  Heterogeneous multi-robot systems are advantageous for operations in unknown
environments because functionally specialised robots can gather environmental
information, while others perform tasks. We define this decomposition as the
scout-task robot architecture and show how it avoids the need to explicitly
balance exploration and exploitation~by permitting the system to do both
simultaneously. The challenge is to guide exploration in a way that improves
overall performance for time-limited tasks. We derive a novel upper confidence
bound for simultaneous exploration and exploitation based on mutual information
and present a general solution for scout-task coordination using decentralised
Monte Carlo tree search. We evaluate the performance of our algorithms in a
multi-drone surveillance scenario in which scout robots are equipped with
low-resolution, long-range sensors and task robots capture detailed information
using short-range sensors. The results address a new class of coordination
problem for heterogeneous teams that has many practical applications.
