A Self-Organizing Area Coverage Method for Swarm Robots Based on Gradient and Grouping

Abstract

The openness of the environment brings great challenges to the swarm robotic system to cover the task area quickly and effectively. In this paper, a coverage method based on gradient and grouping (GGC) is proposed. What is novel about our proposed solution is that it is suitable for extremely simple robots that lack computing or storage power. Through the change of the robot gradient, the swarm robot system with very simple functions can effectively self-organize to cover the unknown task area. By grouping the swarm robots, each group can cover the task area in parallel, which greatly improves the coverage speed. We verified our proposed method through experimental simulation and found that the gradient and grouping-based method in this paper was superior to other methods in terms of coverage, coverage completion time, and other aspects. Simultaneously, the robustness of the proposed method is analyzed and admirable experimental results are obtained. Because the applicable robot is very simple, the method in this paper can be applied to the submillimeter swarm robot system, which will lay the foundation for micro medicine.