Decentralized Control of Unmanned Aerial Robots for Wireless Airborne Communication Networks

Deok-Jin Lee, Richard Mark
2010 International Journal of Advanced Robotic Systems  
This paper presents a cooperative control strategy for a team of aerial robotic vehicles to establish wireless airborne communication networks between distributed heterogeneous vehicles. Each aerial robot serves as a flying mobile sensor performing a reconfigurable communication relay node which enabls communication networks with static or slow-moving nodes on gorund or ocean. For distributed optimal deployment of the aerial vehicles for communication networks, an adaptive hill-climbing type
more » ... entralized control algorithm is developed to seek out local extremum for optimal localization of the vehicles. The sensor networks estabilished by the decentralized cooperative control approach can adopt its configuraiton in response to signal strength as the function of the relative distance between the autonomous aerial robots and distributed sensor nodes in the sensed environment. Simulation studies are conducted to evaluate the effectiveness of the proposed decentralized cooperative control technique for robust communication networks. aerial robotic vehicles to establish wireless communication networks between heterogeneous vehicles for wide area surveillance, rescue, and tracking applications. This reserch is based on the previous work (Lee 2009) and further extends it to an advanced cooperative capability for controlling multiple aerial robotic vehicles. In this coopeartive sensor networks, each aerial robot serves as a flying mobile sensor as well as a reconfigurable communication array. In order to accomplish the goal of building a stable wireless communication sensor networks using multiple aerial robotic vehicles, the following two tactical approaches are required ; one is a decentralized cooperative control technique and the other is about a formation control approach. First, distributed optimal deployment of the aerial vehicles for high bandwidth communication networks is accomplished by apply an adaptive hill-climbing type control algorithm, with which each aerial vehicle seeks out its own local extremum location by using the information received from neighboring aerial vehicles and remote nodes in a decentralized way. In the second phase, after each aerial robotic vehicle finds its own optimal/suboptimal location for high bandwidth communication to remote nodes located in either gound or surface, it is necessary to the aerial vehicles to fly in a formation to minimize the effets of each robot's bank agnle maximizing the communication signal strength between the aerial vehicles. The formation flying control of the UAVs leads to minimizing the effects of the angle between their antennas to maintain an optimal communication link. Two formation control methods were introduced in the reference (Lee and Mark, 2009), that is, in-phase control, and out-of phase control, and in this paper, the in-phase formation control technique is explored to maximize the communication strength between the aerial vehices. The sensor networks estabilished by the decentralized cooperative control technique with a formation flying of the aerila vehicles for phase synchronization can adopt its configuraiton in response to signal strength as the function of the relative distance between the autonomous aerial robots and distributed sensor nodes in the sensed environment, which resulting in a stable and reconfigurable wireless sensor entworks. The overall concept of establishing a communication sensor networks with the decentralized cooperative control tehcnique is shown in Fig. 1 . The performance of the proposed decentralized cooperative control technique is evaluated by conducting various simulation studies with wireless communication networking applications. The remainder of this paper is organized as follows. Section II describes the overview of a hybrid control of a long-endurance unmanned aerial vehicle which uses a soaring flight technique to harvest lift energy from the natural environment. Section III describes the selfestimating extremum control technique for optimizing the flight trajectory of an uninhabited aerial vehicle to obtain a maximum communication links between
doi:10.5772/9702 fatcat:ak44h3oldbburkcorhv7tthqje