Various Fast Moving Drone
Research on fast-moving drones focuses on developing autonomous systems capable of efficient and safe navigation, particularly in complex environments like dense canopies or GPS-denied spaces. Current efforts concentrate on optimizing 3D trajectory planning, employing algorithms like genetic algorithms, graph attention networks, and model predictive control, often coupled with advanced sensor fusion and computer vision techniques for tasks such as object detection, tracking, and mapping. This research is significant for advancing drone capabilities in diverse applications, including agriculture, delivery, infrastructure inspection, and search and rescue, improving efficiency and safety in these domains.
Papers
Towards Building Secure UAV Navigation with FHE-aware Knowledge Distillation
Arjun Ramesh Kaushik, Charanjit Jutla, Nalini Ratha
Diffusion-based Auction Mechanism for Efficient Resource Management in 6G-enabled Vehicular Metaverses
Jiawen Kang, Yongju Tong, Yue Zhong, Junlong Chen, Minrui Xu, Dusit Niyato, Runrong Deng, Shiwen Mao
AIVIO: Closed-loop, Object-relative Navigation of UAVs with AI-aided Visual Inertial Odometry
Thomas Jantos, Martin Scheiber, Christian Brommer, Eren Allak, Stephan Weiss, Jan Steinbrener
3D UAV Trajectory Planning for IoT Data Collection via Matrix-Based Evolutionary Computation
Pei-Fa Sun, Yujae Song, Kang-Yu Gao, Yu-Kai Wang, Changjun Zhou, Sang-Woon Jeon, Jun Zhang
Towards Safe and Efficient Through-the-Canopy Autonomous Fruit Counting with UAVs
Teaya Yang, Roman Ibrahimov, Mark W. Mueller
Multi-UAV Enabled MEC Networks: Optimizing Delay through Intelligent 3D Trajectory Planning and Resource Allocation
Zhiying Wang, Tianxi Wei, Gang Sun, Xinyue Liu, Hongfang Yu, Dusit Niyato