论文标题
在环境探索和监测环境中极端异常检测的信息路径计划
Informative Path Planning for Extreme Anomaly Detection in Environment Exploration and Monitoring
论文作者
论文摘要
从贝叶斯优化收集的一系列测量值中，发送了一项无人自动驾驶汽车（UAV），以探索和重建未知环境。这项任务的成功是由无人机忠实地重建环境中存在的任何异常特征的能力来判断的，重点是极端（例如，极端的地形凹陷或异常的化学浓度）。我们表明，通常用于确定无人机应访问的位置的标准不适合此任务。我们介绍了许多新的标准，这些标准通过以数学优雅且可计算的方式利用先前收集的信息来指导无人机朝着强烈的异常区域。我们证明了在多种应用中提出的方法的优势，包括从现实世界中的测深数据重建海底地形，以及动态异常的跟踪。我们方法的一个特别有吸引力的特性是它可以克服对抗性条件的能力，即，关于极端位置的先前信念是不精确或错误的情况。
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 features present in the environment, with emphasis on the extremes (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, including reconstruction of seafloor topography from real-world bathymetry data, as well as tracking of dynamic anomalies. A particularly attractive property of our approach is its ability to overcome adversarial conditions, that is, situations in which prior beliefs about the locations of the extremes are imprecise or erroneous.
