A Graph-based Approach for Shepherding Swarms with Limited Sensing Range

Mohamed, Reem E.; Elsayed, Saber M.; Hunjet, Robert; Abbass, Hussein A.

doi:10.1109/cec45853.2021.9504706

Cited by 8 publications

(6 citation statements)

References 12 publications

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“…Centre-of-mass, also known as centre-of-gravity, based approaches are a standard method to control a swarm, with few assumptions around agent homogeneity or influence distribution within the swarm. Current approaches are often data-driven, operating on spatially-derived features to calculate the next move of an agent, for instance El-Fiqi et al (2020); Mohamed, Elsayed, Hunjet, and Abbass (2021). Biologically-inspired models use derivations from empirical research as the basis for agent parameterisations.…”

Section: Methods Of Swarm Controlmentioning

confidence: 99%

“…The predominant approach to extensions of rule-or heuristic-based methods employ arc, line or circle formations, usually relying on the centre of mass and exact position of agents to conduct guidance. Few studies research the impact of limited sensing ranges and the use of only local information by the control agent, for instance El-Fiqi et al (2020); Mohamed et al (2021).…”

Section: Methods Of Swarm Controlmentioning

confidence: 99%

“…Hussein et al (2022) consider curriculum-based reinforcement learning and propose an algorithm that demonstrates superior performance to that of the classic rules-based agent across all training and task types. Mohamed et al (2021) introduce a graphbased approach that promotes cohesion between swarm members, improving shepherding performance and mission success outcomes.…”

Section: Models Of Shepherdingmentioning

confidence: 99%

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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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Section: Methods Of Swarm Controlmentioning

confidence: 99%

Section: Methods Of Swarm Controlmentioning

confidence: 99%

Section: Models Of Shepherdingmentioning

confidence: 99%

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Contextually Aware Intelligent Control Agents for Heterogeneous Swarms

Hepworth

Hussein

Reid

et al. 2022

Preprint

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“…Enhancing cohesion of a swarm by representing it via graph metrics was discussed in ref. [23]. Particle swarm optimization was used to optimize the distance between the flock and the herd's goal zone, subject to cohesion constraints.…”

Section: Related Workmentioning

confidence: 99%

Adversarial scenarios for herding UAVs and counter-swarm techniques

Vasquez

Barca²

2023

Robotica

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The present paper aims to design and simulate an adversarial strategy where a swarm of quadrotor UAVs is herding anti-aircraft land vehicles (AALV) that actively oppose the swarm’s objective by potentially taking them down. The main strategy is to block the AALVs’ line of sight to their goal zone (AALVs’ objective), shifting its trajectory so it reaches a kill zone instead (UAVs’ objective). The counter-swarm strategy performed by the AALVs consists of taking down the closest aerial units to the goal zone. As a result, a consensus algorithm is executed by the UAVs in order to assess the communication network and re-group. Consensus is based on the propagation of local observations that converge into a global agreement on a communication graph. Re-grouping is done via positioning around the kill zone vector or preferring an anti-clockwise formation to better close gaps. The adversarial strategy was tested in an empty arena and urban setting, the latter making use of a path-planning procedure that re-routes the AALV trajectory based on its current destination. Simulation results show a maximum UAV mission success rate converging to roughly 80% in the empty arena. When targeted elimination procedures are executed, UAV mission performance drops 5%, making no distinction between re-grouping strategies in the empty arena. The urban setting shows lower performance due to navigation complexity but favors the decision to re-group based on a formation that close gaps rather than positioning around the kill zone vector.

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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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A Graph-based Approach for Shepherding Swarms with Limited Sensing Range

Cited by 8 publications

References 12 publications

Contextually Aware Intelligent Control Agents for Heterogeneous Swarms

Contextually Aware Intelligent Control Agents for Heterogeneous Swarms

Adversarial scenarios for herding UAVs and counter-swarm techniques

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

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