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Modelling an Industrial Robot and Its Impact on Productivity

Author

Listed:
  • Carlos Llopis-Albert

    (Centro de Investigación en Ingeniería Mecánica (CIIM), Universitat Politècnica de València—Camino de Vera s/n, 46022 Valencia, Spain)

  • Francisco Rubio

    (Centro de Investigación en Ingeniería Mecánica (CIIM), Universitat Politècnica de València—Camino de Vera s/n, 46022 Valencia, Spain)

  • Francisco Valero

    (Centro de Investigación en Ingeniería Mecánica (CIIM), Universitat Politècnica de València—Camino de Vera s/n, 46022 Valencia, Spain)

Abstract

This research aims to design an efficient algorithm leading to an improvement of productivity by posing a multi-objective optimization, in which both the time consumed to carry out scheduled tasks and the associated costs of the autonomous industrial system are minimized. The algorithm proposed models the kinematics and dynamics of the industrial robot, provides collision-free trajectories, allows to constrain the energy consumed and meets the physical characteristics of the robot (i.e., restriction on torque, jerks and power in all driving motors). Additionally, the trajectory tracking accuracy is improved using an adaptive fuzzy sliding mode control (AFSMC), which allows compensating for parametric uncertainties, bounded external disturbances and constraint uncertainties. Therefore, the system stability and robustness are enhanced; thus, overcoming some of the limitations of the traditional proportional-integral-derivative (PID) controllers. The trade-offs among the economic issues related to the assembly line and the optimal time trajectory of the desired motion are analyzed using Pareto fronts. The technique is tested in different examples for a six-degrees-of-freedom (DOF) robot system. Results have proved how the use of this methodology enhances the performance and reliability of assembly lines.

Suggested Citation

  • Carlos Llopis-Albert & Francisco Rubio & Francisco Valero, 2021. "Modelling an Industrial Robot and Its Impact on Productivity," Mathematics, MDPI, vol. 9(7), pages 1-13, April.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:7:p:769-:d:528640
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    References listed on IDEAS

    as
    1. Llopis-Albert, Carlos & Rubio, Francisco & Valero, Francisco, 2015. "Improving productivity using a multi-objective optimization of robotic trajectory planning," Journal of Business Research, Elsevier, vol. 68(7), pages 1429-1431.
    2. Carlos Llopis-Albert & Francisco Valero & Vicente Mata & José L. Pulloquinga & Pau Zamora-Ortiz & Rafael J. Escarabajal, 2020. "Optimal Reconfiguration of a Parallel Robot for Forward Singularities Avoidance in Rehabilitation Therapies. A Comparison via Different Optimization Methods," Sustainability, MDPI, vol. 12(14), pages 1-18, July.
    3. Francisco Rubio & Carlos Llopis-Albert & Francisco Valero & Josep Lluís Suñer, 2015. "Assembly Line Productivity Assessment by Comparing Optimization-Simulation Algorithms of Trajectory Planning for Industrial Robots," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-10, June.
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    Cited by:

    1. Peng Liu & Haibo Tian & Xiangang Cao & Xuhui Zhang & Xinzhou Qiao & Yu Su, 2022. "Dynamic Stability Measurement and Grey Relational Stability Sensitivity Analysis Methods for High-Speed Long-Span 4-1 Cable Robots," Mathematics, MDPI, vol. 10(24), pages 1-20, December.

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