多机器人动态群禁用

Ori Fogler, Noam Agmon
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引用次数: 0

摘要

在机器人用于农业害虫控制的激励下,这项工作引入了多机器人动态群禁用问题,其中需要一组机器人禁用通过一个区域的一群代理(例如,蝗虫代理),同时最小化群体成员在该区域的累积时间(相当于它们造成的累积损害)。即使在朴素的设置中,这个问题也很难解决,我们转而研究通过利用群体代理的已知运动模式来优化机器人对抗群体的性能的算法。当一群弱小的机器人试图抓住一大群智能体时,无论是数量上的少数还是速度差,我们都建议使用阻塞线:机器人形成阻挡智能体在环境中运动的线。我们通过理论分析和在不同环境下的严格经验评估表明,这些算法优于普通的基于任务分配的算法,特别是对于有限的机器人和大群机器人。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Robot Dynamic Swarm Disablement
Motivated by the use of robots for pest control in agriculture, this work introduces the Multi-Robot Dynamic Swarm Disablement problem, in which a team of robots is required to disable a swarm of agents (for example, locust agents) passing through an area while minimizing the cumulative time of the swarm members (equivalent to the cumulative damage they cause) in the area. Showing that the problem is hard even in naive settings, we turn to examine algorithms seeking to optimize the robots' performance against the swarm by exploiting the known movement pattern of the swarm agents. Motivated by the poor performance when a weak group of robots attempts to catch a large swarm of agents, whether it is a significant numerical minority or poor speed gaps, we suggest the use of blocking lines: the robots form lines that block the agents along their movement in the environment. We show by both theoretical analysis and rigorous empirical evaluation in different settings that these algorithms outperform common task-assignment-based algorithms, especially for limited robots versus a large swarm.
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