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
Planning and Perception for Unmanned Aerial Vehicles in Object and Environmental Monitoring
Harnaik Dhami
Is That Rain? Understanding Effects on Visual Odometry Performance for Autonomous UAVs and Efficient DNN-based Rain Classification at the Edge
Andrea Albanese, Yanran Wang, Davide Brunelli, David Boyle
Fusion Flow-enhanced Graph Pooling Residual Networks for Unmanned Aerial Vehicles Surveillance in Day and Night Dual Visions
Alam Noor, Kai Li, Eduardo Tovar, Pei Zhang, Bo Wei
MIXED-SENSE: A Mixed Reality Sensor Emulation Framework for Test and Evaluation of UAVs Against False Data Injection Attacks
Kartik A. Pant, Li-Yu Lin, Jaehyeok Kim, Worawis Sribunma, James M. Goppert, Inseok Hwang
Cooperative Indoor Exploration Leveraging a Mixed-Size UAV Team with Heterogeneous Sensors
Michaela Cihlářová, Václav Pritzl, Martin Saska