Effort Informed Roadmaps (EIRM*)：通过主动重用验证努力实现高效渐近最优多查询规划
Effort Informed Roadmaps (EIRM*): Efficient Asymptotically Optimal Multiquery Planning by Actively Reusing Validation Effort
AI总结：
本文提出EIRM*多查询规划算法，通过非对称双向搜索主动识别并重用已有路径信息，在保证几乎必然渐近最优的同时，使初始解求解速度比现有最优算法快一个数量级。
摘要：
多查询规划算法在单一搜索空间中为各种不同的起点和目标寻找路径。它们旨在通过跨规划查询重用信息来高效实现这一目标。这些信息可以在搜索之前或搜索过程中计算，并且通常包含已知有效路径的知识。利用已知有效路径来解决单个规划查询所需的计算努力比寻找全新解决方案要少。这使得诸如 PRM* 之类的多查询算法在许多问题上能够优于诸如 RRT* 之类的单查询算法，但它们的相对性能取决于重用信息的多少。尽管如此，很少有明确寻求最大化路径重用的多查询规划器，因此许多多查询规划器并不能始终优于单查询替代方案。本文提出了 Effort Informed Roadmaps (EIRM*)，这是一种几乎必然渐近最优的多查询规划算法，明确优先考虑重用计算努力。EIRM* 使用非对称双向搜索来识别可能有助于解决单个规划查询的现有路径，然后利用这些信息对其搜索进行排序并减少计算努力。这使得它在测试的抽象和机器人多查询规划问题上找到初始解的速度比最先进的规划算法快一个数量级。
英文摘要：
Multiquery planning algorithms find paths between various different starts and goals in a single search space. They are designed to do so efficiently by reusing information across planning queries. This information may be computed before or during the search and often includes knowledge of valid paths. Using known valid paths to solve an individual planning query takes less computational effort than finding a completely new solution. This allows multiquery algorithms, such as PRM*, to outperform single-query algorithms, such as RRT*, on many problems but their relative performance depends on how much information is reused. Despite this, few multiquery planners explicitly seek to maximize path reuse and, as a result, many do not consistently outperform single-query alternatives. This paper presents Effort Informed Roadmaps (EIRM*), an almost-surely asymptotically optimal multiquery planning algorithm that explicitly prioritizes reusing computational effort. EIRM* uses an asymmetric bidirectional search to identify existing paths that may help solve an individual planning query and then uses this information to order its search and reduce computational effort. This allows it to find initial solutions up to an order-of-magnitude faster than state-of-the-art planning algorithms on the tested abstract and robotic multiquery planning problems.
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