Coupled Sensor Configuration and Path-Planning in Unknown Environments with Adaptive Cluster Analysis
Coupled Sensor Configuration and Path-Planning in Unknown Environments with Adaptive Cluster Analysis
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DOI：
10.23919/acc53348.2022.9867482
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Chase St. Laurent;Raghvendra V. Cowlagi
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文献类型：
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作者：
Chase St. Laurent;Raghvendra V. Cowlagi
我们提出了一个自适应的快速近似传感器配置，发现接近最佳的位置和传感器的视野（FoV）。通过基于分区或基于密度的聚类分析的快速近似基于路径规划的统计不确定性与环境不确定性之间的关系进行适应。传感器配置是在最直接影响路径规划工作的感兴趣区域上执行的。这些感兴趣的区域可以通过对概率环境模型进行采样来包括探索路径。路径规划工作的目的是决定一条路径，最大限度地减少代理的暴露在一个未知的静态环境中的威胁。噪声传感器网络的观察被用来构建一个威胁场估计使用高斯过程回归，每次迭代与一个固定的内核和异方差高斯似然。任务驱动的信息增益的优化确定最佳的传感器配置时，最大化。给出了直接优化和自适应聚类分析方法的数值性能。最后，我们表明，聚类中心可以作为一种降维技术用于FoV优化，我们只优化FoV径向覆盖。
We present an adaptive fast-approximation for sensor configuration which finds near-optimal placements and sensor field of views (FoV). The fast-approximation, either via partition-based or density-based cluster analysis, adapts based on the relation between statistical uncertainty of the path plan and environmental uncertainty. The sensor configurations are performed over regions of interest which most directly influence the path-planning efforts. These regions of interest can include exploratory paths by sampling the probabilistic environment model. The path-planning efforts aim to decide upon a path which minimizes an agent’s exposure to threats in an unknown static environment. The noisy sensor network observations are used to construct a threat field estimate using Gaussian Process Regression each iteration with a stationary kernel and heteroscedastic gaussian likelihood. The optimization of a task-driven information gain determines optimal sensor configurations when maximized. The numerical performance of the direct optimization and the adaptive cluster analysis method is presented. Finally, we show that the cluster centers can be utilized as a dimensionality reduction technique for FoV optimization whereby we only optimize FoV radial coverage.