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UAV Path Planning with Parallel Genetic Algorithms on CUDA Architecture

Ugur Cekmez, Mustafa Ozsiginan, Musa Aydin, Ozgur Koray Sahingoz
Department of Computer Engineering, Yildiz Technical University, Istanbul, Turkey
Yildiz Technical University, 2014

@article{ccekmez2014uav,

   title={UAV Path Planning with Parallel Genetic Algorithms on CUDA Architecture},

   author={c{C}}ekmez, U{u{g}}ur and {"O}zs{i}{u{g}}{i}nan, Mustafa and Ayd{i}n, Musa and {c{S}}ahing{"o}z, {"O}zg{"u}r Koray},

   year={2014},

   publisher={IAENG}

}

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In recent years, Unmanned Aerial Vehicles (UAVs) are emerged as an attractive technology for different types of military and civil applications which have gained importance in academic researches. In these emerging research areas, UAV autonomy gets a great part and mainly it refers the ability for automatic take-off, landing and path planning of UAVs. In this paper, we focused of the path planning of UAVs for controlling a number of waypoints in the mission area. If the area is large and the number of points that must be checked is greater, then it is not possible to check every possible solution, therefore, we have to use some efficient algorithms, like genetic algorithms (GAs), to calculate the path. However if the number of these points exceeds a certain number, then we have to use some additional accelerating mechanisms to speed up the calculation time. Typically two techniques are used for speeding up: parallelization and distribution of calculation. In this paper genetic algorithm is parallelized on CUDA architecture by using Graphical Processing Units (GPUs). Experimental results showed that this approach produces efficient solutions in a short time.
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