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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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Artificial Intelligence-Based Optimal Grasping Control.

Dongeon Kim1, Jonghak Lee1, Wan-Young Chung2

  • 1Department of Electronics Engineering, Pusan National University, Jangjeon-dong, Geumjeong-gu, Busan 609-735, Korea.

Sensors (Basel, Switzerland)
|November 13, 2020
PubMed
Summary
This summary is machine-generated.

A novel tactile sensing module uses air pressure sensors and deep learning to accurately detect object contact force and location on robot hands. This enables stable grasping and optimized grip force for robotic applications.

Keywords:
air pressure sensordeep neural networktactile sensing module

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Area of Science:

  • Robotics
  • Sensor Technology
  • Artificial Intelligence

Background:

  • Robotic manipulation requires precise tactile feedback for object interaction.
  • Existing tactile sensors often lack the resolution or accuracy for complex grasping tasks.

Purpose of the Study:

  • To develop an advanced tactile sensing module for robot hands.
  • To enhance the detection of contact force and location on robot fingers.
  • To improve grasping stability and efficiency through optimized force control.

Main Methods:

  • Integration of three air pressure sensors at the robot finger tip.
  • Development of a deep neural network for contact force estimation using sensor data.
  • Utilizing air pressure sensor data arrival times for contact point recognition.
  • Application of an artificial neural network to refine contact point resolution.
  • Empirical estimation of optimal grasping force using fuzzy rules.

Main Results:

  • Accurate estimation of contact force at the robot finger.
  • Recognition of contact points with improved resolution (6x4 blocks).
  • Demonstrated stable grasping capabilities through real-world experiments.
  • Empirical determination of optimal grasping force for various objects.

Conclusions:

  • The proposed tactile sensing module significantly enhances robotic grasping capabilities.
  • The combination of air pressure sensing, deep learning, and fuzzy logic offers a robust solution for tactile feedback.
  • This technology paves the way for more dexterous and adaptive robotic manipulation.