  We consider the problem of multi-robot sensor coverage, which deals with
deploying a multi-robot team in an environment and optimizing the sensing
quality of the overall environment. As real-world environments involve a
variety of sensory information, and individual robots are limited in their
available number of sensors, successful multi-robot sensor coverage requires
the deployment of robots in such a way that each individual team member's
sensing quality is maximized. Additionally, because individual robots have
varying complements of sensors and both robots and sensors can fail, robots
must be able to adapt and adjust how they value each sensing capability in
order to obtain the most complete view of the environment, even through changes
in team composition. We introduce a novel formulation for sensor coverage by
multi-robot teams with heterogeneous sensing capabilities that maximizes each
robot's sensing quality, balancing the varying sensing capabilities of
individual robots based on the overall team composition. We propose a solution
based on regularized optimization that uses sparsity-inducing terms to ensure a
robot team focuses on all possible event types, and which we show is proven to
converge to the optimal solution. Through extensive simulation, we show that
our approach is able to effectively deploy a multi-robot team to maximize the
sensing quality of an environment, responding to failures in the multi-robot
team more robustly than non-adaptive approaches.
