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
Design Considerations of an Unmanned Aerial Vehicle for Aerial Filming
Dennis Casazola, Fabio Arnez, Huascar Espinoza
Deep Reinforcement Learning for Trajectory Path Planning and Distributed Inference in Resource-Constrained UAV Swarms
Marwan Dhuheir, Emna Baccour, Aiman Erbad, Sinan Sabeeh Al-Obaidi, Mounir Hamdi
UAVs for Industries and Supply Chain Management
Shrutarv Awasthi, Nils Gramse, Dr. Christopher Reining, Dr. Moritz Roidl
Reinforcement Learning for UAV control with Policy and Reward Shaping
Cristian Millán-Arias, Ruben Contreras, Francisco Cruz, Bruno Fernandes
UAS Simulator for Modeling, Analysis and Control in Free Flight and Physical Interaction
Azarakhsh Keipour, Mohammadreza Mousaei, Dongwei Bai, Junyi Geng, Sebastian Scherer