Investigating the effect of increasing robot group sizes on the human psychophysiological state in the context of human–swarm interaction

Podevijn, Gaëtan; O’Grady, Rehan; Mathews, Nithin; Gilles, Audrey; Fantini‐Hauwel, Carole; Dorigo, Marco

doi:10.1007/s11721-016-0124-3

Cited by 35 publications

(23 citation statements)

References 37 publications

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“…Controlling larger numbers of robots is likely to increase the cognitive load of the operator [31]. Cumulative cognitive load might be a function of attention required to supervise the robots [32], a robot's performance and autonomy when left unattended [33], and the need to manage dependencies between robots when they must coordinate to perform a task [34], in addition to other mission requirements such as communication with teammates.…”

Section: Operator Cognitive Loadmentioning

confidence: 99%

Planetary Exploration With Robot Teams: Implementing Higher Autonomy With Swarm Intelligence

St-Onge

Kaufmann

Panerati

et al. 2020

IEEE Robot. Automat. Mag.

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Since the beginning of space exploration, Mars and the Moon have been explored with orbiters, landers, and rovers. Over forty missions have targeted Mars, and more than a hundred, the Moon. Developing novel strategies and technologies for exploring celestial bodies continues to be a focus of space agencies. Multi-robot systems are particularly promising for planetary exploration, as they are more robust to individual failure and have the potential to explore larger areas; however, there are limits to how many robots an operator can individually control. We recently took part in the European Space Agency's interdisciplinary equipment test campaign (PANGAEA-X) at a Lunar/Mars analogue site in Lanzarote, Spain. We used a heterogeneous fleet of Unmanned Aerial Vehicles (UAVs)-a swarm-to study the interplay of systems operations and human factors. Human operators directed the swarm via ad-hoc networks and data sharing protocols to explore unknown areas under two control modes: one in which the operator instructed each robot separately; and the other in which the operator provided general guidance to the swarm, which self-organized via a combination of distributed decision-making, and consensus building. We assessed cognitive load via pupillometry for each condition, and perceived task demand and intuitiveness via self-report. Our results show that implementing higher autonomy with swarm intelligence can reduce workload, freeing the operator for other tasks such as overseeing strategy, and communication. Future work will further leverage advances in swarm intelligence for exploration missions.

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Section: Operator Cognitive Loadmentioning

confidence: 99%

Planetary Exploration With Robot Teams: Implementing Higher Autonomy With Swarm Intelligence

St-Onge

Kaufmann

Panerati

et al. 2020

IEEE Robot. Automat. Mag.

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“…The model was able to predict a standard measure of situation awareness. Podevijn et al [114] study the psychophysiological state of the participants when they interact with a swarm of robots. A direct relationship was found between user state and number of robots which the user is exposed to and an increase in arousal value was observed when the user was exposed to 24 robots.…”

Section: Human Robot Interaction (Hri) Studiesmentioning

confidence: 99%

A Survey on Psycho-Physiological Analysis & Measurement Methods in Multimodal Systems

Baig¹,

Kavakli²

2019

MTI

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Psycho-physiological analysis has gained greater attention in the last few decades in various fields including multimodal systems. Researchers use psychophysiological feedback devices such as skin conductance (SC), Electroencephalography (EEG) and Electrocardiography (ECG) to detect the affective states of the users during task performance. Psycho-physiological feedback has been successful in detection of the cognitive states of users in human-computer interaction (HCI). Recently, in game studies, psycho-physiological feedback has been used to capture the user experience and the effect of interaction on human psychology. This paper reviews several psycho-physiological, cognitive, and affective assessment studies and focuses on the use of psychophysiological signals in estimating the user's cognitive and emotional states in multimodal systems. In this paper, we review the measurement techniques and methods that have been used to record psycho-physiological signals as well as the cognitive and emotional states in a variety of conditions. The aim of this review is to conduct a detailed study to identify, describe and analyze the key psycho-physiological parameters that relate to different mental and emotional states in order to provide an insight into key approaches. Furthermore, the advantages and limitations of these approaches are also highlighted in this paper. The findings state that the classification accuracy of >90% has been achieved in classifying emotions with EEG signals. A strong correlation between self-reported data, HCI experience, and psychophysiological data has been observed in a wide range of domains including games, human-robot interaction, mobile interaction, and simulations. An increase in β and γ-band activity have been observed in high intense games and simulations.

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“…They found that human workload is not dependent on the number of robots when interacting with a robot swarm. Podevijn et al (2016) studied the effect of the robot swarm size on the human psychophysiological state. They found that higher robot swarm sizes provoke stronger psychophysiological responses.…”

Section: Related Literaturementioning

confidence: 99%

“…With the exception of Setter et al (2015) and Podevijn et al (2016), all the works that study the psychology of humans interacting with a robot swarm are performed in simulation only. Due to the inherent existence of the reality gap, though, it is not clear if human-swarm interaction studies performed in simulation only would provoke the same psychological reactions as the same human-swarm interaction studies performed with a robot swarm made up of real robots.…”

Section: Related Literaturementioning

confidence: 99%

Investigating the effect of the reality gap on the human psychophysiological state in the context of human-swarm interaction

Podevijn

O’Grady

Fantini‐Hauwel

et al. 2016

PeerJ Computer Science

Self Cite

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The reality gap is the discrepancy between simulation and reality-the same behavioural algorithm results in different robot swarm behaviours in simulation and in reality (with real robots). In this paper, we study the effect of the reality gap on the psychophysiological reactions of humans interacting with a robot swarm. We compare the psychophysiological reactions of 28 participants interacting with a simulated robot swarm and with a real (non-simulated) robot swarm. Our results show that a real robot swarm provokes stronger reactions in our participants than a simulated robot swarm. We also investigate how to mitigate the effect of the reality gap (i.e., how to diminish the difference in the psychophysiological reactions between reality and simulation) by comparing psychophysiological reactions in simulation displayed on a computer screen and psychophysiological reactions in simulation displayed in virtual reality. Our results show that our participants tend to have stronger psychophysiological reactions in simulation displayed in virtual reality (suggesting a potential way of diminishing the effect of the reality gap).

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Investigating the effect of increasing robot group sizes on the human psychophysiological state in the context of human–swarm interaction

Cited by 35 publications

References 37 publications

Planetary Exploration With Robot Teams: Implementing Higher Autonomy With Swarm Intelligence

Planetary Exploration With Robot Teams: Implementing Higher Autonomy With Swarm Intelligence

A Survey on Psycho-Physiological Analysis & Measurement Methods in Multimodal Systems

Investigating the effect of the reality gap on the human psychophysiological state in the context of human-swarm interaction

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