论文标题
评估运动计划的指导空间
Evaluating Guiding Spaces for Motion Planning
论文作者
论文摘要
由于问题的棘手性，基于随机抽样的算法被广泛用于机器人运动计划中，并且在广泛的问题实例中在实验上有效。大多数变体不会随机取样，而是使用各种启发式方法来偏向其采样，以确定哪些样品将提供更多信息，或者更有可能参与最终解决方案。在这项工作中，我们定义了\ emph {运动计划指导空间}，该}封装了许多看似不同的先前作品在同一框架下。此外，我们建议一种信息理论方法来评估指导计划，该方法将重点放在产生的偏见抽样质量上。最后，我们分析了几种运动计划算法，以证明我们的定义及其评估的适用性。
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 do not sample uniformly at random, and instead bias their sampling using various heuristics for determining which samples will provide more information, or are more likely to participate in the final solution. In this work, we define the \emph{motion planning guiding space}, which encapsulates many seemingly distinct prior works under the same framework. In addition, we suggest an information theoretic method to evaluate guided planning which places the focus on the quality of the resulting biased sampling. Finally, we analyze several motion planning algorithms in order to demonstrate the applicability of our definition and its evaluation.
