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.

翻译：本文研究多智能体在线规划问题，其目标是通过机载传感器测量值（具有不确定的测量-物体关联关系）发现并跟踪数量未知且时变的移动物体。由于机载传感器视场有限，仅基于跟踪已检测物体或发现未见过物体的常规规划策略并不充分。为此，我们构建了一个新的基于信息的多目标多智能体控制问题，并将其建模为部分可观测马尔可夫决策过程（POMDP）。由于物体与多传感器测量之间存在未知的数据关联，所得的多智能体规划问题具有指数级复杂度，因此计算最优控制动作是不可行的。我们证明了所提出的多目标值函数是单调次模集函数，该性质允许通过贪婪搜索获得具有紧致最优性界的低成本次优解。所得规划算法在物体数量和跨传感器测量值数量上具有线性复杂度，在智能体数量上具有二次复杂度。我们通过基于真实世界数据的一系列数值实验验证了所提出的解决方案。