We are motivated by quantile estimation of algae concentration in lakes and how decentralized multirobot teams can effectively tackle this problem. We find that multirobot teams improve performance in this task over single robots, and communication-enabled teams further over communication-deprived teams; however, real robots are resource-constrained, and communication networks cannot support arbitrary message loads, making naive, constant information-sharing but also complex modeling and decision-making infeasible. With this in mind, we propose online, locally computable metrics for determining the utility of transmitting a given message to the other team members and a decision-theoretic approach that chooses to transmit only the most useful messages, using a decentralized and independent framework for maintaining beliefs of other teammates. We validate our approach in simulation on a real-world aquatic dataset, and we show that restricting communication via a utility estimation method based on the expected impact of a message on future teammate behavior results in a 42% decrease in network load while simultaneously decreasing quantile estimation error by 1.84%.

翻译：我们受湖水中藻类浓度分位数估计问题的驱动，研究去中心化多机器人团队如何有效应对这一挑战。研究发现，多机器人团队在此任务中的表现优于单机器人，而具备通信能力的团队进一步优于通信受限的团队；然而，真实机器人资源受限，通信网络无法承载任意消息负载，这使得简单持续共享信息以及复杂建模与决策均不可行。基于此，我们提出在线、局部可计算的指标，用于确定向其他团队成员传输特定消息的效用，并采用一种基于决策理论的方法，仅选择传输最具效用的消息，同时利用去中心化且独立的框架维护对其他成员信念的估计。我们在真实世界水生数据集上通过仿真验证了该方法，结果表明：采用基于消息对未来队友行为预期影响的效用估计方法来限制通信，可在网络负载降低42%的同时，将分位数估计误差减少1.84%。