This paper presents an AI-augmented decentralized framework for multi-agent (multi-robot) environmental mapping under limited sensing and communication. While conventional coverage formulations achieve effective spatial allocation when an accurate reference map is available, their performance deteriorates under uncertain or biased priors. The proposed method introduces an adaptive and self-correcting mechanism that enables agents to iteratively refine local density estimates within an optimal transport-based framework, ensuring theoretical consistency and scalability. A dual multilayer perceptron (MLP) module enhances adaptivity by inferring local mean-variance statistics and regulating virtual uncertainty for long-unvisited regions, mitigating stagnation around local minima. Theoretical analysis rigorously proves convergence under the Wasserstein metric, while simulation results demonstrate that the proposed AI-augmented Density-Driven Optimal Control consistently achieves robust and precise alignment with the ground-truth density, yielding substantially higher-fidelity reconstruction of complex multi-modal spatial distributions compared with conventional decentralized baselines.

翻译：本文提出了一种AI增强的去中心化框架，用于在有限感知和通信条件下的多智能体（多机器人）环境建图。传统的覆盖控制方法在可获得精确参考地图时能实现有效的空间分配，但其性能在不确定或有偏先验条件下会显著下降。所提出的方法引入了一种自适应且自校正的机制，使智能体能够在基于最优传输的框架内迭代优化局部密度估计，从而确保理论一致性和可扩展性。一个双多层感知机（MLP）模块通过推断局部均值-方差统计量并调控长期未访问区域的虚拟不确定性来增强自适应性，缓解了局部极小值附近的停滞问题。理论分析严格证明了算法在Wasserstein度量下的收敛性，仿真结果表明，与传统的去中心化基线方法相比，所提出的AI增强密度驱动最优控制方法能够持续实现与真实密度的鲁棒精确对齐，对复杂多模态空间分布的重建保真度显著更高。