  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%.
