  We consider the setting where a team of robots is tasked with tracking
multiple targets with the following property: approaching the targets enables
more accurate target position estimation, but also increases the risk of sensor
failures. Therefore, it is essential to address the trade-off between tracking
quality maximization and risk minimization. In our previous work, a centralized
controller is developed to plan motions for all the robots -- however, this is
not a scalable approach. Here, we present a decentralized and risk-aware
multi-target tracking framework, in which each robot plans its motion trading
off tracking accuracy maximization and aversion to risk, while only relying on
its own information and information exchanged with its neighbors. We use the
control barrier function to guarantee network connectivity throughout the
tracking process. Extensive numerical experiments demonstrate that our system
can achieve similar tracking accuracy and risk-awareness to its centralized
counterpart.
