摘要翻译：
概率抽样方法已成为解决单次路径规划问题的热门方法。尤其是快速探索的随机树，在解决高维问题上被证明是有效的。尽管已经提出了几种RRT变体用于动态重新规划，但这些方法只在变化不频繁的环境中表现良好。本文在多阶段概率算法中结合简单技术解决了动态路径规划问题。该算法利用RRTs进行初始规划，利用通知的局部搜索进行导航。我们表明，这种简单技术的组合比RRT扩展对高度动态的环境提供了更好的响应。
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英文标题：
《A Multi-stage Probabilistic Algorithm for Dynamic Path-Planning》
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作者：
Nicolas A. Barriga, Mauricio Araya-L\'opez
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最新提交年份：
2009
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Robotics        机器人学
分类描述：Roughly includes material in ACM Subject Class I.2.9.
大致包括ACM科目I.2.9类的材料。
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英文摘要：
  Probabilistic sampling methods have become very popular to solve single-shot path planning problems. Rapidly-exploring Random Trees (RRTs) in particular have been shown to be efficient in solving high dimensional problems. Even though several RRT variants have been proposed for dynamic replanning, these methods only perform well in environments with infrequent changes. This paper addresses the dynamic path planning problem by combining simple techniques in a multi-stage probabilistic algorithm. This algorithm uses RRTs for initial planning and informed local search for navigation. We show that this combination of simple techniques provides better responses to highly dynamic environments than the RRT extensions.
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PDF链接：
https://arxiv.org/pdf/0912.0224