The paper introduces an asymptotically optimal lifelong sampling-based path planning algorithm that combines the merits of lifelong planning algorithms and lazy search algorithms for rapid replanning in dynamic environments where edge evaluation is expensive. By evaluating only sub-path candidates for the optimal solution, the algorithm saves considerable evaluation time and thereby reduces the overall planning cost. It employs a novel informed rewiring cascade to efficiently repair the search tree when the underlying search graph changes. Simulation results demonstrate that the algorithm outperforms various state-of-the-art sampling-based planners in addressing both static and dynamic motion planning problems.

翻译：本文提出了一种渐进最优的终身采样路径规划算法，该算法融合了终身规划算法与惰性搜索算法的优势，适用于边评估代价高昂的动态环境快速重规划场景。通过仅评估最优解的候选子路径，算法显著节省了评估时间，从而降低了整体规划成本。当底层搜索图发生变化时，算法采用一种新颖的启发式重连级联机制高效修复搜索树。仿真结果表明，该算法在解决静态与动态运动规划问题时，性能优于多种先进的基于采样的规划器。