  We consider the online planning problem for a team of agents to discover and
track an unknown and time-varying number of moving objects from onboard sensor
measurements with uncertain measurement-object origins. Since the onboard
sensors have limited field-of-views, the usual planning strategy based solely
on either tracking detected objects or discovering unseen objects is
inadequate. To address this, we formulate a new information-based
multi-objective multi-agent control problem, cast as a partially observable
Markov decision process (POMDP). The resulting multi-agent planning problem is
exponentially complex due to the unknown data association between objects and
multi-sensor measurements; hence, computing an optimal control action is
intractable. We prove that the proposed multi-objective value function is a
monotone submodular set function, which admits low-cost suboptimal solutions
via greedy search with a tight optimality bound. The resulting planning
algorithm has a linear complexity in the number of objects and measurements
across the sensors, and quadratic in the number of agents. We demonstrate the
proposed solution via a series of numerical experiments with a real-world
dataset.
