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
SeaDroneSim: Simulation of Aerial Images for Detection of Objects Above Water
Xiaomin Lin, Cheng Liu, Allen Pattillo, Miao Yu, Yiannis Aloimonous
iTUAVs: Intermittently Tethered UAVs for Future Wireless Networks
Nesrine Cherif, Wael Jaafar, Evgenii Vinogradov, Halim Yanikomeroglu, Sofie Pollin, Abbas Yongacoglu
Probably Approximately Correct Nonlinear Model Predictive Control (PAC-NMPC)
Adam Polevoy, Marin Kobilarov, Joseph Moore
DroneARchery: Human-Drone Interaction through Augmented Reality with Haptic Feedback and Multi-UAV Collision Avoidance Driven by Deep Reinforcement Learning
Ekaterina Dorzhieva, Ahmed Baza, Ayush Gupta, Aleksey Fedoseev, Miguel Altamirano Cabrera, Ekaterina Karmanova, Dzmitry Tsetserukou
Road Network Deterioration Monitoring Using Aerial Images and Computer Vision
Nicolas Parra-A, Vladimir Vargas-Calderón, Herbert Vinck-Posada, Nicanor Vinck
Towards a Fully Autonomous UAV Controller for Moving Platform Detection and Landing
Michalis Piponidis, Panayiotis Aristodemou, Theocharis Theocharides