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ICRA
2008
IEEE

A Bayesian framework for optimal motion planning with uncertainty

8 years 8 months ago
A Bayesian framework for optimal motion planning with uncertainty
— 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: minimizing the execution time, and minimizing the final covariance, with an upper bound on the execution time. Two correct and complete algorithms are presented. The first is the direct extension of classical graph-search algorithms in the extended space. The second one is a back-projection algorithm: uncertainty constraints are propagated backward from the goal towards the start state.
Andrea Censi, Daniele Calisi, Alessandro De Luca,
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICRA
Authors Andrea Censi, Daniele Calisi, Alessandro De Luca, Giuseppe Oriolo
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