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:我们的研究动机源于湖泊藻类浓度的分位估计。我们发现在这项任务中,与单个机器人相比,多机器人团队可以提高性能,且通信质量良好的团队可超过通信质量不良的团队。但是,真正的机器人受到资源限制,而通信网络无法支持任意的信息负载,因此单纯的常数信息共享和复杂的建模和决策变得不可行。考虑到这一点,我们提出了基于在线局部可计算的度量来确定向其他团队成员传递给定消息的效用值的方法,以及基于决策论的方法,仅传输最有用的消息,使用分散的独立框架来维护其他队员的信念。我们在真实世界的水生数据集上通过仿真验证了我们的方法,并表明通过限制通信 via 一个基于消息对未来队友行为的预期影响的效用估计方法,可以将网络负载减少44 % ,而仅将分位估计误差增加了2.16%。