Quantiles of a natural phenomena can provide scientists with an important understanding of different spreads of concentrations. When there are several available robots, it may be advantageous to pool resources in a collaborative way to improve performance. A multirobot team can be difficult to practically bring together and coordinate. To this end, we present a study across several axes of the impact of using multiple robots to estimate quantiles of a distribution of interest using an informative path planning formulation. We measure quantile estimation accuracy with increasing team size to understand what benefits result from a multirobot approach in a drone exploration task of analyzing the algae concentration in lakes. We additionally perform an analysis on several parameters, including the spread of robot initial positions, the planning budget, and inter-robot communication, and find that while using more robots generally results in lower estimation error, this benefit is achieved under certain conditions. We present our findings in the context of real field robotic applications and discuss the implications of the results and interesting directions for future work.

翻译：自然现象的分位数能为科学家提供关于浓度分布不同层次的重要理解。当有多台机器人可用时，以协作方式整合资源可能有利于提升性能。然而，多机器人团队在实际部署和协调中往往面临挑战。为此，我们围绕使用多台机器人通过信息路径规划框架估计目标分布分位数的效果，从多个维度展开研究。通过测量团队规模扩大时的分位数估计精度，我们分析了在湖泊藻类浓度分析的无人机探测任务中，多机器人方法能带来何种优势。此外，我们对机器人初始位置分布、规划预算以及机器人间通信等若干参数进行了分析，发现尽管增加机器人数量通常能降低估计误差，但这一优势仅在特定条件下实现。我们结合真实野外机器人应用场景阐述了研究结果，并讨论了其意义及未来值得探索的方向。