  Multi-robot exploration of complex, unknown environments benefits from the
collaboration and cooperation offered by inter-robot communication. Accurate
radio signal strength prediction enables communication-aware exploration.
Models which ignore the effect of the environment on signal propagation or rely
on a priori maps suffer in unknown, communication-restricted (e.g.
subterranean) environments. In this work, we present Propagation Environment
Modeling and Learning (PropEM-L), a framework which leverages real-time
sensor-derived 3D geometric representations of an environment to extract
information about line of sight between radios and attenuating walls/obstacles
in order to accurately predict received signal strength (RSS). Our data-driven
approach combines the strengths of well-known models of signal propagation
phenomena (e.g. shadowing, reflection, diffraction) and machine learning, and
can adapt online to new environments. We demonstrate the performance of
PropEM-L on a six-robot team in a communication-restricted environment with
subway-like, mine-like, and cave-like characteristics, constructed for the 2021
DARPA Subterranean Challenge. Our findings indicate that PropEM-L can improve
signal strength prediction accuracy by up to 44% over a log-distance path loss
model.
