In this paper, we propose an efficient method for human dense avoidance based on a coverage control. Our motivation is to avoid crowding in public facilities such as stations and government offices, and human dense in the current situation of COVID-19 from system and control theory. In this paper, humans and robots are modeled as heterogeneous and homogeneous agents, respectively, which make decisions based on their local information. We suppose a dense situation caused by the rendezvous among humans due to their own inherent dynamics. As a main result, we propose a coverage control for a distributed movement of multiple humans. We also characterize the stationary point analytically in terms of the gains that describe a strength of the interconnection of the agents, and the centers of the Voronoi regions related to the agents. Moreover, we verify the meaning of the characterization from an engineering viewpoint of the dense avoidance. Finally, we show the efficiency of the method based on a numerical simulation.

中文翻译：

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基于机器人覆盖控制的人类密集回避

在本文中，我们提出了一种基于覆盖控制的有效人类密集回避方法。我们的动机是从系统和控制理论中避免在 COVID-19 的现状下车站和政府办公室等公共设施拥挤，避免人员密集。在本文中，人类和机器人分别被建模为异构和同质代理，它们根据本地信息做出决策。我们假设由于人类自身固有的动力，人类之间的会合造成了密集的情况。作为主要结果，我们提出了针对多人分布式移动的覆盖控制。我们还根据描述代理互连强度的增益以及与代理相关的 Voronoi 区域的中心来分析地表征驻点。而且，我们从密集回避的工程角度验证了表征的意义。最后，我们展示了基于数值模拟的方法的效率。