a 6R robot end-effector displacement distance along its Cartesian coordinates system axis direction, and respectively recorded the joint angle position data to the measured points, then taking all recorded data as basis for the robot kinematics parameter errors identifying equations and employing a hybrid genetic algorithm to solve the equations. The result shown that incomplete distance info can be used to identify kinematics parameters error in robot calibration process.

Genetic Algorithms—a Tool for OR?

1996 ◽  
Vol 47 (4) ◽  
pp. 550-561 ◽  
Author(s):  
Kathryn A Dowsland
Keyword(s):  
Genetic Algorithms

Enhanced Artificial Intelligent Method for Representing the Photonic Crystal Fiber Profile Index

2018 ◽  
Vol 1 (1) ◽  
pp. 2-19
Author(s):  
Mahmood Sh. Majeed ◽  
Raid W. Daoud
Keyword(s):  
Genetic Algorithms ◽  
Photonic Crystal ◽  
Photonic Crystal Fiber ◽  
New Method ◽  
Proper Solution ◽  
Artificial Intelligent ◽  
Crystal Fiber ◽  
Fitness Value ◽  
First Pass ◽  
Crossover And Mutation

A new method proposed in this paper to compute the fitness in Genetic Algorithms (GAs). In this new method the number of regions, which assigned for the population, divides the time. The fitness computation here differ from the previous methods, by compute it for each portion of the population as first pass, then the second pass begin to compute the fitness for population that lye in the portion which have bigger fitness value. The crossover and mutation and other GAs operator will do its work only for biggest fitness portion of the population. In this method, we can get a suitable and accurate group of proper solution for indexed profile of the photonic crystal fiber (PCF).

Sign in / Sign up

Export Citation Format

Share Document