Evidence map›Paper›PMID 37430816›Full record

ArticleSensors (Basel, Switzerland)2023

Swarm Metaverse for Multi-Level Autonomy Using Digital Twins.

Hung Nguyen, Aya Hussein, Matthew A Garratt, Hussein A Abbass

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Hung NguyenSchool of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2600, Australia.ORCID 0000-0002-0938-0930
Aya HusseinSchool of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2600, Australia.ORCID 0000-0003-3204-0712
Matthew A GarrattSchool of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2600, Australia.ORCID 0000-0003-0222-430X
Hussein A AbbassSchool of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2600, Australia.ORCID 0000-0002-8837-0748

Funding

Australian Research Council DP200101211
6 · The paper itself

Abstract

Robot swarms are becoming popular in domains that require spatial coordination. Effective human control over swarm members is pivotal for ensuring swarm behaviours align with the dynamic needs of the system. Several techniques have been proposed for scalable human-swarm interaction. However, these techniques were mostly developed in simple simulation environments without guidance on how to scale them up to the real world. This paper addresses this research gap by proposing a metaverse for scalable control of robot swarms and an adaptive framework for different levels of autonomy. In the metaverse, the physical/real world of a swarm symbiotically blends with a virtual world formed from digital twins representing each swarm member and logical control agents. The proposed metaverse drastically decreases swarm control complexity due to human reliance on only a few virtual agents, with each agent dynamically actuating on a sub-swarm. The utility of the metaverse is demonstrated by a case study where humans controlled a swarm of uncrewed ground vehicles (UGVs) using gestural communication, and via a single virtual uncrewed aerial vehicle (UAV). The results show that humans could successfully control the swarm under two different levels of autonomy, while task performance increases as autonomy increases.

Indexed as

digital twingestural communicationhuman-swarm interactionlevels of autonomyshepherdingswarm metaverseswarm roboticssymbiotic simulation

Identifiers

PMID37430816
PMCPMC10221815

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.