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Updated: May 28, 2026

Rod-based Fabrication of Customizable Soft Robotic Pneumatic Gripper Devices for Delicate Tissue Manipulation
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Object Recognition-Based Grasping with a Soft Modular Gripper.

Yu Zhang1, Fengwen Zhang1, Zhihui Guo1

  • 1Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin 150080, China.

Biomimetics (Basel, Switzerland)
|May 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel modular soft gripper with pneumatic multi-chamber actuation. Its adaptable design and enhanced fingertip force improve grasping capabilities for various applications.

Keywords:
YOLOv8deformation modelingobject recognitionsoft modular gripper

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Area of Science:

  • Robotics
  • Materials Science
  • Mechanical Engineering

Background:

  • Soft modular grippers are crucial in various fields due to their adaptability and flexibility.
  • Pneumatically actuated systems offer precise control for soft robotic applications.

Purpose of the Study:

  • To propose a novel modular soft gripper driven by pneumatically actuated multi-chambers.
  • To establish a theoretical foundation for grasping tasks and define the gripper's operational workspace.
  • To enhance object recognition and grasping stability using improved visual sensing technology.

Main Methods:

  • Design of a modular soft gripper with independently controlled dual-chamber fingers.
  • Application of classical laminated plate theory and large deformation analysis to model finger bending.
  • Utilizing the Denavit-Hartenberg (D-H) parameter method to define the gripper's coordinate system and workspace.
  • Integration of an improved YOLOv8-based framework for object recognition and localization.

Main Results:

  • The dual-chamber structure significantly enhances fingertip force.
  • A theoretical model relating finger bending to driving pressure was established.
  • The Denavit-Hartenberg parameters defined the operational workspace.
  • Improved object recognition accuracy and grasping stability were achieved through enhanced visual sensing.

Conclusions:

  • The proposed modular soft gripper demonstrates enhanced grasping capabilities through its novel design and control.
  • The theoretical framework provides a basis for optimizing grasping strategies.
  • The integration of advanced visual sensing ensures reliable and stable object manipulation in robotic systems.