The Belief Roadmap: Efficient Planning in Belief Space by Factoring the Covariance
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Author(s)
Roy, Nicholas
Prentice, Samuel James
Date Issued
July 2009
Journal
International Journal of Robotics Research
Publisher
Sage Publications
Citation
Prentice, Samuel, and Nicholas Roy. “The Belief Roadmap: Efficient Planning in Belief Space by Factoring the Covariance.” The International Journal of Robotics Research 28.11-12: 1448 -1465.
Version
Author's final manuscript
Abstract
When a mobile agent does not known its position
perfectly, incorporating the predicted uncertainty of future position
estimates into the planning process can lead to substantially
better motion performance. However, planning in the space
of probabilistic position estimates, or belief space, can incur
substantial computational cost. In this paper, we show that
planning in belief space can be done efficiently for linear Gaussian
systems by using a factored form of the covariance matrix. This
factored form allows several prediction and measurement steps
to be combined into a single linear transfer function, leading to
very efficient posterior belief prediction during planning. We give
a belief-space variant of the Probabilistic Roadmap algorithm
called the Belief Roadmap (BRM) and show that the BRM can
compute plans substantially faster than conventional belief space
planning. We conclude with performance results for an agent
using ultra-wide bandwidth (UWB) radio beacons to localize and
show that we can efficiently generate plans that avoid failures
due to loss of accurate position estimation.
Subjects
planning under uncertainty
motion planning
probabilistic
state estimation
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.1177/0278364909341659
