Multi-agent systems play a central role in area coverage tasks across search-and-rescue, environmental monitoring, and precision agriculture. Achieving non-uniform coverage, where spatial priorities vary across the domain, requires coordinating agents while respecting dynamic and communication constraints. Density-driven approaches can distribute agents according to a prescribed reference density, but existing methods do not ensure connectivity. This limitation often leads to communication loss, reduced coordination, and degraded coverage performance. This letter introduces a connectivity-preserving extension of the Density-Driven Optimal Control (D2OC) framework. The coverage objective, defined using the Wasserstein distance between the agent distribution and the reference density, admits a convex quadratic program formulation. Communication constraints are incorporated through a smooth connectivity penalty, which maintains strict convexity, supports distributed implementation, and preserves inter-agent communication without imposing rigid formations. Simulation studies show that the proposed method consistently maintains connectivity, improves convergence speed, and enhances non-uniform coverage quality compared with density-driven schemes that do not incorporate explicit connectivity considerations.

翻译：多智能体系统在搜索救援、环境监测和精准农业等区域覆盖任务中发挥着核心作用。为实现非均匀覆盖——即空间优先级在区域内变化的场景——需要在满足动态和通信约束的同时协调智能体。密度驱动方法可根据预设参考密度分布智能体，但现有方法无法保证连通性。这一局限常导致通信中断、协调能力下降及覆盖性能恶化。本文提出一种保持连通性的密度驱动最优控制（D2OC）框架扩展。覆盖目标通过智能体分布与参考密度间的Wasserstein距离定义，可转化为凸二次规划问题。通过引入平滑连通性惩罚项融入通信约束，该设计保持严格凸性、支持分布式实现，并在不强制刚性编队的前提下维持智能体间通信。仿真研究表明：与未显式考虑连通性的密度驱动方案相比，所提方法能持续保持连通性、提升收敛速度，并显著改善非均匀覆盖质量。