Evidence map›Paper›PMID 34109162›Full record

ArticleFrontiers in bioengineering and biotechnology2021

Social Integrating Robots Suggest Mitigation Strategies for Ecosystem Decay.

Thomas Schmickl, Martina Szopek, Francesco Mondada, Rob Mills, Martin Stefanec, Daniel N Hofstadler, Dajana Lazic, Rafael Barmak, Frank Bonnet, Payam Zahadat

Abstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Bioinspired robots can foster nature conservation.Frontiers in robotics and AI · 2023
    Article
  2. Article
  3. 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

10 authors.

Thomas SchmicklArtificial Life Laboratory of the Institute of Biology, University of Graz, Graz, Austria.
Martina SzopekArtificial Life Laboratory of the Institute of Biology, University of Graz, Graz, Austria.
Francesco MondadaMobile Robotic Systems Group, School of Engineering and School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Rob MillsMobile Robotic Systems Group, School of Engineering and School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Martin StefanecArtificial Life Laboratory of the Institute of Biology, University of Graz, Graz, Austria.
Daniel N HofstadlerArtificial Life Laboratory of the Institute of Biology, University of Graz, Graz, Austria.
Dajana LazicArtificial Life Laboratory of the Institute of Biology, University of Graz, Graz, Austria.
Rafael BarmakMobile Robotic Systems Group, School of Engineering and School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Frank BonnetMobile Robotic Systems Group, School of Engineering and School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Payam ZahadatDepartment of Computer Science, IT University of Copenhagen, Copenhagen, Denmark.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We develop here a novel hypothesis that may generate a general research framework of how autonomous robots may act as a future contingency to counteract the ongoing ecological mass extinction process. We showcase several research projects that have undertaken first steps to generate the required prerequisites for such a technology-based conservation biology approach. Our main idea is to stabilise and support broken ecosystems by introducing artificial members, robots, that are able to blend into the ecosystem's regulatory feedback loops and can modulate natural organisms' local densities through participation in those feedback loops. These robots are able to inject information that can be gathered using technology and to help the system in processing available information with technology. In order to understand the key principles of how these robots are capable of modulating the behaviour of large populations of living organisms based on interacting with just a few individuals, we develop novel mathematical models that focus on important behavioural feedback loops. These loops produce relevant group-level effects, allowing for robotic modulation of collective decision making in social organisms. A general understanding of such systems through mathematical models is necessary for designing future organism-interacting robots in an informed and structured way, which maximises the desired output from a minimum of intervention. Such models also help to unveil the commonalities and specificities of the individual implementations and allow predicting the outcomes of microscopic behavioural mechanisms on the ultimate macroscopic-level effects. We found that very similar models of interaction can be successfully used in multiple very different organism groups and behaviour types (honeybee aggregation, fish shoaling, and plant growth). Here we also report experimental data from biohybrid systems of robots and living organisms. Our mathematical models serve as building blocks for a deep understanding of these biohybrid systems. Only if the effects of autonomous robots onto the environment can be sufficiently well predicted can such robotic systems leave the safe space of the lab and can be applied in the wild to be able to unfold their ecosystem-stabilising potential.

Indexed as

biohybrid systemsbiomimicryecosystem collapseorganismic augmentationrobot–animal interactionrobot–organism interaction

Identifiers

PMID34109162
PMCPMC8181169

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.