  In the tasks of multi-robot collaborative area search, we propose the unified
approach for simultaneous mapping for sensing more targets (exploration) while
searching and locating the targets (coverage). Specifically, we implement a
hierarchical multi-agent reinforcement learning algorithm to decouple task
planning from task execution. The role concept is integrated into the
upper-level task planning for role selection, which enables robots to learn the
role based on the state status from the upper-view. Besides, an intelligent
role switching mechanism enables the role selection module to function between
two timesteps, promoting both exploration and coverage interchangeably. Then
the primitive policy learns how to plan based on their assigned roles and local
observation for sub-task execution. The well-designed experiments show the
scalability and generalization of our method compared with state-of-the-art
approaches in the scenes with varying complexity and number of robots.
