Genetic algorithm based 3D motion planning for unmanned aerial vehicle

• Main Authors: Rivera, Maverick C., Del Rosario, Jay Robert B., Bandala, Argel A.
• Format: text
• Published: Animo Repository 2019
• Subjects: Drone aircraft in remote sensing; Drone aircraft; Genetic algorithms; Electrical and Computer Engineering; Electrical and Electronics
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2079; https://animorepository.dlsu.edu.ph/context/faculty_research/article/3078/type/native/viewcontent
• Institution: De La Salle University

Development of Unmanned Aerial Vehicle (UAV) is now a popular field in research. In most of its applications, a pathfinding algorithm is needed in order to find the optimal path and avoid obstacles. In this paper, a genetic algorithm is implemented in order to determine the optimal path for a UAV that will avoid obstacles along the way. The genetic algorithm implemented uses variable-length chromosomes to solve the problem. The results of the simulation of the system yield an average of 29 generations and avoided 53, 500 collisions to find the best path. © 2019 IEEE.