On the effect of swarm collective behavior on human perception: Towards brain-swarm interfaces

Karavas, George K.; Artemiadis, Panagiotis

doi:10.1109/mfi.2015.7295804

Cited by 10 publications

(2 citation statements)

References 11 publications

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“…Very few research groups have studied to control a drone swarm. Karavas et al [20] examined the perception and representation of collective behaviors of swarms at the brain level of human supervisors. They extracted event related potentials at EEG signals.…”

Section: Decoding User Intentionmentioning

confidence: 99%

Towards Brain-Computer Interfaces for Drone Swarm Control

Jeong

Lee

Ahn

et al. 2020

2020 8th International Winter Conference on Brain-Computer Interface (BCI)

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Noninvasive brain-computer interface (BCI) decodes brain signals to understand user intention. Recent advances have been developed for the BCI-based drone control system as the demand for drone control increases. Especially, drone swarm control based on brain signals could provide various industries such as military service or industry disaster. This paper presents a prototype of a brain-swarm interface system for a variety of scenarios using a visual imagery paradigm. We designed the experimental environment that could acquire brain signals under a drone swarm control simulator environment. Through the system, we collected the electroencephalogram (EEG) signals with respect to four different scenarios. Seven subjects participated in our experiment and evaluated classification performances using the basic machine learning algorithm. The grand average classification accuracy is higher than the chance level accuracy. Hence, we could confirm the feasibility of the drone swarm control system based on EEG signals for performing high-level tasks.

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Section: Decoding User Intentionmentioning

confidence: 99%

Towards Brain-Computer Interfaces for Drone Swarm Control

Jeong

Lee

Ahn

et al. 2020

2020 8th International Winter Conference on Brain-Computer Interface (BCI)

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“…To this point, [15] found that increasing the number of robots in a swarm is not detrimental to the operators cognitive load when control is performed on the entire swarm. Additionally, [16] showed that as swarm cohesion decreases, changes in swarm motion are less detectable. Individual Gestalt factors, such as similarity, proximity, symmetry, and synchrony are also not specific to static visual information, but apply also to moving objects [17].…”

Section: Introductionmentioning

confidence: 99%

Bots of a Feather: Exploring User Perceptions of Group Cohesiveness for Application in Robotic Swarms

Stower¹,

Zibetti

St-Onge

2022

2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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Behaviours of robot swarms often take inspiration from biological models, such as ant colonies and bee hives. Yet, understanding how these behaviours are actually perceived by human users has so far received limited attention. In this paper, we use animations to represent different kinds of possible swarm motions intended to communicate specific messages to a human. We explore how these animations relate to the perceived group cohesiveness of the swarm, comprised of five different parameters: synchronising, grouping, following, reacting, and shape forming. We conducted an online user study where 98 participants viewed nine animations of a swarm displaying different behaviours and rated them for perceived group cohesiveness. We found that the parameters of group cohesiveness correlated with the messages the swarm was perceived as communicating. In particular, the message of initiating communication was highly positively correlated with all group parameters, whereas broken communication was negatively correlated. In addition, the importance of specific group parameters differed within each animation. For example, the parameter of grouping was most associated with animations signalling an intervention is needed. These findings are discussed within the context of designing intuitive behaviour for robot swarms.

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