Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.

翻译：尽管机器人集群具有诸多优势，但在实际应用中仍需人类监督。人-集群系统的性能取决于多个因素，包括操作员可获取的数据可用性。本文从人因工程角度研究人-集群交互，探究高质量数据的获取如何影响人-集群系统的性能（即完成任务的数量及操作中的信任水平）。我们设计了一项实验，要求操作员操控集群在指定时间内识别某一区域内的伤亡人员。其中一组操作员可请求获取高质量图像，而另一组则需依据现有低质量图像做出决策。我们招募了120名参与者开展用户研究，记录了他们的成功率（通过仿真平台直接记录），以及工作负荷与信任水平（通过完成人-集群任务后的问卷测量）。研究结果表明，获得高质量数据访问权限的组别工作负荷更高，且对集群的信任度更强，这证实了我们最初的假设。然而，我们也发现两组在准确识别伤亡人员数量上并无显著差异，这表明数据质量对任务的成功完成没有影响。