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：摘要：基于库的方法通过从预计算的经验库中检索并适配经验，被公认为能高效实现快速运动规划。本文提出CoverLib，一种构建和利用此类经验库的原则性方法。CoverLib通过迭代向库中添加经验-分类器对，其中每个分类器对应问题空间中经验的可适配区域。该迭代过程具有主动特性，因为后续经验的选择依据是其对未覆盖区域的有效覆盖能力。在查询阶段，这些分类器被用于为给定问题选择预期可适配的经验。实验结果表明，CoverLib能有效缓解全局方法（如基于采样方法）与局部方法（如基于优化方法）在可规划性与速度之间存在的权衡。因此，该方法可在问题域中同时实现快速规划与高成功率。此外，由于其算法无关的适配特性，CoverLib能无缝集成包括非线性规划算法和基于采样算法在内的各类适配方法。