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
TF-Net: Deep Learning Empowered Tiny Feature Network for Night-time UAV Detection
Maham Misbah, Misha Urooj Khan, Zhaohui Yang, Zeeshan Kaleem
Simultaneous Spatial and Temporal Assignment for Fast UAV Trajectory Optimization using Bilevel Optimization
Qianzhong Chen, Sheng Cheng, Naira Hovakimyan
A Search and Detection Autonomous Drone System: from Design to Implementation
Mohammadjavad Khosravi, Rushiv Arora, Saeede Enayati, Hossein Pishro-Nik
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