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.

翻译：A* 路径规划的平行化主要受可能的动议数量的限制,这远远低于现代处理者所支持的平行化水平。在本文中,我们超越了A* 传统平行主义的局限性,提出在资源闲置时加快搜索的投机性路径规划。我们方法的关键理念是预测未来国家扩张依赖扩张模式和对潜在国家的计算进行积极平行化(即预先评估未来节点昂贵的碰撞检查操作)。这种方法使我们得以维持与香草A* 相同的搜索顺序,并保障任何最佳性能保障。我们评估了各种配置的方法,并表明在拥有32个物理核心的机器上,我们的方法提高了平均11x和10x的性能,分别高于单面和多面执行。我们文件的代码可以在这里找到 https://github.com/bakhshalipour/spectulative-path-planing。