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
Deployment of Reliable Visual Inertial Odometry Approaches for Unmanned Aerial Vehicles in Real-world Environment
Jan Bednář, Matěj Petrlík, Kelen Cristiane Teixeira Vivaldini, Martin Saska
SphereMap: Dynamic Multi-Layer Graph Structure for Rapid Safety-Aware UAV Planning
Tomáš Musil, Matěj Petrlík, Martin Saska
Fast and Noise-Resilient Magnetic Field Mapping on a Low-Cost UAV Using Gaussian Process Regression
Prince E. Kuevor, Maani Ghaffari, Ella M. Atkins, James W. Cutler
A Kubernetes-Based Edge Architecture for Controlling the Trajectory of a Resource-Constrained Aerial Robot by Enabling Model Predictive Control
Achilleas Santi Seisa, Sumeet Gajanan Satpute, George Nikolakopoulos
Nonlinear MPC for Full-Pose Manipulation of a Cable-Suspended Load using Multiple UAVs
Sihao Sun, Antonio Franchi
Towards Multi-robot Exploration: A Decentralized Strategy for UAV Forest Exploration
Luca Bartolomei, Lucas Teixeira, Margarita Chli
One-shot Generative Data Augmentation with Bounded Divergence for UAV Identification in Limited RF Environments
Amir Kazemi, Salar Basiri, Volodymyr Kindratenko, Srinivasa Salapaka