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Design and Fabrication of an Elastomeric Unit for Soft Modular Robots in Minimally Invasive Surgery

Design and Fabrication of an Elastomeric Unit for Soft Modular Robots in Minimally Invasive Surgery

Published on: November 14, 2015

Multi-Segment Extendable Soft Manipulator Driven by a Pneumatic-Tendon Coupling Mechanism.

Hongxi Yang¹, Yufeng Zeng¹, Zeyu Zhong¹

¹Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 511442, China.

Biomimetics (Basel, Switzerland)

|October 28, 2025

Summary

This summary is machine-generated.

This study presents a novel Multi-Segment Extendable Soft Manipulator (MSESM) for high-precision control in complex environments. The innovative design and hybrid control method significantly reduce tracking errors in soft robots.

Keywords:

continuum robot hybrid actuation hybrid control kinematic modeling neural network soft manipulator trajectory tracking

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Area of Science:

Robotics
Mechanical Engineering
Control Systems

Background:

Continuum robots offer high flexibility but lack precision control compared to rigid robots.
Existing soft manipulators struggle to balance adaptability with accurate motion.
Achieving high-precision control in soft robotic systems remains a key challenge.

Purpose of the Study:

To introduce an innovative Multi-Segment Extendable Soft Manipulator (MSESM) with enhanced stiffness and a hybrid control strategy.
To demonstrate the feasibility of combining a Constant Curvature (CC) physical prior with a data-driven neural network for improved control.
To validate a practical approach for soft manipulators that integrate flexibility with high-precision capabilities.

Main Methods:

Developed a Multi-Segment Extendable Soft Manipulator (MSESM) using pneumatic-tendon hybrid actuation, off-the-shelf bellows, and 3D-printed parts.
Implemented a differential stiffness design to increase bending, axial stiffness, and torsional resistance, minimizing inter-segment coupling.
Proposed a hybrid control method integrating a Constant Curvature (CC) physical prior with a data-driven neural network.

Main Results:

The MSESM achieved an extension ratio of up to 156.85%.
Differential stiffness design increased bending stiffness 4-5x, axial stiffness 10x, and enhanced torsional resistance.
The hybrid control method reduced average trajectory tracking error by 60.43% compared to a purely neural network controller.

Conclusions:

The developed MSESM effectively merges high flexibility with high-precision control.
The hybrid control strategy significantly improves tracking accuracy for soft manipulators.
This research offers a practical and validated solution for advanced soft robotic applications.