具有LTL约束的多机器人最优路径规划：通过同步保证正确性
Optimal Multi-Robot Path Planning with LTL Constraints: Guaranteeing Correctness Through Synchronization
摘要：
在本文中，我们考虑的机器人团队满足高层次的任务规范的最佳路径的自动规划。团队中的每个机器人被建模为加权过渡系统，其中权重具有相关的偏差值，该偏差值捕获机器人在其部署期间的行进时间中的非确定性。任务是作为一个线性时序逻辑（LTL）公式在一组命题满足的区域的环境。此外，我们有一个优化命题，它捕获了一些必须由团队重复完成的特定任务。目标是最小化连续满足优化命题的实例之间的最大时间，同时保证即使在不确定的旅行时间下也能满足任务。我们的方法依赖于机器人的通信能力，以保证正确性，并在部署过程中保持性能。在为团队成员计算了一组最佳满意路径后，我们还为每个机器人计算了一组同步序列，以确保在部署过程中不会违反LTL公式。我们实施和实验评估我们的方法，考虑在道路网络环境中的持久性监测任务。
英文摘要：
In this paper, we consider the automated planning of optimal paths for a robotic team satisfying a high level mission specification. Each robot in the team is modeled as a weighted transition system where the weights have associated deviation values that capture the non-determinism in the traveling times of the robot during its deployment. The mission is given as a Linear Temporal Logic (LTL) formula over a set of propositions satisfied at the regions of the environment. Additionally, we have an optimizing proposition capturing some particular task that must be repeatedly completed by the team. The goal is to minimize the maximum time between successive satisfying instances of the optimizing proposition while guaranteeing that the mission is satisfied even under non-deterministic traveling times. Our method relies on the communication capabilities of the robots to guarantee correctness and maintain performance during deployment. After computing a set of optimal satisfying paths for the members of the team, we also compute a set of synchronization sequences for each robot to ensure that the LTL formula is never violated during deployment. We implement and experimentally evaluate our method considering a persistent monitoring task in a road network environment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
