Speculative Path Planning

TL;DR

This paper introduces Speculative Path Planning, a parallel A* variant that predicts and pre-evaluates future states to significantly accelerate pathfinding without sacrificing optimality, especially on multi-core processors.

Contribution

It proposes a novel speculative parallelization technique for A* that predicts future expansions and pre-computes collision checks, maintaining search order and optimality guarantees.

Findings

Achieves 11x speedup on 32-core machines.

Maintains optimality while parallelizing state expansions.

Effective on various configurations.

Abstract

Parallelization of A* path planning is mostly limited by the number of possible motions, which is far less than the level of parallelism that modern processors support. In this paper, we go beyond the limitations of traditional parallelism of A* and propose Speculative Path Planning to accelerate the search when there are abundant idle resources. The key idea of our approach is predicting future state expansions relying on patterns among expansions and aggressively parallelize the computations of prospective states (i.e. pre-evaluate the expensive collision checking operation of prospective nodes). This method allows us to maintain the same search order as of vanilla A* and safeguard any optimality guarantees. We evaluate our method on various configurations and show that on a machine with 32 physical cores, our method improves the performance around 11x and 10x on average over counterpart single-threaded and multi-threaded implementations respectively. The code to our paper can be found here: https://github.com/bakhshalipour/speculative-path-planning.