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
NDOB-Based Control of a UAV with Delta-Arm Considering Manipulator Dynamics
Hongming Chen, Biyu Ye, Xianqi Liang, Weiliang Deng, Ximin Lyu
TakuNet: an Energy-Efficient CNN for Real-Time Inference on Embedded UAV systems in Emergency Response Scenarios
Daniel Rossi, Guido Borghi, Roberto Vezzani
Diffusion Models for Smarter UAVs: Decision-Making and Modeling
Yousef Emami, Hao Zhou, Luis Almeida, Kai Li
Navigation Variable-based Multi-objective Particle Swarm Optimization for UAV Path Planning with Kinematic Constraints
Thi Thuy Ngan Duong, Duy-Nam Bui, Manh Duong Phung
UAV-DETR: Efficient End-to-End Object Detection for Unmanned Aerial Vehicle Imagery
Huaxiang Zhang, Kai Liu, Zhongxue Gan, Guo-Niu Zhu
When UAV Meets Federated Learning: Latency Minimization via Joint Trajectory Design and Resource Allocation
Xuhui Zhang, Wenchao Liu, Jinke Ren, Huijun Xing, Gui Gui, Yanyan Shen, Shuguang Cui
Model predictive control-based trajectory generation for agile landing of unmanned aerial vehicle on a moving boat
Ondřej Procházka, Filip Novák, Tomáš Báča, Parakh M. Gupta, Robert Pěnička, Martin Saska