Levels of Automation for Human Influence of Robot Swarms

Abstract

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 robot swarms 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.

Human Interaction With Robot Swarms: A Survey

“…In fact, the formation of subgroups is a common feature in studies with complex application scenarios as in [102], [103], and [135], and envisioned in [88], [90], and [91]. Therefore, it can be argued that the problem of dividing a swarm into subswarms is superimposed over our control taxonomy and that most operators are going to be confronted with the issue of selecting and managing multiple groups of swarm robots.…”

“…A human-swarm system would fall high on the scale-greater than or equal to 7. This model for levels of automation has been used in numerous works studying HRI and HSI (see [103] and [135] for recent examples).…”

“…The primary conclusion is that operators had some positive impact on system performance but that much further work remains to be done to better integrate human and swarm autonomy. Similarly, in [103], the authors use two switchable modes of operation for a swarm to allow the human operator to switch between high and low autonomy. The high autonomy was captured via a dispersion algorithm whereby the swarm members spread to cover the open space in the environment, and the low autonomy mode allowed the user to select subsections of the swarm to direct via waypoints.…”

“…A comparison between behavior selection and environmental influence in [102] indicated superior performance for behavior selection for novice operators. Behavior selection was also used in [103] and [104]. Successful control with behavior selection also presupposes that the operator can develop an understanding and has access to an appropriate visualization of the swarm dynamics [101], discussed earlier in Section III-C.…”

The effect of display type on operator prediction of future swarm states

“…Addressing each member of a swarm individually would take a large amount of time and, due to the interdependent nature of the agents, would have unpredictable results. Therefore, researchers in the field of human swarm interaction (HSI) typically control a swarm by either switching between pre-programmed behaviors [3], [4], or adjusting the parameters of a single behavior [5], [6].…”