  This paper presents a distributed scalable multi-robot planning algorithm for
informed sampling of quasistatic spatial fields. We address the problem of
efficient data collection using multiple autonomous vehicles and consider the
effects of communication between multiple robots, acting independently, on the
overall sampling performance of the team. We focus on the distributed sampling
problem where the robots operate independent of their teammates, but have the
ability to communicate their current state to other neighbors within a fixed
communication range. Our proposed approach is scalable and adaptive to various
environmental scenarios, changing robot team configurations, and runs in
real-time, which are important features for many real-world applications. We
compare the performance of our proposed algorithm to baseline strategies
through simulated experiments that utilize models derived from both synthetic
and field deployment data. The results show that our sampling algorithm is
efficient even when robots in the team are operating with a limited
communication range, thus demonstrating the scalability of our method in
sampling large-scale environments.
