An unmanned autonomous vehicle (UAV) is sent on a mission to explore and reconstruct an unknown environment from a series of measurements collected by Bayesian optimization. The success of the mission is judged by the UAV's ability to faithfully reconstruct any anomalous feature present in the environment (e.g., extreme topographic depressions or abnormal chemical concentrations). We show that the criteria commonly used for determining which locations the UAV should visit are ill-suited for this task. We introduce a number of novel criteria that guide the UAV towards regions of strong anomalies by leveraging previously collected information in a mathematically elegant and computationally tractable manner. We demonstrate superiority of the proposed approach in several applications.

中文翻译：

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环境探索和监测中异常检测的信息路径规划

无人驾驶自动驾驶汽车 (UAV) 被派往执行任务，通过贝叶斯优化收集的一系列测量结果探索和重建未知环境。任务的成功取决于无人机忠实地重建环境中存在的任何异常特征的能力（例如，极端的地形洼地或异常的化学浓度）。我们表明，通常用于确定无人机应该访问哪些位置的标准不适合这项任务。我们引入了许多新颖的标准，通过以数学上优雅且计算上易于处理的方式利用先前收集的信息，引导无人机进入强异常区域。我们在几个应用中证明了所提出的方法的优越性。