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摘要翻译：
基于采样的运动规划器是生成无碰撞运动路径的有效手段。然而，这些运动路径的质量（就质量度量而言，如路径长度、间隙、平滑度或能量）往往很低，尤其是在高维构型空间中。本文介绍了一种简单的算法，用于将任意数目的输入运动路径合并为高质量的混合输出路径，从而得到了路径质量的广义和一般表达式。我们的方法是基于这样一个观察，即每个解决方案中某些子路径的质量可能高于整个路径的质量。我们最近提出了一种动态规划算法，用于比较和聚类多个运动路径，大大减少了合并算法的运行时间。我们在多达12个自由度的运动规划问题中测试了我们的算法。我们证明了我们的算法能够合并由几个不同的运动规划器产生的少数输入路径，以产生更高质量的输出路径。
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英文标题：
《A Little More, a Lot Better: Improving Path Quality by a Simple Path
Merging Algorithm》
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作者：
Barak Raveh, Angela Enosh and Dan Halperin
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最新提交年份：
2010
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分类信息：
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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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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英文摘要：
Sampling-based motion planners are an effective means for generating collision-free motion paths. However, the quality of these motion paths (with respect to quality measures such as path length, clearance, smoothness or energy) is often notoriously low, especially in high-dimensional configuration spaces. We introduce a simple algorithm for merging an arbitrary number of input motion paths into a hybrid output path of superior quality, for a broad and general formulation of path quality. Our approach is based on the observation that the quality of certain sub-paths within each solution may be higher than the quality of the entire path. A dynamic-programming algorithm, which we recently developed for comparing and clustering multiple motion paths, reduces the running time of the merging algorithm significantly. We tested our algorithm in motion-planning problems with up to 12 degrees of freedom. We show that our algorithm is able to merge a handful of input paths produced by several different motion planners to produce output paths of much higher quality.
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PDF链接：
https://arxiv.org/pdf/1001.2391
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