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Human-in-the-Loop Robot Control for Human-Robot Collaboration: HUMAN INTENTION ESTIMATION AND SAFE TRAJECTORY

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This study enhances human-robot collaboration (HRC) in cyberphysical human systems (CPHSs) by inferring human intentions and using control barrier functions for safe robot motion. This improves efficiency and safety in manufacturing settings.

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Area of Science:

  • Robotics
  • Human-Computer Interaction
  • Control Systems Engineering

Background:

  • Cyberphysical human systems (CPHSs) integrate cyberphysical systems and human elements.
  • Close-proximity human-robot collaboration (HRC) in manufacturing presents challenges in efficiency, safety, and human factors.
  • Controlling robot actions based on human factors and robot capabilities is crucial for effective collaboration.

Purpose of the Study:

  • To address estimation and control challenges in CPHSs, specifically within HRC.
  • To develop methods for inferring human intentions and designing controllers for safe robot motion.
  • To enhance the safety, efficiency, and comfort of human-robot interactions in manufacturing.

Main Methods:

  • Intention inference using skeletal tracking (Microsoft Kinect) and gaze data.
  • Control barrier functions to constrain robot motion within a 3D space.
  • Fusion of motion and gaze data for forward hand trajectory estimation.
  • Adaptive controller for trajectory tracking with the Baxter robot in Gazebo simulation.

Main Results:

  • Successful inference of human intention during a pick-and-place task.
  • Generation of safe robot trajectories that account for predicted human movements.
  • Demonstrated feasibility of the approach in a simulated manufacturing environment.

Conclusions:

  • The proposed methods effectively infer human intention and ensure safe robot motion in HRC.
  • Integrating human factors into control systems significantly improves collaborative task performance.
  • This work advances the development of intelligent and safe CPHSs for real-world applications.