Evidence map›Paper›PMID 37234508›Full record

ArticleRoyal Society open science2023

Steering herds away from dangers in dynamic environments.

Stef Van Havermaet, Pieter Simoens, Tim Landgraf, Yara Khaluf

Abstract read
In one paragraph

Article in Royal Society open science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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.

Stef Van HavermaetDepartment of Information Technology, University of Ghent-imec, Technologiepark 126, 9052 Ghent, Belgium.ORCID 0000-0002-5060-6807
Pieter SimoensDepartment of Information Technology, University of Ghent-imec, Technologiepark 126, 9052 Ghent, Belgium.ORCID 0000-0002-9569-9373
Tim LandgrafDepartment of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 7, 14195 Berlin, Germany.
Yara KhalufDepartment of Information Technology, University of Ghent-imec, Technologiepark 126, 9052 Ghent, Belgium.ORCID 0000-0002-5590-9321

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Shepherding, the task of guiding a herd of autonomous individuals in a desired direction, is an essential skill to herd animals, enable crowd control and rescue from danger. Equipping robots with the capability of shepherding would allow performing such tasks with increased efficiency and reduced labour costs. So far, only single-robot or centralized multi-robot solutions have been proposed. The former is unable to observe dangers at any place surrounding the herd, and the latter does not generalize to unconstrained environments. Therefore, we propose a decentralized control algorithm for multi-robot shepherding, where the robots maintain a caging pattern around the herd to detect potential nearby dangers. When danger is detected, part of the robot swarm positions itself in order to repel the herd towards a safer region. We study the performance of our algorithm for different collective motion models of the herd. We task the robots to shepherd a herd to safety in two dynamic scenarios: (i) to avoid dangerous patches appearing over time and (ii) to remain inside a safe circular enclosure. Simulations show that the robots are always successful in shepherding when the herd remains cohesive, and enough robots are deployed.

Indexed as

collective motiondecentralized decision-makingmulti-agent systemshepherding

Identifiers

PMID37234508
PMCPMC10206474

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.