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
Genetic Algorithm Based System for Path Planning with Unmanned Aerial Vehicles Swarms in Cell-Grid Environments
Alejandro Puente-Castro, Enrique Fernandez-Blanco, Daniel Rivero
Real-Time AIoT for UAV Antenna Interference Detection via Edge-Cloud Collaboration
Jun Dong, Jintao Cheng, Jin Wu, Chengxi Zhang, Shunyi Zhao, Xiaoyu Tang
Proximal Control of UAVs with Federated Learning for Human-Robot Collaborative Domains
Lucas Nogueira Nobrega, Ewerton de Oliveira, Martin Saska, Tiago Nascimento
Bio-inspired visual relative localization for large swarms of UAVs
Martin Křížek, Matouš Vrba, Antonella Barišić Kulaš, Stjepan Bogdan, Martin Saska
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