This study proposes a hierarchical coverage control method customized for heterogeneous multi-agent systems. In the proposed framework, agents are divided into leaders and followers. The leaders perform computationally intensive centroidal Voronoi control, whereas the followers track their assigned leaders using lightweight local rules. This division of roles addresses the challenge of the high computational demand of conventional coverage algorithms, thus making the method suitable for systems with limited onboard processing capabilities. Simulation results show that the proposed method significantly reduces computational load while achieving similar or superior coverage performance compared with conventional methods, even under non-uniform spatial importance distributions. The results demonstrate the feasibility of scalable and efficient coverage control for heterogeneous agents and indicate its applicability to real-world scenarios such as environmental monitoring and disaster response.

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