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. In order to maximize the potential of LTL specifications, we
capitalized on the intrinsic structure of tasks and introduced a hierarchical
structure to LTL specifications. In contrast to the "flat" structure, our
hierarchical model has multiple levels of compositional specifications and
offers benefits such as greater syntactic brevity, improved interpretability,
and more efficient planning. To address tasks under this hierarchical temporal
logic structure, we formulated a decomposition-based method. Each specification
is first broken down into a range of temporally interrelated sub-tasks. We
further mine the temporal relations among the sub-tasks of different
specifications within the hierarchy. Subsequently, a Mixed Integer Linear
Program is utilized to generate a spatio-temporal plan for each robot. Our
hierarchical LTL specifications were experimentally applied to domains of
robotic navigation and manipulation. Results from extensive simulation studies
illustrated both the enhanced expressive potential of the hierarchical form and
the efficacy of the proposed method.Comment: 8 pages, 4 figure