  In this paper, we design an information-based multi-robot source seeking
algorithm where a group of mobile sensors localizes and moves close to a single
source using only local range-based measurements. In the algorithm, the mobile
sensors perform source identification/localization to estimate the source
location; meanwhile, they move to new locations to maximize the Fisher
information about the source contained in the sensor measurements. In doing so,
they improve the source location estimate and move closer to the source. Our
algorithm is superior in convergence speed compared with traditional field
climbing algorithms, is flexible in the measurement model and the choice of
information metric, and is robust to measurement model errors. Moreover, we
provide a fully distributed version of our algorithm, where each sensor decides
its own actions and only shares information with its neighbors through a sparse
communication network. We perform intensive simulation experiments to test our
algorithms on large-scale systems and physical experiments on small ground
vehicles with light sensors, demonstrating success in seeking a light source.
