  The generalized label correcting method is an efficient search-based approach
to trajectory optimization. It relies on a finite set of control primitives
that are concatenated into candidate control signals. This paper investigates
the principled selection of this set of control primitives. Emphasis is placed
on a particularly challenging input space geometry, the $n$-dimensional sphere.
We propose using controls which minimize a generalized energy function and
discuss the optimization technique used to obtain these control primitives. A
numerical experiment is presented showing a factor of two improvement in
running time when using the optimized control primitives over a random sampling
strategy.
