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Optimal strategies for the control of autonomous vehicles in data assimilation

We propose a method to compute optimal control paths for autonomous vehicles deployed for the purpose of inferring a velocity field. In addition to being advected by the flow, the vehicles are able to effect a fixed relative speed with arbitrary control over direction. It is this direction that is used as the basis for the locally optimal control algorithm presented here, with objective formed from the variance trace of the expected posterior distribution. We present results for linear flows near hyperbolic fixed points.

preprint2015arXivOpen access
Damon McDougallRichard Moore
Computationmath.OCMachine Learning
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Damon McDougallRichard Moore

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