Characterizing Motion Prediction in Small Autonomous Swarms

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.