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Sensor coverage is the critical multi-robot problem of maximizing thedetection of events in an environment through the deployment of multiplerobots. Large multi-robot systems are often composed of simple robots that aretypically not equipped with a complete set of sensors, so teams withcomprehensive sensing abilities are required to properly cover an area. Robotsalso exhibit multiple forms of relationships (e.g., communication connectionsor spatial distribution) that need to be considered when assigning robot teamsfor sensor coverage. To address this problem, in this paper we introduce anovel formulation of sensor coverage by multi-robot systems with heterogeneousrelationships as a graph representation learning problem. We propose aprincipled approach based on the mathematical framework of regularizedoptimization to learn a unified representation of the multi-robot system fromthe graphs describing the heterogeneous relationships and to identify thelearned representation's underlying structure in order to assign the robots toteams. To evaluate the proposed approach, we conduct extensive experiments onsimulated multi-robot systems and a physical multi-robot system as a casestudy, demonstrating that our approach is able to effectively assign teams forheterogeneous multi-robot sensor coverage.

Team Assignment for Heterogeneous Multi-Robot Sensor Coverage through Graph Representation Learning