Evolutionary computation based mission planning in swarms of unmanned aerial vehicles

Unmanned aerial vehicles are used in lots of areas thanks to the advantages which they offer. Unmanned aerial vehicles can be more efficient when they are used with swarm systems. In order to make swarm systems more efficient, mission planning of the swarm must be done appropriately. When mission planning algorithms are examined in literature, it can be seen that evolutionary computation algorithms rise to prominence. Within the scope of the thesis, two software have been designed to use unmanned aerial vehicle swarms more effectively and efficiently. The designed software are simulation software and mission planning software. Using simulation software, unmanned aerial vehicle swarms can be created and an unmanned aerial vehicle swarm whose mission planning is done can be simulated. Using mission planning software, mission planning of a swarm system can be done with evolutionary computation algorithms. These two software run integrated. In this work, architecture of the software are explained in details. Experiments were done for different swarm sizes and different evolutionary computation algorithms. In the experiments, the algorithms were compared. Results of the experiments show that the designed software can run different swarm sizes and different evolutionary computation algorithms.