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

  • 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
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.

 

Downloads

Download data is not yet available.

References

Bach, P. F. (2009). Myoelectric signal features for upper limb prostheses. Institutt for teknisk kybernetikk,
Bonilla, V., Lukyanov, Y., Anatolevich, L., Anatoly, V., & Alekseevich, D. D. (2015). Identificación de parámetros cinemáticos del movimiento del codo utilizando tecnologías de redes neuronales artificiales. Boletín de la Universidad Técnica del Estado de Don, 15(1 (80)).
Cipriano, M. (2014). Antropología física.
Ferguson, S., & Dunlop, G. R. (2002). Grasp recognition from myoelectric signals. Paper presented at the Proceedings of the Australasian Conference on Robotics and Automation, Auckland, New Zealand.
Forsberg, K., Mooz, H., & Cotterman, H. (2005). Visualizing project management: models and frameworks for mastering complex systems: John Wiley & Sons.
Gauchía Babé, L. (2008). Modelado y simulación HIL (hardware-in-the-loop) de un sistema pila de combustible-batería.
Hoyo, A., Reyes, O., Rebolledo, A., & Espinoza, L. (2009). Simulación de robots con Matlab y Simulink en escenarios virtuales 3D. Paper presented at the I Congreso Iberoamericano de Enseñanza de la Ingeniería.
Isermann, R. (1996). Modeling and design methodology for mechatronic systems. IEEE/ASME Transactions on mechatronics, 1(1), 16-28.
Litvin, A., Lukyanov, E., Bonilla, F., & Deplov, D. (2014). EFFECT OF KINEMATIC PARAMETERS OF ELBOW MOTION ON BICEPS ELECTROMYOGRAPHIC SIGNAL. Vestnik of Don State Technical University(14), 8.
Morais, G. D., Neves, L. C., Masiero, A. A., & de Castro, M. C. F. (2016). Application of Myo Armband System to Control a Robot Interface. Paper presented at the BIOSIGNALS.
Ramos, M., Betancourt, Á., Vázquez, G., Hernández, E., & Juárez, L. (2011). Detección y Acondicionamiento de Señales Mioeléctricas.
Sánchez, N. M. C. (2016). Gesture classification based on electromyography.
Veer, K., & Sharma, T. (2016). A novel feature extraction for robust EMG pattern recognition. Journal of medical engineering & technology, 40(4), 149-154.
Published
2018-06-29
How to Cite
Bonilla Venegas, F., Moya, M., Litvin, A., Lukyanov, E., & Marín Pillajo, L. (2018). Modeling and Simulation of the Robot Mitsubishi RV-2JA controlled by electromyographic signals. Enfoque UTE, 9(2), pp. 208 - 222. https://doi.org/https://doi.org/10.29019/enfoqueute.v9n2.326
Section
Automation and Control, Mechatronics, Electromechanics, Automotive