Past research into robotic planning with temporal logic specifications, notably Linear Temporal Logic (LTL), was largely based on a single formula 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~\cite{luo2024simultaneous} 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 used 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 navigation and manipulation. The simulation demonstrated that our approach can find better solutions using less runtimes.

翻译：以往关于时序逻辑规范（特别是线性时序逻辑，LTL）下的机器人规划研究，主要基于单个公式（适用于单个或一组机器人）。但随着任务复杂度的增加，LTL公式不可避免地变得冗长，这既增加了公式解释与规范生成的难度，也对规划器的计算能力提出了更高要求。近期的一项进展是LTL的层次化表示方法（参见文献~\cite{luo2024simultaneous}），它包含多个时序逻辑规范，提供了一个更具可解释性的框架。然而，该文献提出的规划算法假设了每个规范内的机器人是相互独立的，这限制了其在具有复杂时序约束的多机器人协同任务中的应用。在本工作中，我们提出了一种基于分解的层次化框架。在高层，每个规范首先被分解为一组原子子任务。我们进一步推断不同规范的子任务之间的时序关系，以构建一个任务网络。随后，通过混合整数线性规划将子任务分配给不同的机器人。在底层，则采用领域特定的控制器来执行这些子任务。我们的方法在导航和操作领域进行了实验验证。仿真结果表明，我们的方法能够以更短的运行时间找到更优的解决方案。