Quantiles of a natural phenomena can provide scientists with an important understanding of typical, extreme, or other spreads of concentrations. When a group has several available robots, or teams of scientists come together to study a particular environment, it may be advantageous to pool robot resources in a collaborative way to improve performance. A multirobot team can be difficult to practically bring together and coordinate, especially when robot communication is involved. 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够为科学家提供关于典型值、极值或其他浓度分布的重要理解。当团队拥有多个可用机器人，或科学家团队共同研究特定环境时，以协作方式整合机器人资源以提升性能可能具有优势。多机器人团队在实际组队与协调中面临挑战，尤其涉及机器人通信时。为此，我们基于信息路径规划框架，从多个维度系统研究了利用多机器人估计目标分布分位数的影响。通过分析无人机探测湖泊藻类浓度任务中不同团队规模下的分位数估计精度，我们揭示了多机器人方法带来的效益。此外，我们进一步分析了机器人初始位置分布、规划预算及机器人间通信等参数的影响，发现尽管使用更多机器人通常能降低估计误差，但该效益仅在特定条件下实现。基于实际野外机器人应用场景，我们阐述了研究结果的启示意义，并探讨了未来值得深入的研究方向。