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SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
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Sensor-Driven Human-Robot Synergy: A Systems Engineering Approach.

Naoum Tsolakis1,2, Antonios Gasteratos3

  • 1Department of Supply Chain Management, International Hellenic University, 570 01 Thessaloniki, Greece.

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|January 8, 2023
PubMed
Summary
This summary is machine-generated.

Knowledge-based synergistic automation enhances farming by integrating skilled workers with robotic systems. This approach uses advanced information and communication technologies (ICT) and human-robot interaction (HRI) for efficient, productive agricultural tasks.

Keywords:
data and information flowsdecision structure matrixhuman-centric agriculturehuman–robot collaborationhuman–robot synergysystems engineering

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Area of Science:

  • Agricultural Engineering
  • Robotics
  • Human-Computer Interaction

Background:

  • Manual labor and full automation present limitations in agriculture.
  • Information and Communication Technologies (ICT) and Human-Robot Interaction (HRI) offer new possibilities.
  • Synergistic automation provides a middle ground, enhancing farmer capabilities.

Purpose of the Study:

  • To apply systems engineering principles to design a conceptual human-robot synergistic platform.
  • To assess a sensor-driven ICT sub-system for agricultural applications.
  • To develop a human-centric worker-robot logistics synergy for high-value crops.

Main Methods:

  • A use case involving a human-robot synergistic platform (drone, mobile platform, wearable equipment) was presented.
  • An extended sensor network within the ICT sub-system was designed for data acquisition and context extraction.
  • Design Structure Matrix (DSM) was employed to map interrelations between sensors and data signals.

Main Results:

  • The research presents a technology framework for human-centric worker-robot logistics synergy.
  • The ICT sub-system enables context awareness (worker status, environment) for planning robotic agents.
  • The DSM analysis provides a clear understanding of the sensor sub-system architecture and its interrelations.

Conclusions:

  • The Systems Engineering approach offers a comprehensible analysis of human-robot synergy platforms.
  • DSM aids in synthesizing sensor sub-system architecture and improving efficacy through targeted interventions.
  • This human-centric, data-driven approach is valuable for optimizing farming operations and similar applications.