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Delay induced swarm pattern bifurcations in mixed reality experiments.

Victoria Edwards1, Philip deZonia2, M Ani Hsieh2

  • 1Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, Washington, DC 20375, USA.

Chaos (Woodbury, N.Y.)
|August 6, 2020
PubMed
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Communication delays in robot swarms create diverse spatial patterns. Experiments validate these delay-induced dynamic patterns, revealing multi-stable rotational behaviors not predicted by theory.

Area of Science:

  • Robotics
  • Complex Systems
  • Control Theory

Background:

  • Coupled mobile agents exhibit complex spatiotemporal dynamics influenced by interaction strength and communication delays.
  • Understanding these dynamics is crucial for designing robust and predictable swarm behaviors.

Purpose of the Study:

  • To experimentally demonstrate communication delay-induced bifurcations in robot swarm spatiotemporal patterns.
  • To validate theoretically predicted parameter regions for pattern transitions in robotic swarms.
  • To investigate the persistence of rotational patterns with collision avoidance and explore multi-stable dynamics.

Main Methods:

  • Utilized two distinct hardware platforms within a mixed reality framework for experimental control.
  • Implemented collision avoidance strategies to assess their impact on swarm patterns.

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Last Updated: Jul 25, 2026

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
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  • Compared experimental results with theoretical predictions for pattern transitions.

    • Main Results:

    • Successfully demonstrated communication delay-induced bifurcations in robot swarm patterns.
    • Observed the persistence of multiple rotation patterns even with collision avoidance mechanisms.
    • Identified multi-stable, co-existing rotational patterns beyond standard mean-field predictions.

    Conclusions:

    • Experimental validation of delay-induced dynamic patterns in real robotic swarms.
    • Confirmation of pattern robustness and the existence of complex, multi-stable behaviors.
    • Significant progress toward bridging the gap between theoretical models and experimental robotic swarm systems.