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Human-Swarm Interaction: An Experimental Study of Two Types of Interaction with Foraging Swarms
| Citations: | 3 - 0 self |
BibTeX
@MISC{Kolling_human-swarminteraction:,
author = {Andreas Kolling and Katia Sycara and Steven Nunnally and Michael Lewis},
title = {Human-Swarm Interaction: An Experimental Study of Two Types of Interaction with Foraging Swarms},
year = {}
}
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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.
Keyphrases
control method human-swarm interaction foraging swarm experimental study different strategy selection control human operator beacon control task-specific benchmark complex environment swarm behavior autonomous swarm algorithm nearby robot structured obstacle set range fundamental type open environment simple autonomous swarm operator utilized different swarm behavior local communication basic behavior thirty-two participant first study active selection robotic swarm repeated simulation swarm system operator performance spatial influence beacon control differ appropriate swarm behavior information foraging problem swarm range different frequency different environment appropriate type autonomous swarm
