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A Novel Human Intention Prediction Approach Based on Fuzzy Rules through Wearable Sensing in Human-Robot Handover.

Rui Zou1, Yubin Liu1, Ying Li2

  • 1State Key Laboratory of Robotics and Systems, Harbin 150001, China.

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Summary

This study introduces a new method for robots to predict human handover intentions using wearable data gloves and fuzzy rules. This improves human-robot collaboration efficiency in intelligent manufacturing.

Keywords:
human–robot handoverintention predictionrobot controlwearable sensing

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Area of Science:

  • Robotics
  • Human-Computer Interaction
  • Intelligent Manufacturing

Background:

  • Human-robot interaction is crucial in collaborative manufacturing.
  • Human-robot handover significantly impacts interaction efficiency.
  • Current research often overlooks human intention prediction in handover.

Purpose of the Study:

  • To develop a novel approach for predicting human handover intentions.
  • To enhance the efficiency and safety of human-robot object handover.
  • To enable robots to anticipate human actions during collaborative tasks.

Main Methods:

  • Utilized wearable data gloves for human handover intention sensing (HIS).
  • Developed a fuzzy rule-based method for human handover intention prediction (HIP).
  • Compared data glove sensing with vision-based and contact-based methods.

Main Results:

  • Wearable data gloves provide occlusion-free and safe intention sensing.
  • The fuzzy rule-based method enables fast and accurate intention prediction.
  • Experimental results validate the proposed approach's efficacy.

Conclusions:

  • The proposed method enhances human handover intention prediction.
  • This advancement improves human-robot collaboration in intelligent manufacturing.
  • Robots can efficiently predict human intentions for smoother handovers.