  This paper proposes a new reactive temporal logic planning algorithm for
multiple robots that operate in environments with unknown geometry modeled
using occupancy grid maps. The robots are equipped with individual sensors that
allow them to continuously learn a grid map of the unknown environment using
existing Simultaneous Localization and Mapping (SLAM) methods. The goal of the
robots is to accomplish complex collaborative tasks, captured by global Linear
Temporal Logic (LTL) formulas. The majority of existing LTL 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, abstraction-free, and
distributed LTL planning algorithm that can be applied for complex mission
planning of multiple robots operating in unknown environments. The proposed
algorithm is reactive, i.e., planning is adapting to the updated environmental
map and abstraction-free as it does not rely on designing abstractions of the
robot dynamics. Also, our algorithm is distributed in the sense that the global
LTL task is decomposed into single-agent reachability problems constructed
online based on the continuously learned map. The proposed algorithm is
complete under mild assumptions on the structure of the environment and the
sensor models. We provide extensive numerical simulations and hardware
experiments that illustrate the theoretical analysis and show that the proposed
algorithm can address complex planning tasks for large-scale multi-robot
systems in unknown environments.
