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
EvMAPPER: High Altitude Orthomapping with Event Cameras
Fernando Cladera, Kenneth Chaney, M. Ani Hsieh, Camillo J. Taylor, Vijay Kumar
Navigation in a simplified Urban Flow through Deep Reinforcement Learning
Federica Tonti, Jean Rabault, Ricardo Vinuesa
Multi-UAV Enabled MEC Networks: Optimizing Delay through Intelligent 3D Trajectory Planning and Resource Allocation
Zhiying Wang, Tianxi Wei, Gang Sun, Xinyue Liu, Hongfang Yu, Dusit Niyato
Learning with Dynamics: Autonomous Regulation of UAV Based Communication Networks with Dynamic UAV Crew
Ran Zhang, Bowei Li, Liyuan Zhang, Jiang (Linda)Xie, Miao Wang
Predictive Covert Communication Against Multi-UAV Surveillance Using Graph Koopman Autoencoder
Sivaram Krishnan, Jihong Park, Gregory Sherman, Benjamin Campbell, Jinho Choi
Advance and Refinement: The Evolution of UAV Detection and Classification Technologies
Vladislav Semenyuk, Ildar Kurmashev, Alberto Lupidi, Dmitriy Alyoshin, Liliya Kurmasheva, Alessandro Cantelli-Forti
UAVDB: Trajectory-Guided Adaptable Bounding Boxes for UAV Detection
Yu-Hsi Chen
DWA-3D: A Reactive Planner for Robust and Efficient Autonomous UAV Navigation
Jorge Bes, Juan Dendarieta, Luis Riazuelo, Luis Montano