Scientists interested in studying natural phenomena often take physical samples for later analysis at locations specified by expert heuristics. Instead, we propose to guide scientists' physical sampling by using a robot to perform an adaptive sampling survey to find locations to suggest that correspond to the quantile values of pre-specified quantiles of interest. We develop a robot planner using novel objective functions to improve the estimates of the quantile values over time and an approach to find locations which correspond to the quantile values. We demonstrate our approach on two different sampling tasks in simulation using previously collected aquatic data and validate it in a field trial. Our approach outperforms objectives that maximize spatial coverage or find extrema in planning and is able to localize the quantile spatial locations.
翻译:有意研究自然现象的科学家往往在专家经济学家指定的地点进行物理取样,以便日后进行分析。相反,我们提议指导科学家的物理取样,方法是使用机器人进行适应性取样调查,以找到符合有关特定孔数前的四分位值的位置。我们开发了一个机器人规划师,利用新的客观功能来改进对孔数值的长期估计,并采用一种方法来寻找与孔数值相对应的位置。我们展示了我们在利用先前收集的水生数据进行模拟的两种不同取样任务上采用的方法,并在现场试验中加以验证。我们的方法优于在规划中尽量扩大空间覆盖范围或找到外体,并能将孔数空间位置本地化的目标。