We are motivated by quantile estimation of algae concentration in lakes. 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 na\"ive, 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 show that restricting communication via a utility estimation method based on the expected impact of a message on future teammate behavior results in a 44% decrease in network load while increasing quantile estimation error by only 2.16%.

翻译：我们以湖泊藻类浓度分位数估计为研究动机。研究发现，多机器人团队在此任务中优于单个机器人，而具备通信能力的团队进一步优于无通信能力的团队；然而，实际机器人资源受限，通信网络无法支持任意消息负载，这使得天真的持续信息共享以及复杂的建模与决策变得不可行。基于此，我们提出在线、本地可计算的度量指标，用于评估将特定消息传输给其他团队成员的效用，并采用一种决策理论方法，仅传输最有效的消息，同时利用去中心化且独立的信息框架维护对其他队友的信念。我们在真实水生数据集上通过仿真验证了该方法，结果表明：基于消息对未来队友行为的预期影响而进行效用估计的通信限制策略，可将网络负载降低44%，同时分位数估计误差仅增加2.16%。