Cooperative Kalman filters for cooperative exploration
Fumin Zhang and Naomi Ehrich Leonard
Proceedings of the American Control Conference, Seattle, WA, June 2008.
Cooperative exploration requires multiple robotic sensor platforms to
navigate in an unknown scalar field to reveal its global structure.
Sensor readings from the platforms are combined into estimates
to direct motion and reduce noise.
We show that the combined estimates for the
field value, the gradient and the Hessian satisfy an information dynamic model
that does not depend on motion models of the platforms.
Based on this model, we design cooperative Kalman filters that apply
to general cooperative exploration missions. We rigorously justify a set of sufficient conditions that
guarantee the convergence of the cooperative Kalman filters. These sufficient
conditions provide guidelines on mission design issues such as the number of
platforms to use, the shape of the platform formation,
and the motion for each platforms.
(160 KB pdf)
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