  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.
