用于发现和跟踪多个移动物体的多目标多智能体规划
Multi-Objective Multi-Agent Planning for Discovering and Tracking Multiple Mobile Objects
AI总结：
该研究面向多智能体发现与跟踪未知数量移动物体的在线规划问题，提出基于POMDP的信息论多目标控制框架，并利用子模性与贪婪搜索给出具有复杂度和最优性保证的可扩展算法。
摘要：
我们考虑一组智能体的在线规划问题，这些智能体需要利用机载传感器测量来发现并跟踪数量未知且随时间变化的移动物体，而测量与物体之间的来源关联并不确定。由于机载传感器视场有限，通常仅基于跟踪已检测物体或发现未见物体的规划策略都不够充分。为解决这一问题，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，并将其表述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数据关联，由此产生的多智能体规划问题具有指数复杂度，因此计算最优控制动作是难以处理的。我们证明，所提出的多目标价值函数是单调子模集合函数，可通过贪婪搜索获得低成本的次优解，并具有紧的最优性界。所得规划算法在物体数量和跨传感器测量数量方面具有线性复杂度，在智能体数量方面具有二次复杂度。我们通过一系列使用真实世界数据集的数值实验验证了所提出的解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
