Self-organized graph partitioning approach for multi-agent patrolling in generic graphs

Bernát Wiandt, V. Simon, András Kokuti
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引用次数: 6

Abstract

Recently multi-agent patrolling became more and more crucial in security, monitoring, etc. applications. It can be used, for example, to monitor points of interest in space, such as measurement points or entrances to a guarded area. A good patrolling strategy would ensure frequent visits to all points of interest defined by a user. A variety of centralized and distributed approaches exist already to the multi-agent patrolling problem, ranging from simple reactive solutions to sophisticated planning approaches. However there has been no self-organizing partition based solution proposed that can match and improve on the performance of already existing algorithms. In this article we introduce a self-organizing autonomous algorithm to partition a graph into non-overlapping subgraphs and assign the resulting subgraphs to agents to perform patrolling. We compare our solution to other patrolling algorithms in a thorough performance evaluation and show that our autonomous solution can outperform state of the art patrolling schemes.
通用图中多智能体巡逻的自组织图划分方法
近年来,多智能体巡逻在安防、监控等应用中发挥着越来越重要的作用。例如,它可以用于监视空间中的兴趣点,例如测量点或守卫区域的入口。良好的巡逻策略将确保频繁访问用户定义的所有兴趣点。针对多智能体巡逻问题,已经存在多种集中式和分布式的解决方法,从简单的反应式解决方案到复杂的规划方法。然而,目前还没有一种基于自组织分区的解决方案能够匹配和提高现有算法的性能。在本文中,我们引入了一种自组织自治算法,将图划分为不重叠的子图,并将结果子图分配给代理执行巡逻。我们将我们的解决方案与其他巡逻算法进行了全面的性能评估,并表明我们的自治解决方案可以优于最先进的巡逻方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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