Skip to content
DraconiticResearch
Search Sources API
203,234 studies31 sourcesUpdated 5 min ago
PreprintCC BY-NC-SA 4.0arXiv

Entropy-Regularized Optimal Transport for Time-Varying Multi-Agent Coverage Control

  • Italo Napolitano
  • Mario di Bernardo
Open at sourcePDFDOICite

Abstract

This paper addresses time-varying coverage control for multi-agent systems, formulated as the tracking of an evolving target density via entropy-regularized semi-discrete optimal transport. Unlike the hard Laguerre partition of the unregularized formulation, entropic regularization assigns fractions of the mass at each point to all agents, simplifying the design and numerical implementation of the control law. We derive a feedback-feedforward controller that tracks the evolving first-order optimality conditions with exponential convergence, and we investigate the role of the regularization parameter, which, above a critical threshold, renders the fully collapsed configuration locally optimal. Numerical experiments validate the theory: for moderate regularization, the proposed approach has performance comparable to the unregularized formulation and outperforms the corresponding Voronoi-based coverage baseline.

Related

  1. Accelerating Sinkhorn for Entropy-Regularized Optimal Transport
    Long Chen, Zeyi Xu
    arXivPreprintCC BY 4.0Full textOptimisation & controlarXiv
  2. Partial identification with entropy regularized optimal transport
    Bruno N. Costa, Florian F. Gunsilius
    arXivPreprintCC BY 4.0Full textStatisticsarXiv
  3. Sparsity of Quadratically Regularized Optimal Transport: Scalar Case
    Alberto González-Sanz, Marcel Nutz
    SIAM Journal on Mathematical AnalysisPeer-reviewedLicence not statedFull text24 citedGeneral mathematicsOpenAlex
  4. Geometry and Convergence of Quadratically Regularized Optimal Transport I
    Alberto González-Sanz, Marcel Nutz
    arXivPreprintarXiv licenceFull textAnalysis & PDEsOptimisation & controlarXiv
  5. Regularized Coupling Maps on Matrix Polytopes: From Assignment to Graph Matching and Optimal Transport
    Binrui Shen, Shengxin Zhu
    arXivPreprintarXiv licenceFull textOptimisation & controlarXiv

Cite

BibTeXRIS
BibTeX
@misc{napolitano2026entropyregularized,
  title = {{Entropy-Regularized Optimal Transport for Time-Varying Multi-Agent Coverage Control}},
  author = {Italo Napolitano and Mario di Bernardo},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.09829},
  eprint = {2609.09829},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.09829},
  note = {CC BY-NC-SA 4.0},
}
RIS
TY  - UNPB
TI  - Entropy-Regularized Optimal Transport for Time-Varying Multi-Agent Coverage Control
AU  - Italo Napolitano
AU  - Mario di Bernardo
PY  - 2026
DA  - 2026/09/09
PB  - arXiv
DO  - 10.48550/arxiv.2609.09829
UR  - https://arxiv.org/abs/2609.09829
AB  - This paper addresses time-varying coverage control for multi-agent systems, formulated as the tracking of an evolving target density via entropy-regularized semi-discrete optimal transport. Unlike the hard Laguerre partition of the unregularized formulation, entropic regularization assigns fractions of the mass at each point to all agents, simplifying the design and numerical implementation of the control law. We derive a feedback-feedforward controller that tracks the evolving first-order optimality conditions with exponential convergence, and we investigate the role of the regularization parameter, which, above a critical threshold, renders the fully collapsed configuration locally optimal. Numerical experiments validate the theory: for moderate regularization, the proposed approach has performance comparable to the unregularized formulation and outperforms the corresponding Voronoi-based coverage baseline.
ER  -