  The distributed coordination of robot teams performing complex tasks is
challenging to formulate. The different aspects of a complete task such as
local planning for obstacle avoidance, global goal coordination and
collaborative mapping are often solved separately, when clearly each of these
should influence the others for the most efficient behaviour. In this paper we
use the example application of distributed information acquisition as a robot
team explores a large space to show that we can formulate the whole problem as
a single factor graph with multiple connected layers representing each aspect.
We use Gaussian Belief Propagation (GBP) as the inference mechanism, which
permits parallel, on-demand or asynchronous computation for efficiency when
different aspects are more or less important. This is the first time that a
distributed GBP multi-robot solver has been proven to enable intelligent
collaborative behaviour rather than just guiding robots to individual, selfish
goals. We encourage the reader to view our demos at
https://aalpatya.github.io/gbpstack
