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Adaptivity: a path towards general swarm intelligence?

Hian Lee Kwa1, Jabez Leong Kit2, Nikolaj Horsevad3

  • 1Thales Research and Technology, Singapore, Singapore.

Frontiers in Robotics and AI
|May 25, 2023
PubMed
Summary
This summary is machine-generated.

Multi-robot systems (MRS) excel in complex tasks but struggle in dynamic environments. Adaptivity is key to enhancing swarm intelligence and performance in multi-robot swarms.

Keywords:
adaptivitycollective roboticsmulti-agent systemsmulti-robot systemsswarm intelligenceswarm robotics

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Complex Systems

Background:

  • Multi-robot systems (MRS) offer advantages over single robots for distributed tasks.
  • Traditional MRS struggle in unstructured, dynamic environments due to centralized planning limitations.
  • Swarming MRS show promise due to inherent adaptivity.

Purpose of the Study:

  • To emphasize the critical role of adaptivity in multi-robot system swarming.
  • To establish adaptivity as a foundation for defining and quantifying swarm intelligence.
  • To advocate for benchmark tests to measure swarm adaptivity.

Main Methods:

  • Literature review and conceptual analysis of adaptivity in MRS.
  • Exploration of adaptivity's link to swarm intelligence.
  • Proposal for developing standardized adaptivity benchmarks.

Main Results:

  • Adaptivity is identified as the core capability enabling effective multi-robot swarming.
  • Adaptivity provides a quantifiable metric for swarm intelligence.
  • The need for standardized testing protocols for swarm adaptivity is highlighted.

Conclusions:

  • Focusing on adaptivity is crucial for advancing swarm robotics.
  • Developing benchmark tests will accelerate progress in swarm intelligence.
  • Enhanced adaptivity in MRS will lead to more capable and robust swarm systems.