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Cited by 134Modular Robots and Swarm IntelligenceDistributed Control Multi-Agent SystemsMetaheuristic Optimization Algorithms Research

Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots

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Duarte, Miguel, Costa, Vasco, Gomes, Jorge, Rodrigues, Tiago, Silva, Fernando, Oliveira, Sancho Moura, Christensen, Anders Lyhne·2016·PLOS ONE
DOI 10.1371/journal.pone.0151834PMC PMC4801206
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Abstract

Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers.

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  • Natural self-organizing systems (e.g., social insects, fish schools, bird flocks) are characterized by emergent collective behavior based on simple local interaction rules.

    “It takes inspiration from natural self-organizing systems, such as social insects, fish schools, or bird flocks, characterized by emergent collective behavior based on simple local interaction rules [1], [2].”

    From Swarm Robotics: Past, Present, and Future [Point of View] (2021) · cites this paper as [2]

    Section 2.1 (Swarm Robotics; page unavailable) says swarm robotics takes inspiration from social insects and describes their self-organized collective behavior. The Introduction links local interaction rules to emergent behavior in robot swarms. Our copy does not establish the claim for fish schools or bird flocks, or explicitly describe their local interaction rules. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Sorted by citations

Authors
Duarte, M., Costa, V., Gomes, J., Rodrigues, T., Silva, F., Oliveira, S. M., & Christensen, A. L.
Affiliations
  • Iscte – Instituto Universitário de Lisboa (PT)
  • University of Lisbon (PT)
Corresponding author
Duarte, Miguel
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2016-03-21
Volume
11
Issue
3
Pages
e0151834
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0151834
PMC
PMC4801206
OpenAlex
W2270784458
Cited by
134
References
1
Citations by year
2026: 9 · 2025: 8 · 2024: 9 · 2023: 15 · 2022: 10 · 2021: 8 · 2020: 18 · 2019: 20 · 2018: 19 · 2017: 7 · 2016: 11
FWCI
16.90
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
  • Repositório do ISCTE-IUL (submittedVersion, other-oa)
  • arXiv (Cornell University)
Funders
Fundação para a Ciência e a Tecnologia (EXPL/EEI-AUT/0329/2013, 50008, UID/EEA/50008/2013)
Topics
  • Physical Sciences › Engineering › Mechanical Engineering › Modular Robots and Swarm Intelligence
  • Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Control Multi-Agent Systems
  • Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
Keywords
swarm robotics, evolutionary robotics, neural network controller, swarm intelligence, collective behavior, homing, dispersion, clustering, environmental monitoring, scalability, robustness
MeSH terms
Portugal, Robotics, Task Performance and Analysis, Telecommunications, Cluster Analysis

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