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Summary

This study introduces an enhanced Human-Robot Interaction architecture using Internet-of-Things sensors for pre-interaction information gathering. This approach improves interlocutor identification and interaction parameters for smarter robotic systems.

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Area of Science:

  • Robotics
  • Human-Computer Interaction
  • Artificial Intelligence

Background:

  • Current Human-Robot Interaction (HRI) systems often lack comprehensive pre-interaction data acquisition.
  • Integrating external subsystems can significantly enhance the intelligence and adaptability of robotic systems.

Purpose of the Study:

  • To present a detailed HRI systems architecture focused on advanced information acquisition.
  • To introduce and evaluate custom Internet-of-Things (IoT)-based sensor subsystems for interlocutor identification and parameter acquisition.
  • To demonstrate the benefits of integrating these subsystems into an AI interaction framework.

Main Methods:

  • Development and implementation of IoT-based sensor subsystems connected to Smart Infrastructure.
  • Extension of an AI interaction framework with device-based human identification, visual identification, and audio-based localization subsystems.
  • Detailed analysis and prototype implementation of a Bluetooth Human Identification Smart Subsystem within a real-world office environment.

Main Results:

  • The proposed architecture enables robust interlocutor identification and acquisition of initial interaction parameters before interaction begins.
  • Integration of external subsystems (e.g., Bluetooth Human Identification) enhances the robotic system's knowledge base and interaction capabilities.
  • A functional prototype demonstrated the feasibility and benefits of the integrated Smart Infrastructure and HRI system.

Conclusions:

  • The novel HRI architecture significantly improves interaction by leveraging pre-interaction data.
  • IoT and Smart Infrastructure integration offers a powerful approach to creating more informed and responsive robotic systems.
  • The evaluated subsystems provide a scalable and effective method for enhancing HRI.