Abstract-Recent advances in technology are delivering robots of reduced size and cost. A natural outgrowth of these advances are systems comprised of large numbers of robots that collaborate autonomously in diverse applications. Research on effective autonomous control of such systems, commonly called swarms, has increased dramatically in recent years and received attention from many domains, such as bioinspired robotics and control theory. These kinds of distributed systems present novel challenges for the effective integration of human supervisors, operators, and teammates that are only beginning to be addressed. This paper is the first survey of human-swarm interaction (HSI) and identifies the core concepts needed to design a human-swarm system. We first present the basics of swarm robotics. Then, we introduce HSI from the perspective of a human operator by discussing the cognitive complexity of solving tasks with swarm systems. Next, we introduce the interface between swarm and operator and identify challenges and solutions relating to human-swarm communication, state estimation and visualization, and human control of swarms. For the latter, we develop a taxonomy of control methods that enable operators to control swarms effectively. Finally, we synthesize the results to highlight remaining challenges, unanswered questions, and open problems for HSI, as well as how to address them in future works.Index Terms-Human-robot interaction (HRI), human-swarm interaction (HSI), multi-robot systems, swarm robotics.

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Autonomous swarm algorithms have been studied extensively in the past several years. However, there is little research on the effect of injecting human influence into a robot swarm-whether it be to update the swarm's current goals or reshape swarm behavior. While there has been growing research in the field of human-swarm interaction (HSI), no previous studies have investigated how humans interact with swarms under communication latency. We investigate the effects of latency both with and without a predictive display in a basic swarm foraging task to see if such a display can help mitigate the effects of delayed feedback of the swarm state. Furthermore, we introduce a new concept called neglect benevolence to represent how a human operator may need to give time for swarm algorithms to stabilize before issuing new commands, and we investigate it with respect to task performance. Our study shows that latency did affect a user's ability to control a swarm to find targets in the foraging task, and that the predictive display helped to remove these effects. We also found evidence for neglect benevolence, and that operators exploited neglect benevolence in different ways, leading to two different, but equally successful strategies in the target-searching task.

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