Control of Industrial AGV Based on Reinforcement Learn

  1. Jesus Enrique Sierra-García 1
  2. Matilde Santos 2
  1. 1 Universidad de Burgos
    info

    Universidad de Burgos

    Burgos, España

    ROR https://ror.org/049da5t36

  2. 2 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

Libro:
15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020): Burgos, Spain ; September 2020
  1. Álvaro Herrero (coord.)
  2. Carlos Cambra (coord.)
  3. Daniel Urda (coord.)
  4. Javier Sedano (coord.)
  5. Héctor Quintián (coord.)
  6. Emilio Corchado (coord.)

Editorial: Springer Suiza

ISBN: 978-3-030-57801-5,  978-3-030-57802-2

Año de publicación: 2021

Páginas: 647-656

Congreso: International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO (15. 2020. Burgos)

Tipo: Aportación congreso

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Resumen

Automatic Guided Vehicles (AGV) suffer degradation in their electromechanical components which affect the navigation performance over time. The use of intelligent control techniques can help to alleviate this issue. In this work a new approach to control an AGV based on reinforcement learning (RL) is proposed. The space of states is defined using the guiding error, and the set of control actions provides the reference for the velocities of each wheel. Two different reward strategies are implemented, and different updating policies are tested. Simulation results show how the RL controller is able to successfully track a complex trajectory. The controller has been compared with a PID obtaining better results.

Fuente de los datos: Dialnet