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This study introduces a metaverse to control robot swarms, simplifying human interaction. The system enables effective control of uncrewed ground vehicles (UGVs) with adaptive autonomy, improving task performance.

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digital twingestural communicationhuman-swarm interactionlevels of autonomyshepherdingswarm metaverseswarm roboticssymbiotic simulation

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Area of Science:

  • Robotics
  • Human-Computer Interaction
  • Metaverse Technology

Background:

  • Robot swarms require sophisticated spatial coordination for complex tasks.
  • Scalable human control is crucial for aligning swarm behavior with dynamic system needs.
  • Existing human-swarm interaction techniques often lack real-world scalability.

Purpose of the Study:

  • To address the research gap in real-world scalable control of robot swarms.
  • To propose a metaverse framework for simplified and effective human-robot swarm interaction.
  • To introduce an adaptive framework supporting different levels of autonomy in swarm control.

Main Methods:

  • Development of a metaverse integrating physical robot swarms with virtual digital twins.
  • Implementation of logical control agents within the metaverse to manage sub-swarms.
  • A case study involving human gestural control of uncrewed ground vehicles (UGVs) via a virtual uncrewed aerial vehicle (UAV).

Main Results:

  • The proposed metaverse significantly reduces swarm control complexity by abstracting control to virtual agents.
  • Humans successfully controlled a swarm of UGVs using gestural commands within the metaverse.
  • Task performance demonstrated a positive correlation with increasing levels of autonomy.

Conclusions:

  • The metaverse provides a viable solution for scalable and intuitive human control of robot swarms in real-world applications.
  • The adaptive framework effectively supports varying levels of autonomy, enhancing swarm operational efficiency.
  • This approach facilitates complex swarm coordination and management through simplified human oversight.