Optimum flight application of flock UAVs in search and rescue activities

With the technology developing day by day, Unmanned Aerial Vehicle (UAV) technology has started to be used and developed in challenging tasks such as post-disaster search and rescue activities. As developments continue, larger areas can be tracked in shorter times with a kind of swarm of multiple UAVs instead of a single UAV. With all these developments, it is planned to work autonomously by creating smarter routes than UAV swarms. In this way, results are achieved faster in difficult tasks such as disasters. In this study, a preformed fire disaster scenario was studied. According to the scenario, the coordinates where the UAVs start their flight, the fire center coordinate and the border coordinates that will be the search space have been determined. In order to realize the scenario environment, the data planned to be received by the sensors was created virtually. UAVs, which started their flights from the starting point, tried to approach the fire area with the data received from the virtual sensors and optimized the problem with the maximum approach in the minimum time. The importance of using heuristic algorithms is investigated in a real problem by comparing the scanning of the entire region as a grid with UAVs and the routes created using Particle Swarm Optimization (PSO) and Artificial Algae algorithm (AAA) heuristic algorithms. AAA and PSO algorithms were also compared with each other on the determined scenario and the results were examined. In order for the project to be compatible with the real environment, the simulation environment of the ground control station software called Mission Planner was used.