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，在解决高维问题上已经被证明是非常有效的。尽管已经提出了几种RRT变体来解决动态重新规划问题，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。该算法使用RRTs作为初始解，通知局部搜索来修复不可行路径，并使用一个简单的贪婪优化器。该算法能够在局部搜索陷入停滞时进行识别，并重新启动RRT。我们表明，这种简单技术的组合比动态RRT变体对高度动态的环境提供了更好的响应。
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英文标题：
《Combining a Probabilistic Sampling Technique and Simple Heuristics to
  solve the Dynamic Path Planning Problem》
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作者：
Nicolas A. Barriga, Mauricio Araya-L\'opez, Mauricio Solar
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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 very efficient in solving high dimensional problems. Even though several RRT variants have been proposed to tackle the dynamic replanning problem, 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 as an initial solution, informed local search to fix unfeasible paths and a simple greedy optimizer. The algorithm is capable of recognizing when the local search is stuck, and subsequently restart the RRT. We show that this combination of simple techniques provides better responses to a highly dynamic environment than the dynamic RRT variants.
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PDF链接：
https://arxiv.org/pdf/0912.0266