Static Coverage for Drone Swarms Using K-MADDPG: A K-means Enhanced Multi-Agent Deep Deterministic Policy Gradient Approach

This paper introduces K-MADDPG, a K-means enhanced Multi-Agent Deep Deterministic Policy Gradient algorithm, for solving static coverage tasks in drone swarms. The proposed approach implements a hierarchical control architecture that integrates K-means clustering for global task allocation with an enhanced MADDPG for local coordination, creating a two-layer control mechanism that optimizes both global coverage efficiency and local collaborative behavior. First, K-MADDPG employs K-means clustering to dynamically partition the target area into sub-regions, with each drone assigned to a cluster center via the Hungarian algorithm. This global allocation strategy ensures balanced coverage distribution and provides clear navigation objectives for each agent. Then, an enhanced MADDPG algorithm processes grid information to enable precise local decision-making. Simulation results demonstrate that K-MADDPG outperforms traditional MADDPG in both convergence speed and coverage stability. The hierarchical architecture enables efficient adaptation to varying environment sizes, while the grid processing enhances spatial awareness and navigation precision. This approach effectively addresses the scalability and efficiency challenges faced by swarm robots in static coverage scenarios.