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Dynamical Analysis of a Navigation Algorithm

Author

Listed:
  • Mireya Cabezas-Olivenza

    (System Engineering and Automation Control Department, University of the Basque Country (UPV/EHU), Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain)

  • Ekaitz Zulueta

    (System Engineering and Automation Control Department, University of the Basque Country (UPV/EHU), Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain)

  • Ander Sánchez-Chica

    (System Engineering and Automation Control Department, University of the Basque Country (UPV/EHU), Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain)

  • Adrian Teso-Fz-Betoño

    (System Engineering and Automation Control Department, University of the Basque Country (UPV/EHU), Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain)

  • Unai Fernandez-Gamiz

    (Department of Nuclear and Fluid Mechanics, University of the Basque Country (UPV/EHU), Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain)

Abstract

There is presently a need for more robust navigation algorithms for autonomous industrial vehicles. These have reasonably guaranteed the adequate reliability of the navigation. In the current work, the stability of a modified algorithm for collision-free guiding of this type of vehicle is ensured. A lateral control and a longitudinal control are implemented. To demonstrate their viability, a stability analysis employing the Lyapunov method is carried out. In addition, this mathematical analysis enables the constants of the designed algorithm to be determined. In conjunction with the navigation algorithm, the present work satisfactorily solves the localization problem, also known as simultaneous localization and mapping (SLAM). Simultaneously, a convolutional neural network is managed, which is used to calculate the trajectory to be followed by the AGV, by implementing the artificial vision. The use of neural networks for image processing is considered to constitute the most robust and flexible method for realising a navigation algorithm. In this way, the autonomous vehicle is provided with considerable autonomy. It can be regarded that the designed algorithm is adequate, being able to trace any type of path.

Suggested Citation

  • Mireya Cabezas-Olivenza & Ekaitz Zulueta & Ander Sánchez-Chica & Adrian Teso-Fz-Betoño & Unai Fernandez-Gamiz, 2021. "Dynamical Analysis of a Navigation Algorithm," Mathematics, MDPI, vol. 9(23), pages 1-20, December.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:23:p:3139-:d:695838
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    Citations

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    Cited by:

    1. Mireya Cabezas-Olivenza & Ekaitz Zulueta & Ander Sanchez-Chica & Unai Fernandez-Gamiz & Adrian Teso-Fz-Betoño, 2022. "Stability Analysis for Autonomous Vehicle Navigation Trained over Deep Deterministic Policy Gradient," Mathematics, MDPI, vol. 11(1), pages 1-27, December.
    2. Miguel Clavijo & Felipe Jiménez & Francisco Serradilla & Alberto Díaz-Álvarez, 2022. "Assessment of CNN-Based Models for Odometry Estimation Methods with LiDAR," Mathematics, MDPI, vol. 10(18), pages 1-19, September.
    3. Agnieszka A. Tubis & Honorata Poturaj, 2022. "Risk Related to AGV Systems—Open-Access Literature Review," Energies, MDPI, vol. 15(23), pages 1-23, November.

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