Deep CNN-Based Static Modeling of Soft Robots Utilizing Absolute Nodal Coordinate Formulation.

Area of Science:

• Robotics
• Computational Mechanics
• Artificial Intelligence

Background:

• Soft continuum robots mimic natural organisms for adaptable motion.
• Precise modeling and control are essential for their application.
• Existing methods face challenges with complex non-linear dynamics.

Purpose of the Study:

• To develop an efficient inverse quasi-static modeling approach for soft continuum robots.
• To combine the Absolute Nodal Coordinate Formulation (ANCF) with Deep Convolutional Neural Networks (CNN).
• To enable real-time simulation and control for statics modeling.

Main Methods:

• Utilized the Absolute Nodal Coordinate Formulation (ANCF) to represent robot mechanics.
• Developed and optimized Deep Convolutional Neural Networks (CNNs) for inverse statics modeling.
• Conducted extensive numerical experiments and cross-validation of CNN architectures.

Main Results:

• Demonstrated the computational efficiency and precision of the CNN-ANCF approach.
• Validated the suitability of the models for real-time simulation and control.
• Identified optimal CNN architectures through cross-validation.

Conclusions:

• The integration of Deep CNN with ANCF offers significant advantages for soft continuum robot modeling.
• This approach effectively addresses complex inverse statics problems.
• Paves the way for advanced statics modeling and control in soft robotics.