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Robust Multi-Robot Optimal Path Planning with Temporal Logic Constraints

机译:具有时态逻辑约束的鲁棒多机器人最优路径规划

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摘要

In this paper we present a method for automatically planning robust optimalpaths for a group of robots that satisfy a common high level missionspecification. Each robot's motion in the environment is modeled as a weightedtransition system, and the mission is given as a Linear Temporal Logic (LTL)formula over a set of propositions satisfied by the regions of the environment.In addition, an optimizing proposition must repeatedly be satisfied. The goalis to minimize the maximum time between satisfying instances of the optimizingproposition while ensuring that the LTL formula is satisfied even withuncertainty in the robots' traveling times. We characterize a class of LTLformulas that are robust to robot timing errors, for which we generate optimalpaths if no timing errors are present, and we present bounds on the deviationfrom the optimal values in the presence of errors. We implement andexperimentally evaluate our method considering a persistent monitoring task ina road network environment.
机译:在本文中,我们提出了一种用于自动规划满足常见高级任务规范的机器人组的鲁棒最优路径的方法。将每个机器人在环境中的运动建模为加权过渡系统,并将任务指定为线性时态逻辑(LTL)公式,针对环境区域所满足的一组命题,此外,还必须反复满足优化命题。目的是使满足优化建议的实例之间的最大时间最小化,同时确保即使在机器人的行进时间不确定的情况下也能满足LTL公式。我们对一类对机器人时序误差具有鲁棒性的LTL公式进行了刻画,如果不存在时序误差,我们会为其生成最优路径,并在存在误差的情况下给出与最优值的偏差范围。考虑到路网环境中的持续监控任务,我们实施并实验评估了我们的方法。

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