Path planning for quadrotor UAV using genetic algorithm
Path planning in quadrotor-typed UAV is essential in navigating from initial to destination point. This will minimize the power consumption of the vehicle which is important to avoid wasted energy in a given amount of time. This paper will use Genetic Algorithm (GA) to determine the shortest path th...
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oai:animorepository.dlsu.edu.ph:faculty_research-43662021-09-06T08:35:33Z Path planning for quadrotor UAV using genetic algorithm Galvez, Reagan L. Dadios, Elmer P. Bandala, Argel A. Path planning in quadrotor-typed UAV is essential in navigating from initial to destination point. This will minimize the power consumption of the vehicle which is important to avoid wasted energy in a given amount of time. This paper will use Genetic Algorithm (GA) to determine the shortest path that the quadrotor must travel given one target point to save energy and time without hitting an obstacle. The obstacle is assumed to be any point within the boundary. This algorithm is effective in searching solutions in a given sample space or population. If you know the possible solutions of the problem, you can evaluate it based on its fitness until the fittest individual arrives. © 2014 IEEE. 2014-01-01T08:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/3364 info:doi/10.1109/HNICEM.2014.7016260 https://animorepository.dlsu.edu.ph/context/faculty_research/article/4366/type/native/viewcontent/HNICEM.2014.7016260 Faculty Research Work Animo Repository Drone aircraft Quadrotor helicopters Genetic algorithms Manufacturing |
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Drone aircraft Quadrotor helicopters Genetic algorithms Manufacturing Galvez, Reagan L. Dadios, Elmer P. Bandala, Argel A. Path planning for quadrotor UAV using genetic algorithm |
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Path planning in quadrotor-typed UAV is essential in navigating from initial to destination point. This will minimize the power consumption of the vehicle which is important to avoid wasted energy in a given amount of time. This paper will use Genetic Algorithm (GA) to determine the shortest path that the quadrotor must travel given one target point to save energy and time without hitting an obstacle. The obstacle is assumed to be any point within the boundary. This algorithm is effective in searching solutions in a given sample space or population. If you know the possible solutions of the problem, you can evaluate it based on its fitness until the fittest individual arrives. © 2014 IEEE. |
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text |
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Galvez, Reagan L. Dadios, Elmer P. Bandala, Argel A. |
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Galvez, Reagan L. Dadios, Elmer P. Bandala, Argel A. |
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Galvez, Reagan L. |
title |
Path planning for quadrotor UAV using genetic algorithm |
title_short |
Path planning for quadrotor UAV using genetic algorithm |
title_full |
Path planning for quadrotor UAV using genetic algorithm |
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Path planning for quadrotor UAV using genetic algorithm |
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Path planning for quadrotor UAV using genetic algorithm |
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path planning for quadrotor uav using genetic algorithm |
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2014 |
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https://animorepository.dlsu.edu.ph/faculty_research/3364 https://animorepository.dlsu.edu.ph/context/faculty_research/article/4366/type/native/viewcontent/HNICEM.2014.7016260 |
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