Patterned Structure Muscle : Arbitrary Shaped Wire-driven Artificial Muscle Utilizing Anisotropic Flexible Structure for Musculoskeletal Robots

Shunnosuke Yoshimura, Akihiro Miki, Kazuhiro Miyama, Yuta Sahara, Kento Kawaharazuka, Kei Okada +1 · arXiv · 2024

The proposed Patterned Structure Muscle (PSM) enables musculoskeletal robots to generate force in various shapes and environments using flexible materials.

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Plain English summary

This research introduces the Patterned Structure Muscle (PSM), a new type of artificial muscle designed for musculoskeletal robots. It utilizes flexible Thermoplastic Polyurethane (TPU) and a wire-driven mechanism to create muscles that can take on various shapes and exert force effectively. The PSM can be 3D printed in different configurations, allowing for a wide range of motion and adaptability in challenging environments.

Why this matters

This work addresses the limitations of current artificial muscles in robotics, which struggle to generate force in diverse shapes and conditions. By improving the functionality of robotic muscles, this research could enhance the performance and versatility of robots in various applications, making them more effective in tasks that require adaptability.

Key findings

  • Introduction of Patterned Structure Muscle (PSM) for musculoskeletal robots.
  • Utilizes anisotropic characteristics and wire-driven mechanisms.
  • Made from flexible Thermoplastic Polyurethane (TPU) using FDM 3D printing.
  • Demonstrated capability to operate in various shapes and environments.
  • Showed effective lifting and movement through environmental contact.

What's new

The PSM introduces a new approach to artificial muscles by combining patterned structures with flexible materials, allowing for greater adaptability in robotic applications.

Limitations

The abstract does not provide details on the performance metrics or comparisons with existing technologies.

Commercial context

While the technology shows promise, further development and testing are likely needed before commercial application.

Publication

Publisher
arXiv
Publication date
October 10, 2024
Research type
Preprint
arXiv
2410.07682
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-4o-mini-2024-07-18