多智能体环境地图构建中，用AI自适应修正密度估计，提升精度与鲁棒性。
AI-Augmented Density-Driven Optimal Control (D2OC) for Decentralized Environmental Mapping
- 基于最优传输框架，动态优化局部密度估计。
- 仿真显示对复杂多模态分布重建精度显著优于传统方法。
- 适合需要高精度分布式感知的机器人协同任务。
本文提出一种面向多智能体（多机器人）在传感与通信受限下的分布式环境地图构建框架。传统覆盖方法在已有精确参考地图时表现良好，但在先验信息不确定或有偏差时性能下降。所提方法引入自适应、可自我修正机制，在最优传输框架内迭代优化局部密度估计，保证理论一致性与可扩展性。通过双层多层感知机（MLP）模块，智能体可推断局部均值方差统计量，并调节长期未访问区域的虚拟不确定性，有效避免陷入局部极小值。理论分析严格证明了在Wasserstein度量下的收敛性；仿真结果表明，该AI增强的密度驱动最优控制方法能持续实现与真实密度的高度一致，相较于传统分布式基线，对复杂多模态空间分布的重建精度显著提升。
原文摘要 · Abstract (English)
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.
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