Past research into robotic planning with temporal logic specifications, notably Linear Temporal Logic (LTL), was largely based on singular formulas for individual or groups of robots. But with increasing task complexity, LTL formulas unavoidably grow lengthy, complicating interpretation and specification generation, and straining the computational capacities of the planners. A recent development has been the hierarchical representation of LTL [1] that contains multiple temporal logic specifications, providing a more interpretable framework. However, the proposed planning algorithm assumes the independence of robots within each specification, limiting their application to multi-robot coordination with complex temporal constraints. In this work, we formulated a decomposition-based hierarchical framework. At the high level, each specification is first decomposed into a set of atomic sub-tasks. We further infer the temporal relations among the sub-tasks of different specifications to construct a task network. Subsequently, a Mixed Integer Linear Program is utilized to assign sub-tasks to various robots. At the lower level, domain-specific controllers are employed to execute sub-tasks. Our approach was experimentally applied to domains of robotic navigation and manipulation. The outcomes of thorough simulations, which included comparative analyses, demonstrated the effectiveness of the proposed approach.

翻译：过去对具有时态逻辑规范（特别是线性时态逻辑LTL）的机器人规划研究，主要基于针对单个或群体机器人的单一公式。但随着任务复杂度的增加，LTL公式不可避免地变得冗长，不仅增加了规范解释与生成的难度，也对规划器的计算能力提出了挑战。近期一项进展是提出了包含多个时态逻辑规范的分层LTL表示[1]，该框架具有更强的可解释性。然而，其提出的规划算法假设每个规范内机器人间相互独立，限制了其在具有复杂时态约束的多机器人协调场景中的应用。本文提出了一种基于分解的分层框架：在高层，每个规范首先被分解为一组原子子任务，进而通过推断不同规范子任务间的时序关系构建任务网络，随后利用混合整数线性规划将子任务分配给不同机器人；在低层，采用领域专用控制器执行子任务。本方法已在机器人导航与操作领域进行实验验证，包含对比分析的充分仿真结果表明了该方法的有效性。