The Belief Roadmap: Efficient Planning in Belief Space by Factoring the Covariance
The Belief Roadmap: Efficient Planning in Belief Space by Factoring the Covariance
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DOI：
10.1177/0278364909341659
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发表时间：
2009-11-01
影响因子：
9.2
通讯作者：
Roy, Nicholas
中科院分区：
文献类型：
--
作者：
Prentice, Samuel;Roy, Nicholas
当移动智能体不完全知道自己的位置时，将未来位置估计的预测不确定性纳入规划过程可以大大提高运动性能。然而，在概率位置估计空间或信念空间中进行规划会产生大量的计算成本。本文证明了利用协方差矩阵的因子形式可以有效地对线性高斯系统进行置信空间规划。这种因子形式允许将几个预测和测量步骤组合成一个单一的线性传递函数，从而在规划期间实现非常有效的后验信念预测。我们给出了概率路线图算法的一个信念空间变体，称为信念路线图（BRM），并表明BRM可以比传统的信念空间规划更快地计算计划。最后，我们给出了使用超宽带无线电信标进行定位的智能体的性能结果，并表明我们可以有效地生成计划，避免由于丢失准确位置估计而导致的故障。
When a mobile agent does not know 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 a substantial computational cost. In this paper, we show that planning in belief space can be performed 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 radio beacons to localize and show that we can efficiently generate plans that avoid failures due to loss of accurate position estimation.