Shepherding Behaviors with Single Shepherd in Crowd Management

Li, Meng; Hu, Zhiwei; Jia-hong, Liang; Li, Shilei

doi:10.1007/978-3-642-34381-0_48

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…One solution for the first challenge lies in shepherding (Long et al, 2020), a bio-inspired swarm guidance method that mimics how sheepdogs guide a swarm of sheep. The concept of shepherding for swarm guidance has been applied in many applications including agriculture (Strömbom et al, 2014), crowd control (Li et al, 2012; Mould et al, 2014) and uninhabited vehicle (UxV) navigation (Abbass & Hunjet, 2021b), and has proved viable in limited communication settings (Mohamed et al, 2021). The two remaining challenges also exist in shepherding research.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…These models often use static behaviour selection policies for the control agent to guide a swarm to a goal location Debie et al (2021). As a biologically-inspired approach to swarm control, shepherding has applications across different domains, such as the guidance and control of crowds Li, Hu, Liang, and Li (2012), herding biological animals Paranjape, Chung, Kim, and Shim (2018), guiding teams of uncrewed system (UxS) Hepworth (2021), and controlling a group of robotic platforms Cowling and Gmeinwieser (2010); Lee and Kim (2017).…”

Section: Introductionmentioning

confidence: 99%

Contextually Aware Intelligent Control Agents for Heterogeneous Swarms

Hepworth

Hussein

Reid

et al. 2022

Preprint

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An emerging challenge in swarm shepherding research is to design effective and efficient artificial intelligence algorithms that maintain a low-computational ceiling while increasing the swarm's abilities to operate in diverse contexts. We propose a methodology to design a context-aware swarm-control intelligent agent. The intelligent control agent (shepherd) first uses swarm metrics to recognise the type of swarm it interacts with to then select a suitable parameterisation from its behavioural library for that particular swarm type. The design principle of our methodology is to increase the situation awareness (i.e. information contents) of the control agent without sacrificing the low-computational cost necessary for efficient swarm control. We demonstrate successful shepherding in both homogeneous and heterogeneous swarms.

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Activity Recognition for Shepherding

Hepworth

2021

Unmanned System Technologies

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Shepherding Behaviors with Single Shepherd in Crowd Management

Cited by 4 publications

References 7 publications

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

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

Contextually Aware Intelligent Control Agents for Heterogeneous Swarms

Activity Recognition for Shepherding

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