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Genetic algorithm-based optimal design for fluidic artificial muscle (FAM) bundles.

Emily Duan¹, Matthew Bryant¹

¹Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, United States of America.

Bioinspiration & Biomimetics

|November 21, 2024

Summary

This study introduces a design optimization framework for fluidic artificial muscle (FAM) bundles. The framework maps architectural tradeoffs to FAM designs, enabling enhanced force and stroke for soft robotic applications.

Keywords:

fluidic artificial muscle muscle architecture pennate robot design soft actuators

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Area of Science:

Robotics
Biomimetics
Mechanical Engineering

Background:

Natural skeletal muscles inspire soft robotic actuator designs for safe human-robot interaction.
Fluidic artificial muscles (FAMs), or McKibben muscles, offer compliance, low cost, and muscle-like performance.
Previous research modeled FAM bundles in parallel and pennate architectures, with bipennate designs showing advantages under spatial constraints.

Purpose of the Study:

To present a design optimization framework for fluidic artificial muscle (FAM) bundles.
To map architectural tradeoffs to soft actuator designs based on geometric constraints.
To identify optimal FAM bundle topologies for maximum force and stroke within a given spatial envelope.

Main Methods:

Utilized a multi-objective genetic algorithm-based optimization framework.
Modeled FAM bundles inspired by natural muscle fiber architectures (parallel, unipennate, bipennate).
Analyzed the impact of spatial constraints on FAM bundle performance.

Main Results:

The optimization framework identifies desirable topological properties for FAM bundles.
Results reveal tradeoffs between force, stroke, and spatial constraints.
Demonstrated how optimal FAM bundle properties vary with different spatial bounds.

Conclusions:

The framework provides a method to inform design decisions for FAM bundles.
Optimized FAM bundle designs can outperform parallel configurations under spatial limitations.
This approach aids in tailoring soft actuator performance to specific task requirements.