  We consider a scenario where a team of robots with heterogeneous sensors must
track a set of hostile targets which induce sensory failures on the robots. In
particular, the likelihood of failures depends on the proximity between the
targets and the robots. We propose a control framework that implicitly
addresses the competing objectives of performance maximization and sensor
preservation (which impacts the future performance of the team). Our framework
consists of a predictive component -- which accounts for the risk of being
detected by the target, and a reactive component -- which maximizes the
performance of the team regardless of the failures that have already occurred.
Based on a measure of the abundance of sensors in the team, our framework can
generate aggressive and risk-averse robot configurations to track the targets.
Crucially, the heterogeneous sensing capabilities of the robots are explicitly
considered in each step, allowing for a more expressive risk-performance
trade-off. Simulated experiments with induced sensor failures demonstrate the
efficacy of the proposed approach.
