  Robots often use feature-based image tracking to identify their position in
their surrounding environment; however, feature-based image tracking is prone
to errors in low-textured and poorly lit environments. Specifically, we
investigate a scenario where robots are tasked with exploring the surface of
the Moon and are required to have an accurate estimate of their position to be
able to correctly geotag scientific measurements. To reduce localization error,
we complement traditional feature-based image tracking with ultra-wideband
(UWB) distance measurements between the robots. The robots use an advanced
mesh-ranging protocol that allows them to continuously share distance
measurements amongst each other rather than relying on the common "anchor" and
"tag" UWB architecture. We develop a decentralized multi-robot coordination
algorithm that actively plans paths based on measurement line-of-sight vectors
amongst all robots to minimize collective localization error. We then
demonstrate the emergent behavior of the proposed multi-robot coordination
algorithm both in simulation and hardware to lower a geometry-based uncertainty
metric and reduce localization error.
