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Related Experiment Video

Updated: Feb 7, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
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A Vision-Driven Collaborative Robotic Grasping System Tele-Operated by Surface Electromyography.

Andrés Úbeda1,2, Brayan S Zapata-Impata3,4, Santiago T Puente5,6

  • 1Department of Physics, System Engineering and Signal Theory, University of Alicante, 03690 Alicante, Spain. andres.ubeda@ua.es.

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Summary
This summary is machine-generated.

This study integrates computer vision and surface electromyography (sEMG) for robotic grasping. The combined system achieved 95% grasping accuracy, improving performance by over 13% compared to previous methods.

Keywords:
assistive roboticscomputer visiongraspingsurface electromyography

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Area of Science:

  • Robotics
  • Human-Computer Interaction
  • Biomedical Engineering

Background:

  • Robotic grasping requires precise object manipulation.
  • Integrating human control can enhance robotic task performance.
  • Surface electromyography (sEMG) offers a non-invasive method for biological signal acquisition.

Purpose of the Study:

  • To develop and evaluate a robotic grasping system combining computer vision and sEMG control.
  • To improve grasping accuracy and reliability through human-in-the-loop adjustments.
  • To enable intuitive fine-tuning of robotic hand pre-grasping poses.

Main Methods:

  • A vision-driven system analyzes 3D object features to compute initial pre-grasping poses.
  • Human operators utilize forearm sEMG signals (wrist flexion/extension) for pose correction.
  • Weak sEMG signals facilitate fine adjustments; strong flexion initiates grasping.

Main Results:

  • The integrated system demonstrated a grasping accuracy of approximately 95%.
  • This represents a significant improvement of over 13% compared to experiments without sEMG control.
  • User testing confirmed the system's effectiveness and intuitive control mechanism.

Conclusions:

  • Combining computer vision with sEMG provides an effective method for enhancing robotic grasping.
  • sEMG-based fine-tuning allows for precise adjustments, leading to higher success rates.
  • This hybrid approach offers a promising direction for intuitive and accurate robotic manipulation.