  This paper proposes a novel highly scalable non-myopic planning algorithm for
multi-robot Active Information Acquisition (AIA) tasks. AIA scenarios include
target localization and tracking, active SLAM, surveillance, environmental
monitoring and others. The objective is to compute control policies for
multiple robots which minimize the accumulated uncertainty of a static hidden
state over an a priori unknown horizon. The majority of existing AIA approaches
are centralized and, therefore, face scaling challenges. To mitigate this
issue, we propose an online algorithm that relies on decomposing the AIA task
into local tasks via a dynamic space-partitioning method. The local subtasks
are formulated online and require the robots to switch between exploration and
active information gathering roles depending on their functionality in the
environment. The switching process is tightly integrated with optimizing
information gathering giving rise to a hybrid control approach. We show that
the proposed decomposition-based algorithm is probabilistically complete for
homogeneous sensor teams and under linearity and Gaussian assumptions. We
provide extensive simulation results that show that the proposed algorithm can
address large-scale estimation tasks that are computationally challenging to
solve using existing centralized approaches.
