  In this paper, a novel deep reinforcement learning (DRL)-based method is
proposed to navigate the robot team through unknown complex environments, where
the geometric centroid of the robot team aims to reach the goal position while
avoiding collisions and maintaining connectivity. Decentralized robot-level
policies are derived using a mechanism of centralized learning and
decentralized executing. The proposed method can derive end-to-end policies,
which map raw lidar measurements into velocity control commands of robots
without the necessity of constructing obstacle maps. Simulation and indoor
real-world unmanned ground vehicles (UGVs) experimental results verify the
effectiveness of the proposed method.
