A Robotic Calibration Method Using a Model-Based Identification Technique and an Invasive Weed Optimization Neural Network Compensator

A Robotic Calibration Method Using a Model-Based Identification Technique and an Invasive Weed Optimization Neural Network Compensator

The study proposed a robotic calibration algorithm for improving the robot manipulator position precision. At first, the kinematic parameters as well as the compliance parameters of the robot can be identified together to improve its accuracy using the joint deflection model and the conventional kin...

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Main Authors: Phu-Nguyen Le, Hee-Jun Kang
Format: Article
Language:English
Published: MDPI AG 2020-10-01
Series:Applied Sciences
Subjects:
invasive weed optimization
neural network
robot accuracy
robot calibration
Online Access:https://www.mdpi.com/2076-3417/10/20/7320
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spelling doaj-1702a1e7f84944d0b42cb31c2e8863372020-11-25T04:00:57ZengMDPI AGApplied Sciences2076-34172020-10-01107320732010.3390/app10207320A Robotic Calibration Method Using a Model-Based Identification Technique and an Invasive Weed Optimization Neural Network CompensatorPhu-Nguyen Le0Hee-Jun Kang1Graduate School of Electrical Engineering, University of Ulsan, Ulsan 44610, KoreaSchool of Electrical Engineering, University of Ulsan, Ulsan 44610, KoreaThe study proposed a robotic calibration algorithm for improving the robot manipulator position precision. At first, the kinematic parameters as well as the compliance parameters of the robot can be identified together to improve its accuracy using the joint deflection model and the conventional kinematic model calibration technique. Then, an artificial neural network is constructed for further compensating the unmodeled errors. The invasive weed optimization is used to determine the parameters of the neural network. To show the advantages of the suggested technique, an HH800 robot is employed for the experimental study of the proposed algorithm. The improved position precision of the robot after the experiment firmly proves the practicability and positional precision of the proposed method over the other algorithms in comparison.https://www.mdpi.com/2076-3417/10/20/7320invasive weed optimizationneural networkrobot accuracyrobot calibration
collection DOAJ
language English
format Article
sources DOAJ
author Phu-Nguyen Le
Hee-Jun Kang
spellingShingle Phu-Nguyen Le
Hee-Jun Kang
A Robotic Calibration Method Using a Model-Based Identification Technique and an Invasive Weed Optimization Neural Network Compensator
Applied Sciences
invasive weed optimization
neural network
robot accuracy
robot calibration
author_facet Phu-Nguyen Le
Hee-Jun Kang
author_sort Phu-Nguyen Le
title A Robotic Calibration Method Using a Model-Based Identification Technique and an Invasive Weed Optimization Neural Network Compensator
title_short A Robotic Calibration Method Using a Model-Based Identification Technique and an Invasive Weed Optimization Neural Network Compensator
title_full A Robotic Calibration Method Using a Model-Based Identification Technique and an Invasive Weed Optimization Neural Network Compensator
title_fullStr A Robotic Calibration Method Using a Model-Based Identification Technique and an Invasive Weed Optimization Neural Network Compensator
title_full_unstemmed A Robotic Calibration Method Using a Model-Based Identification Technique and an Invasive Weed Optimization Neural Network Compensator
title_sort robotic calibration method using a model-based identification technique and an invasive weed optimization neural network compensator
publisher MDPI AG
series Applied Sciences
issn 2076-3417
publishDate 2020-10-01
description The study proposed a robotic calibration algorithm for improving the robot manipulator position precision. At first, the kinematic parameters as well as the compliance parameters of the robot can be identified together to improve its accuracy using the joint deflection model and the conventional kinematic model calibration technique. Then, an artificial neural network is constructed for further compensating the unmodeled errors. The invasive weed optimization is used to determine the parameters of the neural network. To show the advantages of the suggested technique, an HH800 robot is employed for the experimental study of the proposed algorithm. The improved position precision of the robot after the experiment firmly proves the practicability and positional precision of the proposed method over the other algorithms in comparison.
topic invasive weed optimization
neural network
robot accuracy
robot calibration
url https://www.mdpi.com/2076-3417/10/20/7320
work_keys_str_mv AT phunguyenle aroboticcalibrationmethodusingamodelbasedidentificationtechniqueandaninvasiveweedoptimizationneuralnetworkcompensator
AT heejunkang aroboticcalibrationmethodusingamodelbasedidentificationtechniqueandaninvasiveweedoptimizationneuralnetworkcompensator
AT phunguyenle roboticcalibrationmethodusingamodelbasedidentificationtechniqueandaninvasiveweedoptimizationneuralnetworkcompensator
AT heejunkang roboticcalibrationmethodusingamodelbasedidentificationtechniqueandaninvasiveweedoptimizationneuralnetworkcompensator
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