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.

翻译：多查询规划算法在单个搜索空间中寻找不同起点与终点之间的路径。这类算法通过跨规划查询复用信息以实现高效运作，此类信息可在搜索前或搜索过程中计算生成，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比重新寻找全新方案所需的计算更少，这使得PRM*等多查询算法在诸多问题上优于RRT*等单查询算法，但两者的相对性能取决于信息复用的程度。尽管如此，鲜有多查询规划器明确追求最大化路径复用，导致许多算法无法持续超越单查询替代方案。本文提出"努力感知路图"（EIRM*），一种几乎必然渐近最优的多查询规划算法，其明确优先考虑复用计算努力。EIRM*采用非对称双向搜索来识别可能有助于解决特定规划查询的现有路径，并据此对搜索进行排序以减少计算消耗。在测试的抽象与机器人多查询规划问题中，该方法比现有最优规划算法快一个数量级找到初始解。