Parallel search algorithms have been shown to improve planning speed by harnessing the multithreading capability of modern processors. One such algorithm PA*SE achieves this by parallelizing state expansions, whereas another algorithm ePA*SE achieves this by effectively parallelizing edge evaluations. ePA*SE targets domains in which the action space comprises actions with expensive but similar evaluation times. However, in a number of robotics domains, the action space is heterogenous in the computational effort required to evaluate the cost of an action and its outcome. Motivated by this, we introduce GePA*SE: Generalized Edge-based Parallel A* for Slow Evaluations, which generalizes the key ideas of PA*SE and ePA*SE i.e. parallelization of state expansions and edge evaluations respectively. This extends its applicability to domains that have actions requiring varying computational effort to evaluate them. The open-source code for GePA*SE along with the baselines is available here: https://github.com/shohinm/parallel_search

翻译：并行搜索算法已被证明能够通过利用现代处理器的多线程能力来提升规划速度。其中PA*SE算法通过并行化状态扩展实现这一目标，而另一算法ePA*SE则通过有效并行化边缘评估实现加速。ePA*SE适用于动作空间包含评估时间昂贵但相近的领域。然而在众多机器人技术领域中，动作空间存在异质性——不同动作的代价及其结果的评估所需计算量差异显著。基于此，我们提出GePA*SE：面向慢速评估的广义边缘并行A*算法，该算法将PA*SE与ePA*SE的核心思想（即分别并行化状态扩展与边缘评估）进行泛化统一，从而扩展了算法对需要不同计算量的动作评估领域的适用性。GePA*SE及基线算法的开源代码已发布于：https://github.com/shohinm/parallel_search