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In this paper, we present a meta-algorithm intended to accelerate manyexisting path optimization algorithms. The central idea of our work is tostrategically 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 insuch a way that adjacent pods of the same color have an appropriate buffer ofthe opposite color between them, reducing the risk of interference betweenconcurrent computations. We present a path splitting algorithm to create blueand red pod groupings and detail steps for a meta-algorithm that optimizes overthese pods in parallel. We assessed how our method works on a testbed ofsimulated path optimization scenarios using various optimization tasks andcharacterize how it scales with additional threads. We also compared ourmeta-algorithm on these tasks to other parallelization schemes. Our resultsshow that our method more effectively utilizes concurrency compared to thealternatives, both in terms of speed and optimization quality.

Strobe: An Acceleration Meta-algorithm for Optimizing Robot Paths using Concurrent Interleaved Sub-Epoch Pods