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.

中文翻译：

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用于发现和跟踪多个移动对象的多目标多代理规划


我们考虑一个代理团队的在线规划问题，以通过具有不确定测量对象来源的机载传感器测量来发现和跟踪未知且随时间变化的数量的移动对象。由于机载传感器的视野有限，仅基于跟踪检测到的物体或发现看不见的物体的通常规划策略是不够的。为了解决这个问题，我们制定了一个新的基于信息的多目标多智能体控制问题，将其转换为部分可观察的马尔可夫决策过程（POMDP）。由于对象和多传感器测量之间的未知数据关联，由此产生的多智能体规划问题呈指数级复杂；因此，计算最优控制动作是很困难的。我们证明所提出的多目标价值函数是单调子模集函数，它通过具有严格最优性界限的贪婪搜索来允许低成本次优解决方案。由此产生的规划算法有物体数量和传感器测量的线性复杂度，以及代理数量的二次方。我们通过一系列真实世界数据集的数值实验来演示所提出的解决方案。