Library-based methods are known to be very effective for fast motion planning by adapting an experience retrieved from a precomputed library. This article presents CoverLib, a principled approach for constructing and utilizing such a library. CoverLib iteratively adds an experience-classifier-pair to the library, where each classifier corresponds to an adaptable region of the experience within the problem space. This iterative process is an active procedure, as it selects the next experience based on its ability to effectively cover the uncovered region. During the query phase, these classifiers are utilized to select an experience that is expected to be adaptable for a given problem. Experimental results demonstrate that CoverLib effectively mitigates the trade-off between plannability and speed observed in global (e.g. sampling-based) and local (e.g. optimization-based) methods. As a result, it achieves both fast planning and high success rates over the problem domain. Moreover, due to its adaptation-algorithm-agnostic nature, CoverLib seamlessly integrates with various adaptation methods, including nonlinear programming-based and sampling-based algorithms.

翻译：基于库的方法通过利用预计算库中检索的经验，在快速运动规划中展现出显著有效性。本文提出CoverLib，一种构建与使用此类库的原则性方法。CoverLib通过迭代方式向库中添加经验-分类器对，其中每个分类器对应问题空间中该经验的可适配区域。该迭代过程具有主动特性，因其基于当前经验对未覆盖区域的有效覆盖能力来选择下一个经验。在查询阶段，这些分类器被用于选取预期能够适配给定问题的经验。实验结果表明，CoverLib有效缓解了全局方法（如基于采样的方法）与局部方法（如基于优化的方法）在规划可行性与速度之间的权衡。因此，它在问题域上同时实现了快速规划与高成功率。此外，由于其与适配算法无关的特性，CoverLib可无缝集成包括基于非线性规划与基于采样的算法在内的多种适配方法。