The Belief Roadmap: Efficient Planning in Belief Space by Factoring the Covariance
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...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Sage Publications
2010
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| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/58752 https://orcid.org/0000-0002-4959-7368 https://orcid.org/0000-0002-8293-0492 |
| Summary: | 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. |
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