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

TL;DR

This paper introduces an AI-augmented decentralized control framework for multi-agent environmental mapping that adaptively refines density estimates, ensuring robust, scalable, and high-fidelity spatial distribution reconstruction under uncertain priors.

Contribution

It proposes a novel adaptive, self-correcting mechanism integrated with optimal transport and a dual MLP module, with proven convergence and improved mapping accuracy over traditional methods.

Findings

Achieves robust density estimation under uncertain priors.

Demonstrates higher-fidelity spatial distribution reconstruction.

Proves convergence under the Wasserstein metric.

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.