Unmanned Aerial Vehicle
Unmanned Aerial Vehicles (UAVs), or drones, are increasingly used for diverse applications, driving research focused on improving their autonomy, safety, and efficiency. Current research emphasizes robust navigation and control in complex environments, employing techniques like nonlinear model predictive control and advanced search algorithms for path planning, often coupled with deep learning models (e.g., YOLO, U-Net) for perception and object detection. These advancements are crucial for expanding UAV capabilities in sectors such as agriculture, search and rescue, and infrastructure monitoring, while also addressing critical concerns like security and reliable operation in challenging conditions (e.g., GPS-denied environments, harsh weather).
Papers
Depth-CUPRL: Depth-Imaged Contrastive Unsupervised Prioritized Representations in Reinforcement Learning for Mapless Navigation of Unmanned Aerial Vehicles
Junior Costa de Jesus, Victor Augusto Kich, Alisson Henrique Kolling, Ricardo Bedin Grando, Rodrigo da Silva Guerra, Paulo Lilles Jorge Drews
Automated Wheat Disease Detection using a ROS-based Autonomous Guided UAV
Behzad Safarijalal, Yousef Alborzi, Esmaeil Najafi