  This paper proposes a cooperative environmental learning algorithm working in
a fully distributed manner. A multi-robot system is more effective for
exploration tasks than a single robot, but it involves the following
challenges: 1) online distributed learning of environmental map using multiple
robots; 2) generation of safe and efficient exploration path based on the
learned map; and 3) maintenance of the scalability with respect to the number
of robots. To this end, we divide the entire process into two stages of
environmental learning and path planning. Distributed algorithms are applied in
each stage and combined through communication between adjacent robots. The
environmental learning algorithm uses a distributed Gaussian process, and the
path planning algorithm uses a distributed Monte Carlo tree search. As a
result, we build a scalable system without the constraint on the number of
robots. Simulation results demonstrate the performance and scalability of the
proposed system. Moreover, a real-world-dataset-based simulation validates the
utility of our algorithm in a more realistic scenario.
