  This paper addresses the problem of active information gathering for
multi-robot systems. Specifically, we consider scenarios where robots are
tasked with reducing uncertainty of dynamical hidden states evolving in complex
environments. The majority of existing information gathering approaches are
centralized and, therefore, they cannot be applied to distributed robot teams
where communication to a central user is not available. To address this
challenge, we propose a novel distributed sampling-based planning algorithm
that can significantly increase robot and target scalability while decreasing
computational cost. In our non-myopic approach, all robots build in parallel
local trees exploring the information space and their corresponding motion
space. As the robots construct their respective local trees, they communicate
with their neighbors to exchange and aggregate their local beliefs about the
hidden state through a distributed Kalman filter. We show that the proposed
algorithm is probabilistically complete and asymptotically optimal. We provide
extensive simulation results that demonstrate the scalability of the proposed
algorithm and that it can address large-scale, multi-robot information
gathering tasks, that are computationally challenging for centralized methods.
