BEST PAPER AWARD AT THE HUMAN FACTORS AND ERGONOMICS SOCIETY-COMPUTER SYSTEMS TECHNICAL GROUP (HFES CSTG 2013)
Autonomous swarm algorithms and human-robot interaction (HRI) have both attracted increasing attention from researchers in recent years. However, HRI has rarely extended beyond single robots or small multi-robot teams. While one of the benefits of swarm robotics is their robust capabilities and the ability of their distributed algorithms to deal autonomously with the complex interactions amongst swarm members, there is undoubtedly a need for humans to influence such swarms in some circumstances—especially when these swarms are operating in unknown or hostile environments. In this paper, we approach the problem of human-swarm interaction (HSI) using previous research in levels of automation (LOAs) in HRI. We create a target searching task whereby the swarm can operate at two different levels of autonomy: an autonomous dispersion algorithm, or user-defined goto points. We investigate what environmental conditions are conducive to different amounts of human influence, and at what point further human intervention has a detrimental effect on the swarm’s performance. The results show that for complex environments containing numerous obstacles and small passageways, there is indeed a need for some human influence; however, after a certain point, further influence causes performance degradation.