  Search-based methods that use motion primitives can incorporate the system's
dynamics into the planning and thus generate dynamically feasible MAV
trajectories that are globally optimal. However, searching high-dimensional
state lattices is computationally expensive. Local multiresolution is a
commonly used method to accelerate spatial path planning. While paths within
the vicinity of the robot are represented at high resolution, the
representation gets coarser for more distant parts. In this work, we apply the
concept of local multiresolution to high-dimensional state lattices that
include velocities and accelerations. Experiments show that our proposed
approach significantly reduces planning times. Thus, it increases the
applicability to large dynamic environments, where frequent replanning is
necessary.
