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Trajectories Generation for Unmanned Aerial Vehicles Based on Obstacle Avoidance Located by a Visual Sensing System

Author

Listed:
  • Luis Felipe Muñoz Mendoza

    (Departamento de Electro–Fotónica, Centro Universitario de Ciencias Exactas e Ingenierías (C.U.C.E.I.), Universidad de Guadalajara (U. de G.), Blvd. M. García Barragán 1421, Guadalajara 44410, Jalisco, Mexico)

  • Guillermo García-Torales

    (Departamento de Electro–Fotónica, Centro Universitario de Ciencias Exactas e Ingenierías (C.U.C.E.I.), Universidad de Guadalajara (U. de G.), Blvd. M. García Barragán 1421, Guadalajara 44410, Jalisco, Mexico)

  • Cuauhtémoc Acosta Lúa

    (Departamento de Ciencias Tecnológicas, Centro Universitario de La Ciénega, Universidad de Guadalajara, Av. Universidad 1115, Ocotlán 47820, Jalisco, Mexico
    Center of Excellence DEWS, University of L’Aquila, Via Vetoio, Loc. Coppito, 67100 L’Aquila, Italy)

  • Stefano Di Gennaro

    (Center of Excellence DEWS, University of L’Aquila, Via Vetoio, Loc. Coppito, 67100 L’Aquila, Italy
    Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila, Via Vetoio, Loc. Coppito, 67100 L’Aquila, Italy)

  • José Trinidad Guillen Bonilla

    (Departamento de Electro–Fotónica, Centro Universitario de Ciencias Exactas e Ingenierías (C.U.C.E.I.), Universidad de Guadalajara (U. de G.), Blvd. M. García Barragán 1421, Guadalajara 44410, Jalisco, Mexico)

Abstract

In this work, vectorial trajectories for unmanned aerial vehicles are completed based on a new algorithm named trajectory generation based on object avoidance (TGBOA), which is presented using a UAV camera as a visual sensor to define collision-free trajectories in scenarios with randomly distributed objects. The location information of the objects is collected by the visual sensor and processed in real-time. This proposal has two advantages. First, this system improves efficiency by focusing the algorithm on object detection and drone position, thus reducing computational complexity. Second, online trajectory references are generated and updated in real-time. To define a collision-free trajectory and avoid a collision between the UAV and the detected object, a reference is generated and shown by the vector, symmetrical, and parametric equations. Such vectors are used as a reference in a PI-like controller based on the Newton–Euler mathematical model. Experimentally, the TGBOA algorithm is corroborated by developing three experiments where the F-450 quadcopter, MATLAB ® 2022ª, PI-like controller, and Wi-Fi communication are applied. The TGBOA algorithm and the PI-like controller show functionality because the controller always follows the vector generated due to the obstacle avoidance.

Suggested Citation

  • Luis Felipe Muñoz Mendoza & Guillermo García-Torales & Cuauhtémoc Acosta Lúa & Stefano Di Gennaro & José Trinidad Guillen Bonilla, 2023. "Trajectories Generation for Unmanned Aerial Vehicles Based on Obstacle Avoidance Located by a Visual Sensing System," Mathematics, MDPI, vol. 11(6), pages 1-25, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1413-:d:1097518
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    References listed on IDEAS

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    1. José Trinidad Guillén-Bonilla & Claudia Carolina Vaca García & Stefano Di Gennaro & María Eugenia Sánchez Morales & Cuauhtémoc Acosta Lúa, 2020. "Vision-Based Nonlinear Control of Quadrotors Using the Photogrammetric Technique," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-10, November.
    2. Wenhui Wu & Xin Jin & Yang Tang, 2020. "Vision-based trajectory tracking control of quadrotors using super twisting sliding mode control," Cyber-Physical Systems, Taylor & Francis Journals, vol. 6(4), pages 207-230, October.
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