Human Swarm Interaction: An Experimental Study of Two Types of Interaction with Foraging Swarms

Abstract

In this paper we present the first study of human-swarm interaction comparing two fundamental types of interaction, coined intermittent and environmental. These types are exemplified by two control methods, selection and beacon control, made available to a human operator to control a foraging swarm of robots. Selection and beacon control differ with respect to their temporal and spatial influence on the swarm and enable an operator to generate different strategies from the basic behaviors of the swarm. Selection control requires an active selection of groups of robots while beacon control exerts an influence on nearby robots within a set range. Both control methods are implemented in a testbed in which operators solve an information foraging problem by utilizing a set of swarm behaviors. The robotic swarm has only local communication and sensing capabilities. The number of robots in the swarm range from 50 to 200. Operator performance for each control method is compared in a series of missions in different environments with no obstacles up to cluttered and structured obstacles. In addition, performance is compared to simple and advanced autonomous swarms. Thirty-two participants were recruited for participation in the study. Autonomous swarm algorithms were tested in repeated simulations. Our results showed that selection control scales better to larger swarms and generally outperforms beacon control. Operators utilized different swarm behaviors with different frequency across control methods, suggesting an adaptation to different strategies induced by choice of control method. Simple autonomous swarms outperformed human operators in open environments, but operators adapted better to complex environments with obstacles. Human controlled swarms fell short of task-specific benchmarks under all conditions. Our results reinforce the importance of understanding and choosing appropriate types of human-swarm interaction when designing swarm systems, in addition to choosing appropriate swarm behaviors.

A Survey on Aerial Swarm Robotics

“…It has been argued that humans are able to guide a swarm better using a dynamic set of leaders [143], as compared to manipulating a fixed leader. There is also evidence which suggests that human operators can adapt their handling of (virtual) leaders to guide large swarms through obstacle-rich environments in a better manner than built-in, standard flocking rules [144]. The next level of sophistication involves the human issuing commands using natural language, while still staying outside the algorithmic loop that controls the swarm.…”

Human fitness functions

“…Dynamic Human-in-the-Swarm techniques are now being explored to provide solutions for this need. Research on a human directed dynamic robotic swarm was described in [7] and [8]. The authors of this research provide two forms of human-swarm behavior is driven by the "selection" and "beacon" human influence.…”

Regulating Highly Automated Robot Ecologies

“…One particularly relevant application of shared control [6] that has parallels to regulating HARE is human-swarm interaction (HSI) [16,3]. In HSI, an operator commands or influences a set of robots that have been programmed to mimic biological swarms.…”