This paper presents an incremental replanning algorithm, dubbed LTL-D*, for temporal-logic-based task planning in a dynamically changing environment. Unexpected changes in the environment may lead to failures in satisfying a task specification in the form of a Linear Temporal Logic (LTL). In this study, the considered failures are categorized into two classes: (i) the desired LTL specification can be satisfied via replanning, and (ii) the desired LTL specification is infeasible to meet strictly and can only be satisfied in a "relaxed" fashion. To address these failures, the proposed algorithm finds an optimal replanning solution that minimally violates desired task specifications. In particular, our approach leverages the D* Lite algorithm and employs a distance metric within the synthesized automaton to quantify the degree of the task violation and then replan incrementally. This ensures plan optimality and reduces planning time, especially when frequent replanning is required. Our approach is implemented in a robot navigation simulation to demonstrate a significant improvement in the computational efficiency for replanning by two orders of magnitude.

翻译：本文提出了一种增量式重规划算法LTL-D*，用于动态变化环境中基于时序逻辑的任务规划。环境中的意外变化可能导致以线性时序逻辑形式描述的任务规范无法满足。本研究将此类失败情形分为两类：（i）可通过重规划满足期望的LTL规范；（ii）严格满足期望的LTL规范不可行，仅能以"松弛"方式实现。针对这些失败情形，所提算法通过寻找最小违背期望任务规范的最优重规划方案予以解决。具体而言，本方法基于D* Lite算法，在合成自动机中采用距离度量量化任务违背程度，并实现增量式重规划。这既保证了规划的最优性，又显著缩短了规划时间——尤其在需要频繁重规划的场景下。我们在机器人导航仿真中实现了该方法，结果表明重规划的计算效率提升了两个数量级。