An Area Coverage Method in Unknown Environment Using a Self-Organized Robot Swarm

Area coverage is a classical problem in the applications of swarm robots. Traditional methods rely on global environment modeling or dense sensor data, which is difficult to operate in extreme environments. Minimalist robots with limited capabilities of communication and perception may play an important role. This paper proposes a lightweight self-organizing coverage method based on local awareness and autonomous state switching. By constructing a distributed cooperative coverage framework, the robots in the proposed method make decisions only based on the states of neighboring robots and local environment information. The proposed method established self-organized state transition mechanism to effectively reduce the dependence on explicit communication. Experimental results further demonstrated that the proposed method can achieve global coverage in maze environments with different topological complexity. The research results provide coverage solutions, which do not require prior modeling of the environment and are low requirements of communication. It is thus suitable for tasks in weak information environments, such as nature disaster and underground pipe networks.