  Sensor coverage is the critical multi-robot problem of maximizing the
detection of events in an environment through the deployment of multiple
robots. Large multi-robot systems are often composed of simple robots that are
typically not equipped with a complete set of sensors, so teams with
comprehensive sensing abilities are required to properly cover an area. Robots
also exhibit multiple forms of relationships (e.g., communication connections
or spatial distribution) that need to be considered when assigning robot teams
for sensor coverage. To address this problem, in this paper we introduce a
novel formulation of sensor coverage by multi-robot systems with heterogeneous
relationships as a graph representation learning problem. We propose a
principled approach based on the mathematical framework of regularized
optimization to learn a unified representation of the multi-robot system from
the graphs describing the heterogeneous relationships and to identify the
learned representation's underlying structure in order to assign the robots to
teams. To evaluate the proposed approach, we conduct extensive experiments on
simulated multi-robot systems and a physical multi-robot system as a case
study, demonstrating that our approach is able to effectively assign teams for
heterogeneous multi-robot sensor coverage.
