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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhou, Z., Huang, G., Gao, J., et al.: Radar emitter identification algorithm based on deep learning. J. Xidian Univ. 44(3), 77–82 (2017)
Kamrava, S., Tahmasebi, P., Sahimi, M.: Enhancing images of shale formations by a hybrid stochastic and deep learning algorithm. Neural Netw. Off. J. Int. Neural Netw. Soc. 118, 310 (2019)
Keqiang, B., Zhigui, L., Yingtong, W.: An integrated design method coupling structure and control for industrial robot. Sci. Technol. Rev. 36(9), 91–96 (2018)
Vuthi, Y., Wangyao, N., Phamvan, C.: Recurrent fuzzy wavelet neural networks based on robust adaptive sliding mode control for industrial robot manipulators. Neural Comput. Appl. 31(18), 1–14 (2018)
Rosa, D.G.G., Feiteira, J.F.S., Lopes, A.M., et al.: Analysis and implementation of a force control strategy for drilling operations with an industrial robot. J. Braz. Soc. Mech. Sci. Eng. 39(1), 1–8 (2017)
Xiuxing, Y., Li, P.: Direct adaptive robust tracking control for 6 DOF industrial robot with enhanced accuracy. ISA Trans. 72, 178–184 (2017)
de Gea Fernández, J., Mronga, D., Günther, M., et al.: Multimodal sensor-based whole-body control for human-robot collaboration in industrial settings. Robot. Auton. Syst. 94, 102–119 (2017)
Santos, J., André, C., Santos, T., et al.: Remote control of an omnidirectional mobile robot with time-varying delay and noise attenuation. Mechatronics 52 (2018)
Lee, D., Lee, J.: A hybrid joystick with impedance control for a stable remote control of a mobile robot. Int. J. Human. Robot. 16(1) (2019)
Santos Lopesdos, M.S., Gomes, I.P., Trindade, R.M., et al.: Web environment for programming and control of a mobile robot in a remote laboratory. IEEE Trans. Learn. Technol. 10(4), 526–531 (2017)
Funding
Key topics of Beijing Polytechnic, Research and design of equipment management system based on RFID (CJGX2016-KY-YZK041).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Zhao, D., Qu, M.F. (2020). Simulation Training Remote Control System of Industrial Robot Based on Deep Learning. In: Liu, S., Sun, G., Fu, W. (eds) e-Learning, e-Education, and Online Training. eLEOT 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 340. Springer, Cham. https://doi.org/10.1007/978-3-030-63955-6_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-63955-6_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63954-9
Online ISBN: 978-3-030-63955-6
eBook Packages: Computer ScienceComputer Science (R0)