Solving the Inverse Kinematic Equations of Elastic Robot Arm Utilizing Neural Network

The inverse kinematic equation for a robot is very important to the control robot’s motion and position. The solving of this equation is complex for the rigid robot due to the dependency of this equation on the joint configuration and structure of robot link. In light robot arms, where the flexibil...

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Autores principales: Hussein M. Al-Khafaji, Muhsin J. Jweeg
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Lenguaje:EN
Publicado: Al-Khwarizmi College of Engineering – University of Baghdad 2017
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Acceso en línea:https://doaj.org/article/af07ed821dca42dbbdf2b172d2918cf9
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spelling oai:doaj.org-article:af07ed821dca42dbbdf2b172d2918cf92021-12-02T04:44:59ZSolving the Inverse Kinematic Equations of Elastic Robot Arm Utilizing Neural Network10.22153/kej.2017.11.0021818-11712312-0789https://doaj.org/article/af07ed821dca42dbbdf2b172d2918cf92017-12-01T00:00:00Zhttp://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/330https://doaj.org/toc/1818-1171https://doaj.org/toc/2312-0789 The inverse kinematic equation for a robot is very important to the control robot’s motion and position. The solving of this equation is complex for the rigid robot due to the dependency of this equation on the joint configuration and structure of robot link. In light robot arms, where the flexibility exists, the solving of this problem is more complicated than the rigid link robot because the deformation variables (elongation and bending) are present in the forward kinematic equation. The finding of an inverse kinematic equation needs to obtain the relation between the joint angles and both of the end-effector position and deformations variables. In this work, a neural network has been proposed to solve the problem of inverse kinematic equation. To feed the neural network, experimental data were taken from an elastic robot arm for training the network, these data presented by joint angles, deformation variables and end-effector positions. The results of network training showed a good fit between the output results of the neural network and the targets data. In addition, this method for finding the inverse of kinematic equation proved its effectiveness and validation when applying the results of neural network practically in the robot’s operating software for controlling the real light robot’s position. Hussein M. Al-KhafajiMuhsin J. JweegAl-Khwarizmi College of Engineering – University of BaghdadarticleElastic robotforward and inverse kinematic equation of elastic robotNeural networksChemical engineeringTP155-156Engineering (General). Civil engineering (General)TA1-2040ENAl-Khawarizmi Engineering Journal, Vol 13, Iss 1 (2017)
institution DOAJ
collection DOAJ
language EN
topic Elastic robot
forward and inverse kinematic equation of elastic robot
Neural networks
Chemical engineering
TP155-156
Engineering (General). Civil engineering (General)
TA1-2040
spellingShingle Elastic robot
forward and inverse kinematic equation of elastic robot
Neural networks
Chemical engineering
TP155-156
Engineering (General). Civil engineering (General)
TA1-2040
Hussein M. Al-Khafaji
Muhsin J. Jweeg
Solving the Inverse Kinematic Equations of Elastic Robot Arm Utilizing Neural Network
description The inverse kinematic equation for a robot is very important to the control robot’s motion and position. The solving of this equation is complex for the rigid robot due to the dependency of this equation on the joint configuration and structure of robot link. In light robot arms, where the flexibility exists, the solving of this problem is more complicated than the rigid link robot because the deformation variables (elongation and bending) are present in the forward kinematic equation. The finding of an inverse kinematic equation needs to obtain the relation between the joint angles and both of the end-effector position and deformations variables. In this work, a neural network has been proposed to solve the problem of inverse kinematic equation. To feed the neural network, experimental data were taken from an elastic robot arm for training the network, these data presented by joint angles, deformation variables and end-effector positions. The results of network training showed a good fit between the output results of the neural network and the targets data. In addition, this method for finding the inverse of kinematic equation proved its effectiveness and validation when applying the results of neural network practically in the robot’s operating software for controlling the real light robot’s position.
format article
author Hussein M. Al-Khafaji
Muhsin J. Jweeg
author_facet Hussein M. Al-Khafaji
Muhsin J. Jweeg
author_sort Hussein M. Al-Khafaji
title Solving the Inverse Kinematic Equations of Elastic Robot Arm Utilizing Neural Network
title_short Solving the Inverse Kinematic Equations of Elastic Robot Arm Utilizing Neural Network
title_full Solving the Inverse Kinematic Equations of Elastic Robot Arm Utilizing Neural Network
title_fullStr Solving the Inverse Kinematic Equations of Elastic Robot Arm Utilizing Neural Network
title_full_unstemmed Solving the Inverse Kinematic Equations of Elastic Robot Arm Utilizing Neural Network
title_sort solving the inverse kinematic equations of elastic robot arm utilizing neural network
publisher Al-Khwarizmi College of Engineering – University of Baghdad
publishDate 2017
url https://doaj.org/article/af07ed821dca42dbbdf2b172d2918cf9
work_keys_str_mv AT husseinmalkhafaji solvingtheinversekinematicequationsofelasticrobotarmutilizingneuralnetwork
AT muhsinjjweeg solvingtheinversekinematicequationsofelasticrobotarmutilizingneuralnetwork
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