Scalable Human Interaction with Robotic Swarms

In this paper we evaluate the scalability of human-swarm interaction (HSI) in terms of operator workload and asses the impact of three control methods on swarm performance and operator workload. Specifically, we investigate the ability of HSI to (1) overcome fanout limitations of traditional supervisory control and (2) manage a wide range of team sizes without altering the operator’s control strategy. In order to evaluate our hypothesis, we conduct a user study which we back with validation on physical robots. We evaluate three high-level control methods – leader, predator, and stakeholders – on swarms of 20, 50, and 100 individuals. We find that larger swarm sizes allow increased swarm performance without increasing operator workload, overcoming the fan-out limitations of traditional supervisory control. We further find that control style substantially affects both swarm performance and operator workload, illustrating the impact design decisions can have on the human-swarm system.