摘要翻译：
本文是一篇关于连续、不可预测和高度动态环境中的单智能体在线路径规划的论文。研究的问题是，完整机器人在有几个不可预知的障碍物或对手的环境中，在没有运动动力学限制的情况下，寻找和穿越一条无碰撞路径。假定环境在任何时候都有完全信息的可用性。研究了快速探索随机树(RRT)算法的几种静态和动态变体，以及一种用于动态环境规划的进化算法&进化规划器/导航器（evolutionary Planner/Navigator）。为了克服这两种算法的不足，本文提出了一种新的算法组合，并将RRT变体用于初始规划和基于信息的局部搜索用于导航，再加上简单的贪婪启发式用于优化。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：
《Single-Agent On-line Path Planning in Continuous, Unpredictable and
  Highly Dynamic Environments》
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作者：
Nicolas A. Barriga
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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：
  This document is a thesis on the subject of single-agent on-line path planning in continuous,unpredictable and highly dynamic environments. The problem is finding and traversing a collision-free path for a holonomic robot, without kinodynamic restrictions, moving in an environment with several unpredictably moving obstacles or adversaries. The availability of perfect information of the environment at all times is assumed.   Several static and dynamic variants of the Rapidly Exploring Random Trees (RRT) algorithm are explored, as well as an evolutionary algorithm for planning in dynamic environments called the Evolutionary Planner/Navigator. A combination of both kinds of algorithms is proposed to overcome shortcomings in both, and then a combination of a RRT variant for initial planning and informed local search for navigation, plus a simple greedy heuristic for optimization. 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/0912.0270