@inproceedings{c11e58cf60054d209eef413a21a15cd4,
title = "A vision-based auxiliary system of multirotor unmanned aerial vehicles for autonomous rendezvous and docking",
abstract = "Unmanned aerial vehicles (UAVs) are versatile in maneuverability for both civilian and military applications. To facilitate the long-term tasks of UAVs, autonomous rendezvous and docking (ARaD) will be a need in the emerging field of UAV research. In this paper, we proposed a vision-based auxiliary system (VAS) for multirotor UAVs to implement autonomous rendezvous and docking. The VAS consists of image acquisition and processing unit, wireless communication unit, and tracking and docking control unit. Continuously adaptive mean shift (CamShift) algorithm was applied for tracking the target and obtaining its 3D coordinates. A specific Zigbee protocol was designed to ensure the steady and rapid transmission of status data between UAVs and the ground station. A straight-forward tracking and docking control algorithm was proposed to assist the rendezvous and docking between the two UAVs. Physical simulation experiments were performed by two six-rotor rotorcrafts, which demonstrate the feasibility and practicability of our proposed vision-based auxiliary system for the future application.",
keywords = "Automatic refueling, Camshift, Tracking, UAVs, Wireless communication",
author = "Dexing Zhong and Xuefei Zhang and Haotian Sun and Zhigang Ren and Jiuqiang Han",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 International Joint Conference on Neural Networks, IJCNN 2016 ; Conference date: 24-07-2016 Through 29-07-2016",
year = "2016",
month = oct,
day = "31",
doi = "10.1109/IJCNN.2016.7727801",
language = "英语",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4586--4592",
booktitle = "2016 International Joint Conference on Neural Networks, IJCNN 2016",
}