Learning Swarm Interaction Dynamics From Density Evolution

Mavridis, Christos N.; Amoolya, Tirumalai,; Baras, John S.

doi:10.1109/tcns.2022.3198784

Cited by 7 publications

(4 citation statements)

References 33 publications

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“…We have excluded all articles that show no interaction with drone swarms. Our analysis reduced cumulative work by 32.20%, excluding articles such as [34], [35], [36].…”

Section: Third Stage: Selecting Relevant Articlesmentioning

confidence: 99%

Opportunities and Challenges in Human-Swarm Interaction: Systematic Review and Research Implications

Şiean¹,

Gradinaru²,

Gherman³

et al. 2023

IJACSA

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We conducted a Systematic Literature Review on scientific papers that examined the interaction between operators and drone swarms based on the use of a command and control center. We present the results of a meta-analysis of nine scientific papers published in the ACM DL and IEEE Xplore databases. Our findings show that research on human-drone swarm interaction shows a disproportionate interest in hand gestures compared to other input modalities for drone swarm control. Furthermore, all articles reviewed exclusively explored gestures and the size of the swarm used in the studies was limited, with a median of 3.0 and an average of 3.8 drones per study. We compiled an inventory of interaction modalities, recognition techniques, and application types from the scientific literature, which is presented in this paper. On the basis of our findings, we propose four areas for future research that can guide scientific investigations and practical developments in this field.

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“…We have excluded all articles that show no interaction with drone swarms. Our analysis reduced cumulative work by 32.20%, excluding articles such as [34], [35], [36].…”

Section: Third Stage: Selecting Relevant Articlesmentioning

confidence: 99%

Opportunities and Challenges in Human-Swarm Interaction: Systematic Review and Research Implications

Şiean¹,

Gradinaru²,

Gherman³

et al. 2023

IJACSA

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show abstract

“…Such approaches are aimed to enable more accessible state information representation and classification of emergent behaviours, especially for unknown swarms (Haeri et al, 2020). Mavridis et al, (2021) investigate coordinated movements of swarms, proposing a scheme to infer the laws for inter-agent coordination by observing the swarm density evolution over time (Mavridis et al, 2021).…”

Section: Swarm Indicatorsmentioning

confidence: 99%

Swarm analytics: Designing information markers to characterise swarm systems in shepherding contexts

et al. 2022

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Contemporary swarm indicators are often used in isolation, focussed on extracting information at the individual or collective levels. Consequently, these are seldom integrated to infer a top-level operating picture of the swarm, its members and its overall collective dynamics. The primary contribution of this paper is to organise a suite of indicators about swarms into an ontologically arranged collection of information markers to characterise the swarm from the perspective of an external observer – , a recognition agent. Our contribution shows the foundations for a new area of research that we title swarm analytics, whose primary concern is with the design and organisation of collections of swarm markers to understand, detect, recognise, track and learn a particular insight about a swarm system. We present our designed framework of information markers that offer a new avenue for swarm research, especially for heterogeneous and cognitive swarms that may require more advanced capabilities to detect agencies and categorise agent influences and responses.

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“…At the same time, there is an inverse approach from the required characteristics of the swarm behavior to the characteristics of the drone that are necessary to implement such behavior. In, it is proposed to use the patterns of coordination for interacting agents for observations of the evolution of the swarm density over time to describe a swarm [13]. As a result, the swarm is described by macroscopic parameters.…”

Section: Introductionmentioning

confidence: 99%

Approach to conception and modeling for distributed hierarchical control for autonomous drone swarm

2024

Adv Mach Lear Art Inte

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Control of a drone swarm as a unity requires decentralization and hierarchy. Decentralizing control of drone swarms is necessary to free the human operator from having to constantly control the behavior of the drones within the swarm. Hierarchical control of a drone swarm is necessary so that human operators can adjust the activity of the swarm as a unit. The following separate roles have been identified for the implementation of decentralized hierarchical control of swarm activity: the activity of a separate drone, the activity of a drone- coordinator, and the activity of a human operator. The control hierarchy consists of a human operator who controls the change in the behavior of the drone coordinator. The drone coordinator controls the changes in the programmed behavior of individual drones. This approach is an analog of the management of human workers who perform assigned work, which opens up several possibilities. First, it is possible to use formal models of performance people’s behavior in social teams. Second, formal models can be used for decision-making and optimization for controlling a drone-coordinator in a swarm. Thirdly, computer modeling can be applied to the behavior of a drone swarm, which will allow choosing the optimal behavior of the swarm for different conditions of its activity

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Learning Swarm Interaction Dynamics From Density Evolution

Cited by 7 publications

References 33 publications

Opportunities and Challenges in Human-Swarm Interaction: Systematic Review and Research Implications

Opportunities and Challenges in Human-Swarm Interaction: Systematic Review and Research Implications

Swarm analytics: Designing information markers to characterise swarm systems in shepherding contexts

Approach to conception and modeling for distributed hierarchical control for autonomous drone swarm

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