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
RaCIL: Ray Tracing based Multi-UAV Obstacle Avoidance through Composite Imitation Learning
Harsh Bansal, Vyom Goyal, Bhaskar Joshi, Akhil Gupta, Harikumar Kandath
Quantum Multi-Agent Reinforcement Learning for Cooperative Mobile Access in Space-Air-Ground Integrated Networks
Gyu Seon Kim, Yeryeong Cho, Jaehyun Chung, Soohyun Park, Soyi Jung, Zhu Han, Joongheon Kim
QUADFormer: Learning-based Detection of Cyber Attacks in Quadrotor UAVs
Pengyu Wang, Zhaohua Yang, Nachuan Yang, Zikai Wang, Jialu Li, Fan Zhang, Chaoqun Wang, Jiankun Wang, Max Q. -H. Meng, Ling Shi
The Future of Aerial Communications: A Survey of IRS-Enhanced UAV Communication Technologies
Zina Chkirbene, Ala Gouissem, Ridha Hamila, Devrim Unal