AI-Augmented Density-Driven Optimal Control (D2OC) for Decentralized Environmental Mapping

2026-01-28

AI Summary

This work addresses the challenge of degraded mapping performance in multi-agent systems operating under perceptual and communication constraints with biased prior maps. To overcome this, we propose a decentralized mapping framework grounded in optimal transport theory, which achieves high-fidelity reconstruction through iterative refinement of local density estimates. A dual-MLP module is introduced to adaptively infer local statistical properties and modulate virtual uncertainty accordingly. By integrating optimal control with artificial intelligence techniques, our approach establishes a self-correcting mechanism that operates without global information while guaranteeing theoretical convergence. The method supports accurate multimodal density reconstruction, and extensive simulations demonstrate its superior performance over existing decentralized baselines in complex multimodal environments, exhibiting both robustness and high reconstruction fidelity.

Technology Category

• Multiagent Systems: Multiagent Learning
• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• decentralized environmental mapping
• multi-agent systems
• uncertain priors
• spatial density estimation
• coverage control

Innovation

• AI-augmented control
• density-driven mapping
• optimal transport
• decentralized multi-agent systems
• Wasserstein convergence