Human-swarm interaction
Human-swarm interaction
复制标题
人与群体的互动
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
M. Lewis
中科院分区：
文献类型：
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作者：
A. Kolling;K. Sycara;S. Nunnally;M. Lewis
在本文中，我们提出了人类群体相互作用的第一个研究，比较了两种基本的相互作用类型，即间歇性和环境。这些类型的例子是两种控制方法，选择和信标控制，使人类操作员可以控制一群觅食的机器人。选择和信标控制在时间和空间上对群体的影响是不同的，这使得操作员能够根据群体的基本行为产生不同的策略。选择控制要求主动选择一组机器人，信标控制在一定范围内对附近的机器人施加影响。这两种控制方法都是在一个试验台中实现的，在试验台中，操作者利用一组群体行为来解决信息觅食问题。机器人群只有局部通信和感知能力。蜂群中的机器人数量从50到200不等。在无障碍物、杂乱障碍物和结构化障碍物等不同环境下的一系列任务中，比较了操作员对每种控制方法的性能。此外，还将性能与简单和高级自治群进行了比较。该研究招募了32名参与者。在重复仿真中对自主群算法进行了测试。我们的研究结果表明，选择控制可以更好地扩展到更大的群体，并且通常优于信标控制。不同控制方式下，操作者使用不同频率的群体行为，表明不同控制方式对不同策略的适应性。在开放环境中，简单的自治群体的表现优于人类操作员，但操作员更能适应有障碍物的复杂环境。在所有条件下，人类控制的蜂群都达不到特定任务的基准。我们的研究结果强调了在设计群体系统时，除了选择适当的群体行为外，理解和选择适当的人群交互类型的重要性。
In this paper we present the first study of human-swarm interaction comparing two fundamental types of interaction, coined intermittent and environmental. These types are exemplified by two control methods, selection and beacon control, made available to a human operator to control a foraging swarm of robots. Selection and beacon control differ with respect to their temporal and spatial influence on the swarm and enable an operator to generate different strategies from the basic behaviors of the swarm. Selection control requires an active selection of groups of robots while beacon control exerts an influence on nearby robots within a set range. Both control methods are implemented in a testbed in which operators solve an information foraging problem by utilizing a set of swarm behaviors. The robotic swarm has only local communication and sensing capabilities. The number of robots in the swarm range from 50 to 200. Operator performance for each control method is compared in a series of missions in different environments with no obstacles up to cluttered and structured obstacles. In addition, performance is compared to simple and advanced autonomous swarms. Thirty-two participants were recruited for participation in the study. Autonomous swarm algorithms were tested in repeated simulations. Our results showed that selection control scales better to larger swarms and generally outperforms beacon control. Operators utilized different swarm behaviors with different frequency across control methods, suggesting an adaptation to different strategies induced by choice of control method. Simple autonomous swarms outperformed human operators in open environments, but operators adapted better to complex environments with obstacles. Human controlled swarms fell short of task-specific benchmarks under all conditions. Our results reinforce the importance of understanding and choosing appropriate types of human-swarm interaction when designing swarm systems, in addition to choosing appropriate swarm behaviors.