A Soft Robotic Module with Pneumatic Actuation and Enhanced Controllability Using a Shape Memory Alloy Wire

Mohammadnavid Golchin · arXiv · 2025

Embedding an SMA wire into a pneumatic soft robotic bending module enables more precise, faster closed-loop control of bending angle with lower working pressure.

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Plain English summary

The paper presents a soft robotic bending module driven by compressed air. To improve how accurately it can bend, the authors incorporate a shape memory alloy (SMA) wire into the module. They first fabricate a fiber-reinforced bending module with a strain-limiting layer made of polypropylene. They then replace that strain-limiting layer with a silicon matrix that holds the SMA wire, aiming to better regulate bending behavior. A simple closed-loop control algorithm uses camera measurements of the bending angle in the vertical plane. Experiments test bending angles from 0 to 65 degrees and show that the SMA wire improves control precision and speed, reducing angle error and rise time while also allowing more bending with less working pressure.

Why this matters

Using an SMA wire placed in a silicon matrix as a new strain-limiting layer within a compressed-air-actuated soft robotic module to improve bending-angle precision and controllability. The abstract reports experimental tests but does not provide evidence of prototype deployment, field testing, manufacturability, or commercialization.

Key findings

  • SMA wire integration improves precision of bending-angle control in the vertical plane.
  • Angle error range reduced from an average of 5 degrees to 2 degrees.
  • Rise time reduced from an average of 19 seconds to 3 seconds.
  • More bending is possible with less working pressure.

Limitations

The abstract does not specify long-term durability, robustness to disturbances, performance beyond the tested angle range (0–65 degrees), or comparisons to other actuation/control approaches beyond the reported metrics.

Publication

Publisher
arXiv
Publication date
June 6, 2025
Research type
Preprint
arXiv
2506.05741
Access
open

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Method note: Summaries and ratings on this page are generated by AI from the abstract only. Read the original paper for full context. · Model: gpt-5.4-nano-2026-03-17