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A Recurrent Neural-Network-Based Real-Time Dynamic Model for Soft Continuum Manipulators.

Abbas Tariverdi¹, Venkatasubramanian Kalpathy Venkiteswaran², Michiel Richter²

¹Department of Physics, University of Oslo, Oslo, Norway.

Frontiers in Robotics and AI

|April 19, 2021

Summary

This study presents a real-time dynamic predictive model for soft continuum manipulators using neural networks. The novel approach offers efficient predictions, outperforming traditional methods in simulations and experiments.

Keywords:

Cosserat rod theory Lie group variational integration continuum manipulators dynamic models recurrent neural network soft robotics

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Area of Science:

Robotics
Artificial Intelligence
Mechanical Engineering

Background:

Soft continuum manipulators offer unique advantages in dexterity and safety.
Accurate real-time dynamic modeling is crucial for their effective control and application.
Existing models often struggle with computational complexity and real-time performance.

Purpose of the Study:

To introduce and validate a novel real-time dynamic predictive model for soft continuum manipulators.
To leverage neural network strategies combined with continuum mechanics for enhanced prediction.
To develop a computationally efficient framework suitable for practical applications.

Main Methods:

A time-space integration scheme was used to discretize continuous dynamics.
Dynamic equations for translation and rotation were decoupled for each manipulator node.
Recurrent neural networks (RNNs) were employed to develop distributed prediction algorithms.

Main Results:

The RNN-based parallel predictive scheme demonstrated efficient, real-time performance.
Simulations on soft continuum elastica illustrated the model's effectiveness.
Experimental validation on a magnetically-actuated soft continuum manipulator confirmed its capabilities.

Conclusions:

The proposed neural network model provides accurate real-time dynamic predictions for soft continuum manipulators.
This approach outperforms classical modeling techniques like the Cosserat rod model.
The model shows significant potential for practical implementation in real-world robotic systems.