Modeling and Simulation of the Robot Mitsubishi RV-2JA controlled by electromyographic signals

Authors

  • Félix Vladimir Bonilla Venegas Universidad Tecnológica Equinoccial
  • Marcelo Javier Moya Universidad Tecnológica Equinoccial
  • Anatoly Litvin Don State Technical University
  • Evgeny Lukyanov Don State Technical University
  • Leonardo Emanuel Marín Pillajo Universidad Tecnológica Equinoccial

DOI:

https://doi.org/10.29019/enfoqueute.v9n2.326

Keywords:

Surface electromyography, Hardware in the loop, artificial neural network, Robot Mitsubishi RV-2JA, Myo.

Abstract

The aim of this work is control the Mitsubishi RV-2JA Robot using sEMG surface electromyographic signals. The sEMG signals were obtained from the hand through a Myo bracelet with surface sensors. Myo surface sensors are able to detect the electromyographic signals generated by the muscles. The integration of the system was performed in Matlab's Simulink platform to process, identify, validate and control the robot through the electromyographic signals. The hand gestures analysis was performed using a temporal approximation that allowed the extraction of characteristics of the signals. It was determined that the parameters Electromyographic Integrated, Mean Absolute Value, Quadratic Mean and Variance have direct correlation with the type of Hand movement. In order to classify the first movements like spread fingers, wave right, wave left, elder and voor, we used 6 neural networks, which allow to activate 3 degrees of freedom of the robot. For the integration and verification of the real-time system, the hardware in loop simulation (HIL) was applied. This simulation allowed the execution of the plant model, the connection with the appropriate control and communication system to verify that the system controls the robot.

 

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References

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Published

2018-06-29

How to Cite

Bonilla Venegas, F. V., Moya, M. J., Litvin, A., Lukyanov, E., & Marín Pillajo, L. E. (2018). Modeling and Simulation of the Robot Mitsubishi RV-2JA controlled by electromyographic signals. Enfoque UTE, 9(2), pp. 208 – 222. https://doi.org/10.29019/enfoqueute.v9n2.326

Issue

Vol. 9 No. 2 (2018)

Section

Automation and Control, Mechatronics, Electromechanics, Automotive

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