发表机构
Scuola Superiore Meridionale; University of Naples Federico II（南方高等学院; 那不勒斯费德里科二世大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出熵正则化半离散最优传输方法，用于多智能体时变覆盖控制，通过反馈-前馈控制器跟踪演化目标密度，实现指数收敛，并在中等正则化下优于沃罗诺伊基线。
AI 中文摘要
本文研究了多智能体系统的时变覆盖控制问题，将其表述为通过熵正则化半离散最优传输对演化目标密度的跟踪。与未正则化公式中的硬拉盖尔划分不同，熵正则化将每个点的质量分数分配给所有智能体，简化了控制律的设计和数值实现。我们推导了一种反馈-前馈控制器，以指数收敛速度跟踪演化的一阶最优性条件，并研究了正则化参数的作用，该参数在超过临界阈值时，使得完全坍缩的配置成为局部最优。数值实验验证了理论：对于中等正则化，所提出的方法性能与未正则化公式相当，并优于相应的基于沃罗诺伊的覆盖基线。
英文摘要
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.
