A Bayesian framework for optimal motion planning with uncertainty
- Creators
- Censi, Andrea
- Calisi, D.
- De Luca, A.
- Oriolo, G.
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
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Additional details
- Eprint ID
- 18273
- Resolver ID
- CaltechAUTHORS:20100512-152619370
- Created
-
2010-06-24Created from EPrint's datestamp field
- Updated
-
2021-11-08Created from EPrint's last_modified field
- Other Numbering System Name
- INSPEC Accession Number
- Other Numbering System Identifier
- 10014278