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

Luis Felipe Muñoz Mendoza, Guillermo García-Torales, Cuauhtémoc Acosta Lúa, Stefano Di Gennaro and José Trinidad Guillen Bonilla ()
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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
Stefano Di Gennaro: Center of Excellence DEWS, 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

Mathematics, 2023, vol. 11, issue 6, 1-25

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.

Keywords: TGBOA; UAV; trajectory generation; obstacle avoidance; Newton–Euler model; PI-like controller (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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