  This paper introduces a set of customizable and novel cost functions that
enable the user to easily specify desirable robot formations, such as a
``high-coverage'' infrastructure-inspection formation, while maintaining high
relative pose estimation accuracy. The overall cost function balances the need
for the robots to be close together for good ranging-based relative
localization accuracy and the need for the robots to achieve specific tasks,
such as minimizing the time taken to inspect a given area. The formations found
by minimizing the aggregated cost function are evaluated in a coverage path
planning task in simulation and experiment, where the robots localize
themselves and unknown landmarks using a simultaneous localization and mapping
algorithm based on the extended Kalman filter. Compared to an optimal formation
that maximizes ranging-based relative localization accuracy, these formations
significantly reduce the time to cover a given area with minimal impact on
relative pose estimation accuracy.
