Center for Applied Brain and Cognitive Sciences, Medford, MA, USA; U.S. Army Combat Capabilities Development Command Soldier Center, Natick, MA, USA.
Department of Psychological & Brain Sciences, University of California Santa Barbara, Santa Barbara, CA, USA.
Appl Ergon. 2023 Jan;106:103909. doi: 10.1016/j.apergo.2022.103909. Epub 2022 Oct 12.
The use of robotic swarms has become increasingly common in research, industrial, and military domains for tasks such as collective exploration, coordinated movement, and collective localization. Despite the expanded use of robotic swarms, little is known about how swarms are perceived by human operators. To characterize human-swarm interactions, we evaluate how operators perceive swarm characteristics, including movement patterns, control schemes, and occlusion. In a series of experiments manipulating movement patterns and control schemes, participants tracked swarms on a computer screen until they were occluded from view, at which point participants were instructed to estimate the spatiotemporal dynamics of the occluded swarm by mouse click. In addition to capturing mouse click responses, eye tracking was used to capture participants eye movements while visually tracking swarms. We observed that manipulating control schemes had minimal impact on the perception of swarms, and that swarms are easier to track when they are visible compared to when they were occluded. Regarding swarm movements, a complex pattern of data emerged. For example, eye tracking indicates that participants more closely track a swarm in an arc pattern compared to sinusoid and linear movement patterns. When evaluating behavioral click-responses, data show that time is underestimated, and that spatial accuracy is reduced in complex patterns. Results suggest that measures of performance may capture different patterns of behavior, underscoring the need for multiple measures to accurately characterize performance. In addition, the lack of generalizable data across different movement patterns highlights the complexity involved in the perception of swarms of objects.
机器人集群的使用在研究、工业和军事领域变得越来越普遍，用于集体探索、协调运动和集体定位等任务。尽管机器人集群的使用范围不断扩大，但人们对人类操作员如何感知集群知之甚少。为了描述人机群交互，我们评估了操作员如何感知集群特征，包括运动模式、控制方案和遮挡。在一系列操纵运动模式和控制方案的实验中，参与者在计算机屏幕上跟踪集群，直到它们被遮挡视线，此时参与者被指示通过鼠标点击来估计被遮挡的集群的时空动态。除了捕获鼠标点击响应外，眼动追踪还用于在视觉跟踪集群时捕获参与者的眼球运动。我们观察到，操纵控制方案对集群的感知几乎没有影响，并且与被遮挡相比，当集群可见时，集群更容易跟踪。关于集群运动，出现了一种复杂的数据模式。例如，眼动追踪表明，与正弦和线性运动模式相比，参与者在弧形模式下更紧密地跟踪集群。在评估行为点击响应时，数据显示时间被低估，并且在复杂模式下空间精度降低。结果表明，性能指标可能会捕获不同的行为模式，突出了需要多种措施来准确描述性能的必要性。此外，不同运动模式之间缺乏可推广的数据突出了感知物体集群的复杂性。
