A Novel Approach to Grasping Control of Soft Robotic Grippers based on Digital Twin

TL;DR

This paper introduces a digital twin framework for real-time control of soft robotic grippers, integrating computer vision and kinematic modeling to enhance manipulation precision for industrial use.

Contribution

The paper presents a novel digital twin approach combining computer vision, kinematic modeling, and pressure control for soft robotic grippers, enabling real-time manipulation.

Findings

Achieves satisfactory real-time control performance.

Successfully maps pressure to gripper parameters.

Demonstrates industrial applicability.

Abstract

This paper has proposed a Digital Twin (DT) framework for real-time motion and pose control of soft robotic grippers. The developed DT is based on an industrial robot workstation, integrated with our newly proposed approach for soft gripper control, primarily based on computer vision, for setting the driving pressure for desired gripper status in real-time. Knowing the gripper motion, the gripper parameters (e.g. curvatures and bending angles, etc.) are simulated by kinematics modelling in Unity 3D, which is based on four-piecewise constant curvature kinematics. The mapping in between the driving pressure and gripper parameters is achieved by implementing OpenCV based image processing algorithms and data fitting. Results show that our DT-based approach can achieve satisfactory performance in real-time control of soft gripper manipulation, which can satisfy a wide range of industrial applications.