We present the implementation and demonstration of a team of two fixed-wing Unmanned Aerial Vehicles whose task is to improve the localization accuracy of a number of ground-based features. The underlying algorithmic paradigm is based on over a decade's worth of work on Decentralized Data Fusion and Control. We present the components of the architecture, including vehicle localization, feature tracking, path planning and cooperative control. The algorithms described are implemented on a complete Unmanned Aerial System. Three demonstrations were performed, with varying levels of cooperation between team members. We present the results of these demonstrations and compare the performance of the team in completing the mission along with a quantitative understanding of the benefits achieved.
This paper presents a system architecture for unmanned aerial vehicles ͑UAVs͒ performing information gathering missions. Particular focus in this paper is on the development of the architecture from algorithms to simulation, hardware-in-the-loop testing and flight demonstrations. The architecture has been developed for a team of Brumby Mk 3 fixed wing UAVs. Results from recent flight demonstrations are presented in which a single UAV validated a path-following guidance algorithm for feature orbiting, and a utilitybased dynamic path planning algorithm applied to a feature tracking problem. The architecture has also been designed to accommodate the use of multiple UAVs, and future work on a team approach to the problem is presented. 1 This paper uses the terms feature and feature space to describe the objects of interest and the corresponding states of interest. Figure 6. System architecture for the control of teams of UAVs. The architecture encompasses all levels of control, from low level actuators to mission planning and decentralized control. Also included are vehicle localization, DDF. Cole et al.: System Development of a UAV Control Architecture • 421
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