  Autonomous exploration and multi-object tracking by a team of agents have
traditionally been considered as two separate, yet related, problems which are
usually solved in two phases: an exploration phase then a tracking phase. The
exploration problem is usually viewed through an information theoretic
framework where a robotic agent attempts to gather as much information about
the environment or an Object of Interest (OI). Conversely, the tracking problem
attempts to maintain precise location information about an OI over time. This
work proposes a single framework which enables the multi-robot multi-object
problem to be solved simultaneously. A hierarchical architecture is used to
coordinate robotic agents in the tracking of multiple OIs while simultaneously
allowing the task to remain computationally efficient. The primary
contributions of this work are a probabilistic constraint on the tracked OIs'
covariances guarantees tracking performance throughout the entire mission. The
automatic discovery of new OIs, a seamless transition to guaranteed tracking of
discovered OIs, and the automatic balancing of exploration with the
requirements of tracking.
