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SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
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Human-Robot Perception in Industrial Environments: A Survey.

Andrea Bonci1, Pangcheng David Cen Cheng2, Marina Indri2

  • 1Dipartimento di Ingegneria dell'Informazione (DII), Università Politecnica delle Marche, 60131 Ancona, Italy.

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Summary
This summary is machine-generated.

This study surveys sensors for human detection and action recognition in industrial settings. It addresses safety and productivity in human-robot collaboration by analyzing perception techniques for various robotic systems.

Keywords:
3D sensorscollision avoidancecollision detectionhuman action recognitionhuman-robot collaborationhuman-robot perceptionmachine visionrobot guidance

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Area of Science:

  • Robotics
  • Human-Robot Interaction
  • Industrial Automation

Background:

  • Industrial environments demand high automation for flexibility and cost-efficiency.
  • Autonomous and collaborative robots are crucial for adapting to dynamic conditions, including human presence.
  • Lack of human awareness in shared workspaces poses safety risks and reduces productivity.

Purpose of the Study:

  • To survey sensory equipment for human detection and action recognition in industrial settings.
  • To analyze perception techniques and sensors for various robotic systems (fixed-base, collaborative, mobile).
  • To present proofs of concept for enhanced human perception and interaction in collaborative robotics.

Main Methods:

  • Literature review of sensors and perception techniques for human detection and action recognition.
  • Analysis of sensor suitability for different robotic platforms (manipulators, mobile robots).
  • Development and presentation of two proofs of concept for enhanced human-robot collaboration.

Main Results:

  • Identified key sensors and perception methods for human detection and action recognition in industrial environments.
  • Evaluated the applicability of these methods across diverse robotic systems.
  • Demonstrated practical solutions for human safety (collision avoidance) and collaborative behavior in shared spaces.

Conclusions:

  • Effective human perception is vital for safe and productive human-robot collaboration in industrial settings.
  • A range of sensors and techniques can be employed to enhance robot awareness of human presence and intentions.
  • The presented proofs of concept offer viable pathways for future collaborative robotic applications.