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Modeling robot motion planning with uncertainty in a Bayesian framework leads to a computationally intractable stochastic control problem. We seek hypotheses that can justify a separate implementation of control, localization and planning. In the end, we reduce the stochastic control problem to pathplanning in the extended space of poses × covariances; the transitions between states are modeled through the use of the Fisher information matrix. In this framework, we consider two problems:doi:10.1109/robot.2008.4543469 dblp:conf/icra/CensiCLO08 fatcat:tebubymgfbcwpmx2jbumfsdora