Exploring Modeling Techniques for Soft Arms: A Survey on Numerical, Analytical, and Data-Driven Approaches.

Area of Science:

• Robotics
• Mechanical Engineering
• Control Systems

Background:

• Soft arms offer compliance and adaptability, driving interest in industrial automation and biomedical fields.
• Modeling soft arms is challenging due to high degrees of freedom, nonlinearities, and complex material properties.

Purpose of the Study:

• To provide a comprehensive overview of primary modeling approaches for soft robotic arms.
• To analyze the strengths, limitations, and applications of different modeling techniques.
• To guide researchers in selecting and developing effective modeling strategies.

Main Methods:

• Review of numerical methods (e.g., finite element analysis, multi-body dynamics).
• Analysis of analytical techniques based on continuum mechanics.
• Exploration of data-driven models using machine learning and artificial intelligence.

Main Results:

• Numerical methods offer precision but are computationally intensive.
• Analytical models balance insight and computational efficiency.
• Data-driven models enable adaptive, real-time control by bypassing explicit physics.

Conclusions:

• Each modeling approach has distinct advantages and disadvantages for soft arm applications.
• Integration of these methodologies is a promising future direction.
• Effective modeling is crucial for advancing soft robotic arm design and control.