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NARX Neural Network for Safe Human–Robot Collaboration Using Only Joint Position Sensor

Author

Listed:
  • Abdel-Nasser Sharkawy

    (Mechatronics Engineering, Mechanical Engineering Department, Faculty of Engineering, South Valley University, Qena 83523, Egypt
    Mechanical Engineering Department, College of Engineering, Fahad Bin Sultan University, Tabuk 47721, Saudi Arabia)

  • Mustafa M. Ali

    (Mechatronics Engineering, Mechanical Engineering Department, Faculty of Engineering, South Valley University, Qena 83523, Egypt)

Abstract

Background : Safety is the very necessary issue that must be considered during human-robot collaboration in the same workspace or area. Methods : In this manuscript, a nonlinear autoregressive model with an exog-enous inputs neural network (NARXNN) is developed for the detection of collisions between a manipulator and human. The design of the NARXNN depends on the dynamics of the manipulator’s joints and considers only the signals of the position sensors that are intrinsic to the manipulator’s joints. Therefore, this network could be applied and used with any conventional robot. The data used for training the designed NARXNN are generated by two experiments considering the sinusoidal joint motion of the manipulator. The first experiment is executed using a free-of-contact motion, whereas in the second experiment, random collisions by human hands are performed with the robot. The training process of the NARXNN is carried out using the Levenberg–Marquardt algorithm in MATLAB. The evaluation and the effectiveness (%) of the developed method are investigated taking into account different data and conditions from the used data for training. The experiments are executed using the KUKA LWR IV manipulator. Results : The results prove that the trained method is efficient in estimating the external joint torque and in correctly detecting the collisions. Conclusions : Eventually, a comparison is presented between the proposed NARXNN and the other NN architectures presented in our previous work.

Suggested Citation

  • Abdel-Nasser Sharkawy & Mustafa M. Ali, 2022. "NARX Neural Network for Safe Human–Robot Collaboration Using Only Joint Position Sensor," Logistics, MDPI, vol. 6(4), pages 1-16, October.
    • Handle: RePEc:gam:jlogis:v:6:y:2022:i:4:p:75-:d:945534
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    References listed on IDEAS

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    1. Zina Boussaada & Octavian Curea & Ahmed Remaci & Haritza Camblong & Najiba Mrabet Bellaaj, 2018. "A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation," Energies, MDPI, vol. 11(3), pages 1-21, March.
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