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
UAV-based Visual Remote Sensing for Automated Building Inspection
Kushagra Srivastava, Dhruv Patel, Aditya Kumar Jha, Mohhit Kumar Jha, Jaskirat Singh, Ravi Kiran Sarvadevabhatla, Pradeep Kumar Ramancharla, Harikumar Kandath, K. Madhava Krishna
Reducing safe UAV separation distances with U2U communication and new Remote ID formats
Evgenii Vinogradov, Sofie Pollin
Using Unmanned Aerial Systems (UAS) for Assessing and Monitoring Fall Hazard Prevention Systems in High-rise Building Projects
Yimeng Li, Behzad Esmaeili, Masoud Gheisari, Jana Kosecka, Abbas Rashidi
When Robotics Meets Wireless Communications: An Introductory Tutorial
Daniel Bonilla Licea, Mounir Ghogho, Martin Saska
Analysis of the Effect of Time Delay for Unmanned Aerial Vehicles with Applications to Vision Based Navigation
Muhammad Ahmed Humais, Mohamad Chehadeh, Igor Boiko, Yahya Zweiri
Indoor Path Planning for Multiple Unmanned Aerial Vehicles via Curriculum Learning
Jongmin Park, Kwansik Park