Obstacle Avoidance in Unmanned Aerial Vehicles Using Image Segmentation and Deep Learning

Machine 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.