论文标题

基于模型的歧视的牧羊人异质群

Shepherding Heterogeneous Flock with Model-Based Discrimination

论文作者

Fujioka, Anna, Ogura, Masaki, Wakamiya, Naoki

论文摘要

通过少数外部试剂施加的排斥力将一群代理引导到目的地的问题称为牧羊问题。由于其潜在的应用,此问题引起了人们的关注,包括将鸟类转移出来,以防止飞机事故,在海洋中恢复溢油的油以及引导一群机器人进行映射。尽管已经对牧羊问题进行了各种研究,但其中大多数将统一性假设放在了要指导的代理动力学上。但是,我们可以找到这种假设不一定会成立的各种实际情况。在本文中,我们提出了一种牧羊方法,以使一群由普通剂和其他变体剂组成的药物组成。在这种方法中,牧羊人根据正常药物的潜在不准确模型预测的行为偏离正常和变异剂的偏差。至于歧视过程,我们使用静态和动态阈值提出了两种方法。我们的仿真结果表明,所提出的方法的表现优于各种类型变体剂的常规方法。

The problem of guiding a flock of agents to a destination by the repulsion forces exerted by a smaller number of external agents is called the shepherding problem. This problem has attracted attention due to its potential applications, including diverting birds away for preventing airplane accidents, recovering spilled oil in the ocean, and guiding a swarm of robots for mapping. Although there have been various studies on the shepherding problem, most of them place the uniformity assumption on the dynamics of agents to be guided. However, we can find various practical situations where this assumption does not necessarily hold. In this paper, we propose a shepherding method for a flock of agents consisting of normal agents to be guided and other variant agents. In this method, the shepherd discriminates normal and variant agents based on their behaviors' deviation from the one predicted by the potentially inaccurate model of the normal agents. As for the discrimination process, we propose two methods using static and dynamic thresholds. Our simulation results show that the proposed methods outperform a conventional method for various types of variant agents.

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