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
Cooperative Multi-Agent Planning Framework for Fuel Constrained UAV-UGV Routing Problem
Md Safwan Mondal, Subramanian Ramasamy, James D. Humann, Jean-Paul F. Reddinger, James M. Dotterweich, Marshal A. Childers, Pranav A. Bhounsule
Learning to Recharge: UAV Coverage Path Planning through Deep Reinforcement Learning
Mirco Theile, Harald Bayerlein, Marco Caccamo, Alberto L. Sangiovanni-Vincentelli
Compressing Vision Transformers for Low-Resource Visual Learning
Eric Youn, Sai Mitheran J, Sanjana Prabhu, Siyuan Chen
Personalized Federated Deep Reinforcement Learning-based Trajectory Optimization for Multi-UAV Assisted Edge Computing
Zhengrong Song, Chuan Ma, Ming Ding, Howard H. Yang, Yuwen Qian, Xiangwei Zhou