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Published on: August 2, 2016
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
Summary
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.
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.
