Genetic algorithm based 3D motion planning for unmanned aerial vehicle

Genetic algorithm based 3D motion planning for unmanned aerial vehicle

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 th...

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Bibliographic Details
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
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Institution: De La Salle University
Description
Summary: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.

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