Randomized sampling based algorithms are widely used in robot motion planning due to the problem's intractability, and are experimentally effective on a wide range of problem instances. Most variants bias their sampling using various heuristics related to the known underlying structure of the search space. In this work, we formalize the intuitive notion of guided search by defining the concept of a guiding space. This new language encapsulates many seemingly distinct prior methods under the same framework, and allows us to reason about guidance, a previously obscured core contribution of different algorithms. We suggest an information theoretic method to evaluate guidance, which experimentally matches intuition when tested on known algorithms in a variety of environments. The language and evaluation of guidance suggests improvements to existing methods, and allows for simple hybrid algorithms that combine guidance from multiple sources.

翻译：基于随机采样的算法因其在机器人运动规划问题中的难解性而被广泛使用，并在大量问题实例上表现出实验有效性。大多数变体算法会利用与搜索空间已知底层结构相关的各种启发式信息来偏置其采样过程。在本研究中，我们通过定义引导空间的概念，将引导式搜索的直观理念形式化。这一新表述体系将许多表面相异的前期方法统一于同一框架之下，使我们能够对引导机制——这一以往在不同算法中被模糊处理的核心贡献——进行系统化推理。我们提出了一种基于信息论的方法来评估引导效能，该方法在不同环境下的已知算法测试中与直观认知相吻合。对引导机制的表述与评估不仅为现有方法的改进提供了方向，还使得结合多源引导的简易混合算法成为可能。