We consider the online planning problem for a team of agents with on-board sensors to discover and track an unknown and time-varying number of moving objects from sensor measurements with uncertain measurement-object origins. Since the onboard sensors have limited field of views (FoV), the usual planning strategy based solely on either tracking detected objects or discovering unseen objects is inadequate. To address this, we formulate a new multi-objective multi-agent model for a predictive control problem based on information-theoretic criteria; 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, and develop a greedy algorithm that can achieve an 0.5OPT compared to an optimal algorithm. We demonstrate the proposed solution via a series of numerical experiments with a real-world dataset.

翻译：我们考虑一个在线规划问题，其中配备机载传感器的智能体团队需要利用来自传感器（测量值与物体来源存在不确定性）的测量数据，发现并跟踪数量未知且时变的移动物体。由于机载传感器的视场受限，仅基于跟踪已检测物体或发现未观测物体的传统规划策略难以胜任。为此，我们基于信息论准则构建了一个新的多目标多智能体预测控制问题模型，并将其形式化为部分可观测马尔可夫决策过程。由于物体与多传感器测量值之间的数据关联未知，该多智能体规划问题具有指数级复杂度，导致最优控制动作的计算不可解。我们证明所提多目标值函数是单调子模集函数，并开发了一种贪心算法，该算法可实现最优算法0.5倍近似比。通过基于真实世界数据集的一系列数值实验，我们验证了所提方案的有效性。