让多智能体在非均匀覆盖中保持通信连接，提升协作效率。
Connectivity-Preserving Multi-Agent Area Coverage via Optimal-Transport-Based Density-Driven Optimal Control (D2OC)
- 基于最优传输构建密度驱动控制，动态分配智能体位置。
- 引入平滑连通性惩罚项，确保通信不中断且收敛更快。
- 适合搜救、监测等需灵活协作的场景，尤其关注通信稳定性。
多智能体系统在搜救、环境监测和精准农业等区域覆盖任务中起关键作用。实现空间优先级不同的非均匀覆盖，需协调智能体并满足动态与通信约束。密度驱动方法可按指定参考密度分布智能体，但现有方法无法保证连通性，常导致通信中断、协作下降和覆盖质量恶化。本文提出一种连通性保持的密度驱动最优控制（D2OC）扩展框架。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。通信约束通过平滑连通性惩罚项引入，保持严格凸性，支持分布式实现，并避免刚性队形，有效维持智能体间通信。仿真结果表明，该方法始终维持连通性，加速收敛，显著提升非均匀覆盖质量，优于未显式考虑连通性的密度驱动方案。
原文摘要 · Abstract (English)
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.
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