Randomized trial demonstrates improved localization accuracy in UAV teams, suggesting enhanced tracking capabilities.
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.
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Cole et al. (2010) studied this question.
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