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Past research into robotic planning with temporal logic specifications,notably Linear Temporal Logic (LTL), was largely based on a single formula forindividual or groups of robots. But with increasing task complexity, LTLformulas unavoidably grow lengthy, complicating interpretation andspecification generation, and straining the computational capacities of theplanners. A recent development has been the hierarchical representation ofLTL~\cite{luo2024simultaneous} that contains multiple temporal logicspecifications, providing a more interpretable framework. However, the proposedplanning algorithm assumes the independence of robots within eachspecification, limiting their application to multi-robot coordination withcomplex temporal constraints. In this work, we formulated a decomposition-basedhierarchical framework. At the high level, each specification is firstdecomposed into a set of atomic sub-tasks. We further infer the temporalrelations among the sub-tasks of different specifications to construct a tasknetwork. Subsequently, a Mixed Integer Linear Program is used to assignsub-tasks to various robots. At the lower level, domain-specific controllersare employed to execute sub-tasks. Our approach was experimentally applied todomains of navigation and manipulation. The simulation demonstrated that ourapproach can find better solutions using less runtimes.

Decomposition-based Hierarchical Task Allocation and Planning for Multi-Robots under Hierarchical Temporal Logic Specifications