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2020 | OriginalPaper | Chapter

Simulation Training Remote Control System of Industrial Robot Based on Deep Learning

Authors : Dan Zhao, Ming Fei Qu

Published in: e-Learning, e-Education, and Online Training

Publisher: Springer International Publishing

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Abstract

In order to improve the remote control performance of industrial robot simulation training, deep learning algorithm is used to optimize the design of traditional remote control system. On the basis of traditional remote control system, the configuration of hardware system is modified, and the database of control system is established. With the support of hardware system and database, the remote control of two training items of industrial robot simulation mobile training and simulation picking training are realized respectively. Through the system test experiment, the conclusion is drawn: compared with the traditional industrial robot remote control system, the control function of the design control system is improved, and the system can save about 12.5 s response time in the control process.

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Metadata
Title
Simulation Training Remote Control System of Industrial Robot Based on Deep Learning
Authors
Dan Zhao
Ming Fei Qu
Copyright Year
2020
Book
e-Learning, e-Education, and Online Training

Print ISBN: 978-3-030-63954-9

Electronic ISBN: 978-3-030-63955-6

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-63955-6_21

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