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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.

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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 robothybrid actuationhybrid controlkinematic modelingneural networksoft manipulatortrajectory 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.