  In this work, a set of motion primitives is defined for use in an
energy-aware motion planning problem. The motion primitives are defined as
sequences of control inputs to a simplified four-DOF dynamics model and are
used to replace the traditional continuous control space used in many
sampling-based motion planners. The primitives are implemented in a Stable
Sparse Rapidly Exploring Random Tree (SST) motion planner and compared to an
identical planner using a continuous control space. The planner using
primitives was found to run 11.0\% faster but yielded solution paths that were
on average worse with higher variance. Also, the solution path travel time is
improved by about 50\%. Using motion primitives for sampling spaces in SST can
effectively reduce the run time of the algorithm, although at the cost of
solution quality.
