Robust belief space planning under intermittent sensing via a maximum eigenvalue-based bound
Robust belief space planning under intermittent sensing via a maximum eigenvalue-based bound
复制标题
通过基于最大特征值的界限进行间歇感知下的鲁棒置信空间规划
DOI：
10.1177/0278364916653816
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
J. V. D. Berg
中科院分区：
文献类型：
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作者：
S. Bopardikar;Brendan Englot;A. Speranzon;J. V. D. Berg
我们考虑了在存在间歇感知中，除过程和测量噪声外，将自动车辆从起点到目的地的最小不确定性路径的计算问题，建模为随机过程。我们引入了估计误差协方差矩阵最大特征值的一个新的界作为信念空间规划的代价函数。我们的主要贡献有三个方面。我们首先推导出在传感器错误检测(间歇性)随时间随机发生的情况下状态估计器的性能的解析界。其次，在基于样本的路径规划算法中，我们使用这个界限作为期望的最大特征值演化的代理，以产生一条在精度和稳健性之间权衡的路径。这扩大了最近关于在不确定情况下进行规划的工作范围，包括传感器可能由于错误检测而不能提供任何测量的事实。计算结果表明了该方法的有效性，并与信念空间中路径规划的研究现状进行了比较。第三，也是最后，我们从理论上证明了所提出的算法具有最优子结构性质，即算法返回相对于被视为期望最大特征值演化的代理的界的最优路径。
We consider the problem of computing a minimum uncertainty path for an autonomous vehicle from a start to a destination location in the presence intermittent sensing, modeled as a stochastic process, in addition to process and measurement noise. We introduce the use of a novel bound on the maximum eigenvalue of the estimation error covariance matrix as the cost function for belief space planning. Our main contributions are three-fold. We first derive an analytic bound on the performance of a state estimator under sensor misdetection (intermittency) occurring stochastically over time. Second, we use this bound as a proxy for the expected maximum eigenvalue evolution in a sample-based path planning algorithm to produce a path that trades off accuracy and robustness. This extends the recent body of work on planning under uncertainty to include the fact that sensors may not provide any measurement owing to misdetection. Computational results demonstrate the benefit of the approach and comparisons are made with the state of the art in path planning in belief space. Third, and finally, we establish theoretically that the proposed algorithm possesses the optimal substructure property, i.e. the algorithm returns an optimal path relative to the bound treated as a proxy for the expected maximum eigenvalue evolution.