ePA*SE：基于边的并行A* 算法
ePA*SE: Edge-based Parallel A* for Slow Evaluations
摘要：
并行搜索算法利用现代处理器的多线程能力来实现更快的规划。一个这样的算法是PA*SE（Parallel A* for Slow Expansions），其并行化状态扩展以在状态扩展缓慢的域中实现更快的规划。在这项工作中，我们提出了ePA*SE（Edge-based Parallel A* for Slow Evaluations），它通过并行边缘评估而不是状态扩展来改进PA*SE。这使得ePA*SE在边缘评估昂贵且需要不同计算量的领域中更有效，这在机器人技术中经常发生。在理论方面，我们证明了ePA*SE提供了严格的最优性保证。此外，ePA*SE可以被简单地扩展以处理启发式上的膨胀权重，从而产生有界次优算法w-ePA*SE（加权ePA*SE），其权衡最优性以实现更快的规划。在实验方面，我们在两个不同的规划域：1）运动规划的三维人形导航和2）任务和运动规划的双臂机器人装配任务，验证了所提出的算法。我们表明，ePA*SE可以比PA*SE和其他替代品更有效。ePA*SE的开源代码以及基线可在以下网站获得：https://github.com/shohinm/parallel_search
英文摘要：
Parallel search algorithms harness the multithreading capability of modern processors to achieve faster planning. One such algorithm is PA*SE (Parallel A* for Slow Expansions), which parallelizes state expansions to achieve faster planning in domains where state expansions are slow. In this work, we propose ePA*SE (Edge-based Parallel A* for Slow Evaluations) that improves on PA*SE by parallelizing edge evaluations instead of state expansions. This makes ePA*SE more efficient in domains where edge evaluations are expensive and need varying amounts of computational effort, which is often the case in robotics. On the theoretical front, we show that ePA*SE provides rigorous optimality guarantees. In addition, ePA*SE can be trivially extended to handle an inflation weight on the heuristic resulting in a bounded suboptimal algorithm w-ePA*SE (Weighted ePA*SE) that trades off optimality for faster planning. On the experimental front, we validate the proposed algorithm in two different planning domains: 1) motion planning for 3D humanoid navigation and 2) task and motion planning for a dual-arm robotic assembly task. We show that ePA*SE can be significantly more efficient than PA*SE and other alternatives. The open-source code for ePA*SE along with the baselines is available here: https://github.com/shohinm/parallel_search
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
