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Teleoperated Grasping Using Data Gloves Based on Fuzzy Logic Controller.

Chunxiao Lu¹, Lei Jin¹, Yufei Liu²

¹School of Automotive Engineering, Harbin Institute of Technology (Weihai), Weihai 264209, China.

Biomimetics (Basel, Switzerland)

|February 23, 2024

Summary

This summary is machine-generated.

This study introduces a novel fuzzy logic controller for data gloves to improve teleoperated robot control. The method enhances the accuracy of capturing human intentions and robot arm mapping for precise operation.

Keywords:

data gloves fuzzy logic robot control teleoperation

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Area of Science:

Robotics
Control Systems
Human-Computer Interaction

Background:

Teleoperated robots are increasingly important, with data gloves commonly used for control.
Existing data glove solutions face challenges in accurately capturing user intentions and mapping them to robot actions.

Purpose of the Study:

To propose a novel teleoperation method using data gloves and a fuzzy logic controller.
To address the limitations of current data glove systems in intention recognition and accurate mapping.

Main Methods:

Data from flex sensors on data gloves are collected and normalized to identify human manipulation intentions.
A fuzzy logic controller is designed to translate finger flexion data into robot arm motion commands.

Main Results:

The proposed fuzzy logic controller effectively converts finger flexion into precise robot arm control commands.
Experimental results demonstrate the effectiveness and precision of the novel teleoperation method.

Conclusions:

The developed fuzzy logic-based data glove system offers an effective solution for accurate teleoperation.
This method improves the mapping of human intentions to robot arm movements, enhancing control precision.