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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.

Robust Multi-Robot Optimal Path Planning with Temporal Logic Constraints