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Genetic algorithm-based path planning of quadrotor UAVs on a 3D environment

Published online by Cambridge University Press:  16 December 2024

M.A. Gutierrez-Martinez ,
E.G. Rojo-Rodriguez ,
L.E. Cabriales-Ramirez ,
K. Estabridis  and
O. Garcia-Salazar Open the ORCID record for O. Garcia-Salazar [Opens in a new window]
M.A. Gutierrez-Martinez
Affiliation:
Aerospace Engineering Research and Innovation Center, Faculty of Mechanical and Electrical Engineering, Autonomous University of Nuevo Leon, Apodaca, Nuevo Leon, Mexico
E.G. Rojo-Rodriguez
Affiliation:
Aerospace Engineering Research and Innovation Center, Faculty of Mechanical and Electrical Engineering, Autonomous University of Nuevo Leon, Apodaca, Nuevo Leon, Mexico
L.E. Cabriales-Ramirez
Affiliation:
Aerospace Engineering Research and Innovation Center, Faculty of Mechanical and Electrical Engineering, Autonomous University of Nuevo Leon, Apodaca, Nuevo Leon, Mexico
K. Estabridis
Affiliation:
Naval Air Warfare Center Weapons Division, Research Department, CA, USA
O. Garcia-Salazar*
Affiliation:
Aerospace Engineering Research and Innovation Center, Faculty of Mechanical and Electrical Engineering, Autonomous University of Nuevo Leon, Apodaca, Nuevo Leon, Mexico
*
Corresponding author: Garcia-Salazar; Email: [email protected]
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Abstract

In this article, a genetic algorithm (GA) is proposed as a solution for the path planning of unmanned aerial vehicles (UAVs) in 3D, both static and dynamic environments. In most cases, genetic algorithms are utilised for optimisation in offline applications; however, this work proposes an approach that performs real-time path planning with the capability to avoid dynamic obstacles. The proposed method is based on applying a genetic algorithm to find optimised trajectories in changing static and dynamic environments. The genetic algorithm considers genetic operators that are employed for path planning, along with high mutation criteria, the population of convergence, repopulation criteria and the incorporation of the destination point within the population. The effectiveness of this approach is validated through results obtained from both simulations and experiments, demonstrating that the genetic algorithm ensures efficient path planning and the ability to effectively avoid static and dynamic obstacles. A genetic algorithm for path planning of UAVs is proposed, achieving optimised paths in both static and dynamic environments for real-time tasks. In addition, this path planning algorithm has the properties to avoid static and moving obstacles in real-time environments.

Keywords

path planninggenetic algorithmUAVsobstacle avoidance
Type
Research Article
Information
The Aeronautical Journal , Volume 129 , Issue 1334 , April 2025 , pp. 902 - 938
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Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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