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
Constrained multi-objective optimization for multi-UAV planning
Cristian Ramirez-Atencia, David Camacho
Environmental Awareness Dynamic 5G QoS for Retaining Real Time Constraints in Robotic Applications
Gerasimos Damigos, Akshit Saradagi, Sara Sandberg, George Nikolakopoulos
Dynamic Q-planning for Online UAV Path Planning in Unknown and Complex Environments
Lidia Gianne Souza da Rocha, Kenny Anderson Queiroz Caldas, Marco Henrique Terra, Fabio Ramos, Kelen Cristiane Teixeira Vivaldini
Graph Koopman Autoencoder for Predictive Covert Communication Against UAV Surveillance
Sivaram Krishnan, Jihong Park, Gregory Sherman, Benjamin Campbell, Jinho Choi
Control-Aware Trajectory Predictions for Communication-Efficient Drone Swarm Coordination in Cluttered Environments
Longhao Yan, Jingyuan Zhou, Kaidi Yang
Classification of grapevine varieties using UAV hyperspectral imaging
Alfonso López, Carlos Javier Ogayar, Francisco Ramón Feito, Joaquim João Sousa