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This paper proposes a new reactive temporal logic planning algorithm formultiple robots that operate in environments with unknown geometry modeledusing occupancy grid maps. The robots are equipped with individual sensors thatallow them to continuously learn a grid map of the unknown environment usingexisting Simultaneous Localization and Mapping (SLAM) methods. The goal of therobots is to accomplish complex collaborative tasks, captured by global LinearTemporal Logic (LTL) formulas. The majority of existing LTL planning approachesrely on discrete abstractions of the robot dynamics operating in knownenvironments and, as a result, they cannot be applied to the more realisticscenarios where the environment is initially unknown. In this paper, we addressthis novel challenge by proposing the first reactive, abstraction-free, anddistributed LTL planning algorithm that can be applied for complex missionplanning of multiple robots operating in unknown environments. The proposedalgorithm is reactive, i.e., planning is adapting to the updated environmentalmap and abstraction-free as it does not rely on designing abstractions of therobot dynamics. Also, our algorithm is distributed in the sense that the globalLTL task is decomposed into single-agent reachability problems constructedonline based on the continuously learned map. The proposed algorithm iscomplete under mild assumptions on the structure of the environment and thesensor models. We provide extensive numerical simulations and hardwareexperiments that illustrate the theoretical analysis and show that the proposedalgorithm can address complex planning tasks for large-scale multi-robotsystems in unknown environments.

Reactive Temporal Logic Planning for Multiple Robots in Unknown Occupancy Grid Maps