Neglect benevolence in human control of swarms in the presence of latency

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.

I. INTRODUCTION

Robotic swarms are made up of small, homogeneous robots with limited capabilities that act through local interactions to collectively achieve a variety of behaviors including flocking [1], [2], [3], [4], deployment [5], [6], and foraging [7], [8]. The principal advantage of swarms is that, due to their large numbers and emergent behaviors, they are typically robust to failure of individual robots. For using swarm robotic systems in human-supervised missions, it is imperative to understand the basic tenets of human-swarm interaction (HSI) [9]. Key characteristics of swarm robotic systems that make HSI challenging are (a) the swarm behavior is self-stabilizing and takes some time to emerge (b) individual robots in a swarm are very simple units with limited communication hardware capabilities and (c) robots have poor localization capabilities leading to errors in interpreting the input of the operator. The communication latency and the input error along with the self-stabilizing nature of the emergent behaviors of robotic swarms create challenges for the operator in understanding the current state of the swarm and the effect of his or her command.