Obaid, Amro AliKoyuncu, Hakan2022-12-232022-12-232022Obaid, A. A., Koyuncu, H. (2022). Obstacle avoidance in unmanned aerial vehicles using image segmentation and deep learning. In 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 912-915). IEEE.9781665470131https://hdl.handle.net/20.500.12939/3147Machine learning is a branch of artificial intelligence based on the idea that systems can learn to identify patterns and make decisions with a minimum of human intervention. In this study, demonstration learning will be used, using neural networks in a prototype of a drone built to perform trajectories in controlled environments. To accelerate the training convergence process, a new training data selection approach has been introduced, which picks data from the experience pool based on priority instead of randomness. An autonomous maneuver strategy for dual-UAV olive formation air warfare is provided, which makes use of UAV capabilities such as obstacle avoidance, formation, and confrontation to maximize the effectiveness of the attack.eninfo:eu-repo/semantics/closedAccessLEACHPSORoutingWSNConference Object9129152-s2.0-85142828446N/A