  We propose a risk-aware framework for multi-robot, multi-demand assignment
and planning in unknown environments. Our motivation is disaster response and
search-and-rescue scenarios where ground vehicles must reach demand locations
as soon as possible. We consider a setting where the terrain information is
available only in the form of an aerial, georeferenced image. Deep learning
techniques can be used for semantic segmentation of the aerial image to create
a cost map for safe ground robot navigation. Such segmentation may still be
noisy. Hence, we present a joint planning and perception framework that
accounts for the risk introduced due to noisy perception. Our contributions are
two-fold: (i) we show how to use Bayesian deep learning techniques to extract
risk at the perception level; and (ii) use a risk-theoretical measure, CVaR,
for risk-aware planning and assignment. The pipeline is theoretically
established, then empirically analyzed through two datasets. We find that
accounting for risk at both levels produces quantifiably safer paths and
assignments.
