Unmanned aerial vehicle (UAV) formation flight can vastly increase operational range and persistence through autonomous aerial refuelling or efficient flight on a wingman's wake vortices. Differencing individual UAV state estimates is not sufficiently accurate for close formation operations and must be augmented with vehicle-to-vehicle observations. To this end, we propose a quaternion based unscented Kalman filter to fuse information from each UAV sensor suite with relative vision observations. The result is a vastly improved relative state estimate that is resilient to brief vision dropouts and degrades gracefully during extended dropouts. Simulated formation flight results validate the approach and provide a numerical analysis of the algorithm performance. Ground based experiments demonstrate the algorithm running in real-time on a dual-UAV system. This represents a significant step towards an airborne implementation.


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    Title :

    A vision based relative navigation framework for formation flight


    Contributors:


    Publication date :

    2014-05-01


    Size :

    3186495 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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