Autonomous Micro Aerial Vehicle

Autonomous micro aerial vehicles (MAVs) are increasingly studied for their potential in diverse applications, focusing on achieving robust and efficient autonomous navigation, perception, and task execution in complex environments. Current research emphasizes the development of advanced control algorithms, such as model predictive control and reinforcement learning, often integrated with computer vision techniques (e.g., convolutional neural networks, visual-inertial odometry) and deep learning for tasks like search and rescue, 3D mapping, and infrastructure inspection. These advancements are driving significant progress in areas like safe airspace management, precision agriculture, and improved efficiency in various industrial settings.