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

TL;DR

Strobe is a meta-algorithm that accelerates robot path optimization by dividing paths into colored pods optimized concurrently, improving speed and quality over existing parallel schemes.

Contribution

We introduce a novel meta-algorithm that partitions paths into interleaved pods for parallel optimization, enhancing efficiency and scalability.

Findings

Outperforms existing parallelization schemes in speed.

Achieves higher optimization quality.

Scales effectively with more processing threads.

Abstract

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.