  Linear temporal logic and automaton-based run-time verification provide a
powerful framework for designing task and motion planning algorithms for
autonomous agents. The drawback to this approach is the computational cost of
operating on high resolution discrete abstractions of continuous dynamical
systems. In particular, the computational bottleneck that arises is converting
perceived environment variables into a labeling function on the states of a
Kripke structure or analogously the transitions of a labeled transition system.
This paper presents the design and empirical evaluation of an approach to
constructing the labeling function that exposes a large degree of parallelism
in the operation as well as efficient memory access patterns. The approach is
implemented on a commodity GPU and empirical results demonstrate the efficacy
of the labeling technique for real-time planning and decision-making.
