  In this paper, we present a meta-algorithm intended to accelerate many
existing path optimization algorithms. The central idea of our work is to
strategically break up a waypoint path into consecutive groupings called
"pods," then optimize over various pods concurrently using parallel processing.
Each pod is assigned a color, either blue or red, and the path is divided in
such a way that adjacent pods of the same color have an appropriate buffer of
the opposite color between them, reducing the risk of interference between
concurrent computations. We present a path splitting algorithm to create blue
and red pod groupings and detail steps for a meta-algorithm that optimizes over
these pods in parallel. We assessed how our method works on a testbed of
simulated path optimization scenarios using various optimization tasks and
characterize how it scales with additional threads. We also compared our
meta-algorithm on these tasks to other parallelization schemes. Our results
show that our method more effectively utilizes concurrency compared to the
alternatives, both in terms of speed and optimization quality.
