Multi-unmanned aerial vehicle (UAV) cooperative communication for visual navigation has recently generated
significant concern. It has large amounts of visual information to be transmitted and processed among UAVs with realtime
requirements. And the UAV clusters have self-organized, time-varying and high dynamic characteristics.
Considering the above conditions, we propose an adaptive information interactive mechanism (AIIM) for multi-UAV
visual navigation. In the mechanism, the function modules for UAV inter-communication interface are designed, the
mobility-based link lifetime is established and the information interactive protocol is presented. Thus we combine the
mobility of UAVs with the corresponding communication requirements to make effective information interaction for
UAVs. Task-oriented distributed control is adopted to improve the collaboration flexibility in the multi-UAV visual
navigation system. In order to timely obtain the necessary visual information, each UAV can cooperate with other
relevant UAVs which meet some certain terms such as situation, task or environmental conditions. Simulation results are
presented to show the validity of the proposed mechanism in terms of end-to-end delay and links stability.
In recent years, visual navigation of unmanned aerial vehicles (UAVs) has been an active area of research. There is a
large amount of visual information to be processed and transmitted with real-time requirements for the flight scenes
change rapidly. However, it has already become one of the major factors that block the cooperative communication in
multi-UAV visual navigation. The traditional video image orthogonal decomposition methods can not be well adapted to
the multi-UAV visual navigation system, because with the compression ratio increases, there is a sharp decline in video
image quality. This paper proposes a novel visual information sparse decomposition and transmission (VSDT)
framework for multi-UAV visual navigation.
In the framework, aiming at the visual information characteristics, firstly we pre-process the video images by introducing
a multi-scale visual information acquisition mechanism. Then a fast video image sparse decomposition is made for
transmission. It can greatly reduce the original video information amount, while the quality of visual information needed
for navigation is guaranteed. Finally, based on data correlations and feature matching, a real-time transmission scheme is
designed to make the receiver UAV can quickly reconstruct the flight scene information for navigation. The simulated
results are presented and discussed.
The main advantage of this framework lies in the ability to reduce the visual information transmission amount while
ensuring the quality of visual information needed for navigation and solve the cooperative communication problems such
as information lag, data conjunction and match error often encountered in multi-UAV visual navigation environment.
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