Reactive Temporal Logic Planning for Multiple Robots in Unknown Environments

Abstract

This paper proposes a new reactive mission planning algorithm for multiple robots that operate in unknown environments. The robots are equipped with individual sensors that allow them to collectively learn and continuously update a map of the unknown environment. The goal of the robots is to accomplish complex tasks, captured by global co-safe Linear Temporal Logic (LTL) formulas. The majority of existing temporal logic planning approaches rely on discrete abstractions of the robot dynamics operating in known environments and, as a result, they cannot be applied to the more realistic scenarios where the environment is initially unknown. In this paper, we address this novel challenge by proposing the first reactive, and abstraction-free LTL planning algorithm that can be applied for complex mission planning of multiple robots operating in unknown environments. Our algorithm is reactive in the sense that temporal logic planning is adapting to the updated map of the environment and abstraction-free as it does not rely on designing abstractions of robot dynamics. Our proposed algorithm is complete under mild assumptions on the structure of the environment and the sensor models. Our paper provides extensive numerical simulations and hardware experiments that illustrate the theoretical analysis and show that the proposed algorithm can address complex planning tasks in unknown environments.