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Stigmergy and Self-Organizing Systems in Swarm Robotics: A Systematic Review.

Luigi Maciel Ribeiro1, Nadia Nedjah2, Luiza de Macedo Mourelle1

  • 1Department of Systems and Computer Engineering, State University of Rio de Janeiro, Rio de Janeiro 20550-013, Brazil.

Sensors (Basel, Switzerland)
|July 15, 2026
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Summary

This review analyzes stigmergy and self-organization in swarm robotics, finding these principles enhance robot robustness and adaptability. Hybrid solutions combining swarm optimization, distributed learning, and adaptive control are increasingly popular.

Keywords:
PRISMA 2020bio-inspired computingcollective intelligencemulti-agent systemsself-organizingstigmergyswarm intelligenceswarm roboticssystematic review

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Area of Science:

  • Robotics and Artificial Intelligence
  • Complex Systems Science
  • Computational Intelligence

Background:

  • Swarm robotics leverages collective behavior for complex tasks.
  • Stigmergy and self-organization are key mechanisms driving emergent behavior in swarms.
  • Understanding these mechanisms is crucial for designing advanced robotic systems.

Purpose of the Study:

  • To conduct a bibliometric, thematic, and epistemological analysis of stigmergy and self-organization in swarm robotics.
  • To synthesize findings from 338 scientific works published between June 2025 and April 2026.
  • To identify trends, limitations, and future research directions in the field.

Main Methods:

  • Systematic literature review following PRISMA 2020 guidelines.
  • Inclusion of journal articles on stigmergy, self-organization, and swarm robotics.
  • Exclusion of duplicate, irrelevant, or methodologically insufficient studies.
  • Screening by three independent reviewers.
  • Synthesis of results through four analytical axes.

Main Results:

  • Stigmergy and self-organization principles significantly enhance robustness, scalability, and adaptability in swarm robots.
  • Hybrid solutions integrating swarm optimization, distributed learning, and adaptive control are gaining popularity.
  • Analysis of 338 works reveals diverse approaches and applications.

Conclusions:

  • The study provides an integrated conceptual framework for understanding stigmergy and self-organization in swarm robotics.
  • Identified limitations include methodological fragmentation, lack of benchmarking, and underrepresentation of computational/physical perspectives.
  • Future research should address multi-scale modeling and bridge computational and physical aspects.