The Inverse Solution Algorithm and Trajectory Error Analysis of Robotic Arm Based on MQACA-RBF Network

In order to improve the position accuracy and trajectory accuracy of a 6R robotic arm, a robot arm inverse solution algorithm based on the MQACA- (improved quantum ant colony-) RBF network is proposed. This algorithm establishes the prediction model through the neural network and uses the quantum an...

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Main Authors: Xu Cheng, Ming Zhao
Format: Article
Language:English
Published: Wiley 2020-01-01
Series:Journal of Robotics
Online Access:http://dx.doi.org/10.1155/2020/7807952
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author Xu Cheng
Ming Zhao
author_facet Xu Cheng
Ming Zhao
author_sort Xu Cheng
collection DOAJ
description In order to improve the position accuracy and trajectory accuracy of a 6R robotic arm, a robot arm inverse solution algorithm based on the MQACA- (improved quantum ant colony-) RBF network is proposed. This algorithm establishes the prediction model through the neural network and uses the quantum ant colony algorithm to optimize the output weight. In order to solve the problem that the quantum ant colony algorithm has low convergence precision and easy to fall into the local optimal solution in the inverse solution algorithm of the multifreedom robotic arm, improved measures such as 2-opt local optimization and maximum minimum pheromone limit and variation are adopted. By comparing the simulation results of the 6R robotic arm simulation results and the simulation results based on ACA, QACA, and RBF neural networks on the position and motion trajectory of the space point, the advantages in precision are obvious. This proves the feasibility and effectiveness of the scheme.
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institution Kabale University
issn 1687-9600
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publishDate 2020-01-01
publisher Wiley
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series Journal of Robotics
spelling doaj-art-8d0cf0619256489ea5cc233521bb079b2025-02-03T01:05:21ZengWileyJournal of Robotics1687-96001687-96192020-01-01202010.1155/2020/78079527807952The Inverse Solution Algorithm and Trajectory Error Analysis of Robotic Arm Based on MQACA-RBF NetworkXu Cheng0Ming Zhao1School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, ChinaSchool of Applied Technology, University of Science and Technology Liaoning, Anshan 114051, ChinaIn order to improve the position accuracy and trajectory accuracy of a 6R robotic arm, a robot arm inverse solution algorithm based on the MQACA- (improved quantum ant colony-) RBF network is proposed. This algorithm establishes the prediction model through the neural network and uses the quantum ant colony algorithm to optimize the output weight. In order to solve the problem that the quantum ant colony algorithm has low convergence precision and easy to fall into the local optimal solution in the inverse solution algorithm of the multifreedom robotic arm, improved measures such as 2-opt local optimization and maximum minimum pheromone limit and variation are adopted. By comparing the simulation results of the 6R robotic arm simulation results and the simulation results based on ACA, QACA, and RBF neural networks on the position and motion trajectory of the space point, the advantages in precision are obvious. This proves the feasibility and effectiveness of the scheme.http://dx.doi.org/10.1155/2020/7807952
spellingShingle Xu Cheng
Ming Zhao
The Inverse Solution Algorithm and Trajectory Error Analysis of Robotic Arm Based on MQACA-RBF Network
Journal of Robotics
title The Inverse Solution Algorithm and Trajectory Error Analysis of Robotic Arm Based on MQACA-RBF Network
title_full The Inverse Solution Algorithm and Trajectory Error Analysis of Robotic Arm Based on MQACA-RBF Network
title_fullStr The Inverse Solution Algorithm and Trajectory Error Analysis of Robotic Arm Based on MQACA-RBF Network
title_full_unstemmed The Inverse Solution Algorithm and Trajectory Error Analysis of Robotic Arm Based on MQACA-RBF Network
title_short The Inverse Solution Algorithm and Trajectory Error Analysis of Robotic Arm Based on MQACA-RBF Network
title_sort inverse solution algorithm and trajectory error analysis of robotic arm based on mqaca rbf network
url http://dx.doi.org/10.1155/2020/7807952
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