  This paper considers the problem of distributed state estimation using
multi-robot systems. The robots have limited communication capabilities and,
therefore, communicate their measurements intermittently only when they are
physically close to each other. To decrease the distance that the robots need
to travel only to communicate, we divide them into small teams that can
communicate at different locations to share information and update their
beliefs. Then, we propose a new distributed scheme that combines (i)
communication schedules that ensure that the network is intermittently
connected, and (ii) sampling-based motion planning for the robots in every team
with the objective to collect optimal measurements and decide a location for
those robots to communicate. To the best of our knowledge, this is the first
distributed state estimation framework that relaxes all network connectivity
assumptions, and controls intermittent communication events so that the
estimation uncertainty is minimized. We present simulation results that
demonstrate significant improvement in estimation accuracy compared to methods
that maintain an end-to-end connected network for all time.
