Algılama ve haberleşme kısıtları altında çoklu-İHA sistemi ile işbirlikçi arama

Multi-Unmanned Aerial Vehicle (UAV) systems have many uses in civil applications including Search and Rescue (SaR), surveillance, and environmental monitoring. In dynamic missions such as SaR, where multiple targets or areas of interest might need to be searched and monitored, the accuracy of target detection and information sharing among UAVs and with Ground Control Station (GCS) play a vital role in whether the mission will be successful or not. The state of the art sensors are still imperfect, hence, UAVs might need to take multiple measurement in each area of interest to increase the accuracy of sensor measurements. To that end, in this thesis we propose a joint area coverage, connectivity, and revisit time optimization framework within the context of multi-UAV multi-target cooperative search missions involving dynamic information exchange under imperfect sensing and communication constraints towards the goal of shortening time elapsed between consecutive visits to be able to take multiple sensor measurements as fast as possible (without suffering great losses in performance in terms of area coverage and connectivity), therefore increasing the accuracy of sensor measurements in the early stages of the mission in question. We utilize the Non-Dominated Sorting Genetic Algorithm (NSGA)-II evolutionary algorithm to solve the proposed optimization problem and compare our results on various scenarios. Our results show that by considering cell revisit times as well as connectivity in the path planning in the context of evolutionary algorithms, we observe up to 4-fold improvement in terms of observed times between visits and we see that the target detection times can be improved by 60% and we can achieve instant GCS inform post-detection in realistic, uncertain environments. Keywords: search and rescue, area coverage, drone networks, unmanned aerial vehicles, path planning, maintaining connectivity, multi-visit, target detection