We present a lazy incremental search algorithm, Lifelong-GLS (L-GLS), along with its bounded suboptimal version, Bounded L-GLS (B-LGLS) that combine the search efficiency of incremental search algorithms with the evaluation efficiency of lazy search algorithms for fast replanning in problem domains where edge-evaluations are more expensive than vertex-expansions. The proposed algorithms generalize Lifelong Planning A* (LPA*) and its bounded suboptimal version, Truncated LPA* (TLPA*), within the Generalized Lazy Search (GLS) framework, so as to restrict expensive edge evaluations only to the current shortest subpath when the cost-to-come inconsistencies are propagated during repair. We also present dynamic versions of the L-GLS and B-LGLS algorithms, called Generalized D* (GD*) and Bounded Generalized D* (B-GD*), respectively, for efficient replanning with non-stationary queries, designed specifically for navigation of mobile robots. We prove that the proposed algorithms are complete and correct in finding a solution that is guaranteed not to exceed the optimal solution cost by a user-chosen factor. Our numerical and experimental results support the claim that the proposed integration of the incremental and lazy search frameworks can help find solutions faster compared to the regular incremental or regular lazy search algorithms when the underlying graph representation changes often.
翻译:我们提出了一个懒惰的递增搜索算法,即Lifelong-GLS(L-GLS)及其受约束的亚最佳版本,即Bounded L-GLS(B-LLS),将递增搜索算法的搜索效率与在问题领域快速再规划的懒惰的搜索算法的评价效率结合起来,因为在问题领域,边际评价比顶端Explus(GD*)和Bounded Generalized D*(LPA*)(LPA*)(LPA*)及其受约束的亚最佳版本,即LPA*(TLPA*),在通用的Lazy搜索(GLS)框架内,将昂贵的边际评价限制在目前最短的子路径上,在修理期间传播成本到收益的不一致之处,将目前的次路径限制在目前的最短的子路径上。我们还提出了L-GLS和B-LLLLLS算法的动态版本,称为通用D*(GD*)和B-GMD* (B-GD*),分别用于在非固定查询查询、流动机器人导航的快速查询查询查询中找到高效的递增量查询解决方案时,我们的拟议递增量分析算算算算算法往往能够找到的固定的固定搜索和固定的计算结果,在找到一个最优数级的计算结果的计算中,在找到的固定的计算法中,在寻找到最优的计算中,在寻找到最优的答案。