The paper presents an algorithm, called Self-Morphing Adaptive Replanning Tree (SMART), that facilitates fast replanning in dynamic environments. SMART performs risk based tree-pruning if the current path is obstructed by nearby moving obstacle(s), resulting in multiple disjoint subtrees. Then, for speedy recovery, it exploits these subtrees and performs informed tree-repair at hot-spots that lie at the intersection of subtrees to find a new path. The performance of SMART is comparatively evaluated with eight existing algorithms through extensive simulations. Two scenarios are considered with: 1) dynamic obstacles and 2) both static and dynamic obstacles. The results show that SMART yields significant improvements in replanning time, success rate and travel time. Finally, the performance of SMART is validated by a real laboratory experiment.

翻译：本文提出一种名为“自适应塑形重规划树”（SMART）的算法，旨在实现动态环境下的快速重规划。当当前路径受到附近移动障碍物阻挡时，SMART执行基于风险的树剪枝，产生多个不相交的子树。随后，为加速恢复过程，它利用这些子树，在子树相交处的热点区域进行有依据的树修复，从而找到新的路径。通过大量仿真实验，将SMART的性能与八种现有算法进行了比较评估。考虑了两种场景：1）仅存在动态障碍物；2）同时存在静态与动态障碍物。结果表明，SMART在重规划时间、成功率及行程时间方面均实现了显著改进。最后，通过实际实验室实验验证了SMART的性能。