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
Bio-Inspired Compact Swarms of Unmanned Aerial Vehicles without Communication and External Localization
Pavel Petracek, Viktor Walter, Tomas Baca, Martin Saska
Large-Scale Exploration of Cave Environments by Unmanned Aerial Vehicles
Pavel Petracek, Vit Kratky, Matej Petrlik, Tomas Baca, Radim Kratochvil, Martin Saska
Multi-Start Team Orienteering Problem for UAS Mission Re-Planning with Data-Efficient Deep Reinforcement Learning
Dong Ho Lee, Jaemyung Ahn
Autonomous Aerial Filming With Distributed Lighting by a Team of Unmanned Aerial Vehicles
Vít Krátký, Alfonso Alcántara, Jesús Capitán, Petr Štěpán, Martin Saska, Aníbal Ollero
Autonomous Reflectance Transformation Imaging by a Team of Unmanned Aerial Vehicles
Vít Krátký, Pavel Petráček, Vojtěch Spurný, Martin Saska
Multi-UAV Adaptive Path Planning Using Deep Reinforcement Learning
Jonas Westheider, Julius Rückin, Marija Popović