Path planning for a robot is one of the major problems in the area of robotics. When a robot is given a task in the form of a Linear Temporal Logic (LTL) specification such that the task needs to be carried out repetitively, we want the robot to follow the shortest cyclic path so that the number of times the robot completes the mission within a given duration gets maximized. In this paper, we address the LTL path planning problem in a dynamic environment where the newly arrived dynamic obstacles may invalidate some of the available paths at any arbitrary point in time. We present DT*, an SMT-based receding horizon planning strategy that solves an optimization problem repetitively based on the current status of the workspace to lead the robot to follow the best available path in the current situation. We implement our algorithm using the Z3 SMT solver and evaluate it extensively on an LTL specification capturing a pick-and-drop application in a warehouse environment. We compare our SMT-based algorithm with two carefully crafted greedy algorithms. Our experimental results show that the proposed algorithm can deal with the dynamism in the workspace in LTL path planning effectively.

中文翻译：

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DT *：动态环境中的时间逻辑路径规划

机器人的路径规划是机器人技术领域的主要问题之一。当机器人以线性时态逻辑（LTL）规范的形式给予任务以使该任务需要重复执行时，我们希望机器人遵循最短的循环路径，以使机器人完成该任务的次数。在给定持续时间内的任务得到最大化。在本文中，我们解决了动态环境中的LTL路径规划问题，在这种环境中，新到达的动态障碍物可能会在任意时间点使某些可用路径失效。我们提出了DT *，这是一种基于SMT的后退视野规划策略，可根据工作区的当前状态重复解决优化问题，从而使机器人遵循当前情况下的最佳可用路径。我们使用Z3 SMT求解器实现我们的算法，并根据LTL规范对它进行了广泛的评估，该规范捕获了仓库环境中的取放应用程序。我们将基于SMT的算法与两种精心设计的贪婪算法进行了比较。我们的实验结果表明，该算法可以有效地解决LTL路径规划中工作空间中的动态问题。