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
Extending QGroundControl for Automated Mission Planning of UAVs
Cristian Ramirez-Atencia, David Camacho
Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning
Cristian Ramirez-Atencia, Javier Del Ser, David Camacho
A revision on Multi-Criteria Decision Making methods for Multi-UAV Mission Planning Support
Cristian Ramirez-Atencia, Victor Rodriguez-Fernandez, David Camacho
Human-Centric Aware UAV Trajectory Planning in Search and Rescue Missions Employing Multi-Objective Reinforcement Learning with AHP and Similarity-Based Experience Replay
Mahya Ramezani, Jose Luis Sanchez-Lopez
Redefining Aerial Innovation: Autonomous Tethered Drones as a Solution to Battery Life and Data Latency Challenges
Samuel O. Folorunsho, William R. Norris