Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published May 2008 | Published
Book Section - Chapter Open

A Bayesian framework for optimal motion planning with uncertainty

Abstract

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 path- planning in the extended space of poses x 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.

Additional Information

© 2008 IEEE. Issue Date: 19-23 May 2008; Date of Current Version: 13 June 2008.

Attached Files

Published - Censi2008p84512008_Ieee_International_Conference_On_Robotics_And_Automation_Vols_1-9.pdf

Files

Censi2008p84512008_Ieee_International_Conference_On_Robotics_And_Automation_Vols_1-9.pdf

Additional details

Created:
August 22, 2023
Modified:
October 20, 2023