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We consider the online planning problem for a team of agents to discover andtrack an unknown and time-varying number of moving objects from onboard sensormeasurements with uncertain measurement-object origins. Since the onboardsensors have a limited field-of-view, the usual planning strategy based solelyon either tracking detected objects or discovering unseen objects isinadequate. To address this, we formulate a new information-basedmulti-objective multi-agent control problem, cast as a partially observableMarkov decision process (POMDP). The resulting multi-agent planning problem isexponentially complex due to the unknown data association between objects andmulti-sensor measurements; hence, computing an optimal control action isintractable. We prove that the proposed multi-objective value function is amonotone submodular set function, which admits low-cost suboptimal solutionsvia greedy search with a tight optimality bound. The resulting planningalgorithm has a linear complexity in the number of objects and measurementsacross the sensors, and quadratic in the number of agents. We demonstrate theproposed solution via a series of numerical experiments with a real-worlddataset.

Multi-Objective Multi-Agent Planning for Discovering and Tracking Multiple Mobile Objects