Motion planning is an essential aspect of autonomous systems and robotics and is an active area of research. A recently-proposed sampling-based motion planning algorithm, termed 'Generalized Shape Expansion' (GSE), has been shown to possess significant improvement in computational time over several existing well-established algorithms. The GSE has also been shown to be probabilistically complete. However, asymptotic optimality of the GSE is yet to be studied. To this end, in this paper we show that the GSE algorithm is not asymptotically optimal by studying its behaviour for the promenade problem. In order to obtain a probabilistically complete and asymptotically optimal generalized shape-based algorithm, a modified version of the GSE, namely 'GSE*' algorithm, is subsequently presented. The forementioned desired mathematical properties of the GSE* algorithm are justified by its detailed analysis. Numerical simulations are found to be in line with the theoretical results on the GSE* algorithm.
Monte-Carlo Tree Search with Prioritized Node Expansion for Multi-Goal Task Planning
Schaeffler Hub for Advanced Research at NTU（南洋理工大学舍弗勒高级研究中心）；School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）
ePA*SE: Edge-based Parallel A* for Slow Evaluations
The Robotics Institute, CMU（卡内基梅隆大学机器人研究所）
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
