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 a limited field-of-view, 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：本文研究多智能体团队在线规划问题，旨在通过搭载有限视场角机载传感器的测量数据，发现并追踪数量未知且时变的移动目标，同时处理不确定的测量-目标来源关联问题。由于机载传感器视场角受限，单纯基于追踪已探测目标或发现未观测目标的常规规划策略均存在局限性。为此，我们构建了一种新型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）。由于目标与多传感器测量间存在未知数据关联，所提出的多智能体规划问题具有指数级计算复杂度，因此最优控制动作的求解是不可行的。我们证明所提出的多目标价值函数是单调子模集函数，可通过具有紧致最优性边界的贪婪搜索获得低成本的次优解。该规划算法的复杂度与跨传感器目标及测量数量呈线性关系，与智能体数量呈二次方关系。我们通过基于真实数据集的系列数值实验验证了所提方案的有效性。