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Soft Robot Proprioception Using Unified Soft Body Encoding and Recurrent Neural Network.

Liangliang Wang¹, James Lam¹, Xiaojiao Chen¹

¹Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, China.

|March 31, 2023

Summary

This summary is machine-generated.

Soft robots gain body awareness using a bioinspired proprioception system with pneumatic chambers. This approach overcomes sensor limitations, enabling better control and environmental interaction for flexible robots.

Keywords:

kinematic model pressure information proprioception receptor receptor failure soft pneumatic chamber

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Area of Science:

Robotics
Bio-inspired Engineering
Soft Systems

Background:

Soft robots offer compliance and safety advantages over rigid robots.
Sensing high-dimensional body deformation in soft robots remains a significant challenge.
Existing embedded soft strain sensors often exhibit nonlinearity, hysteresis, and fabrication complexity.

Purpose of the Study:

To develop a novel bio-inspired proprioception system for soft robots.
To address the limitations of current soft robot sensing technologies.
To enable soft robots with body movement awareness for enhanced control and interaction.

Main Methods:

A bio-inspired architecture mimicking the human proprioception system was developed.
Paralleled soft pneumatic chambers were used as receptors to sense body deformation, analogous to the human muscle system.
Deep learning tools were employed to generate the kinematic model from unified pressure information.

Main Results:

A three-degrees-of-freedom continuum joint was designed and its kinematic model learned.
The system demonstrated the ability to learn kinematic models from combined actuator and receptor pressure data.
Both hardware and software solutions for graceful degradation in response to receptor failures were investigated.

Conclusions:

The proposed bio-inspired architecture offers an alternative method for soft robot proprioception.
This approach enables soft robots with body movement awareness, crucial for closed-loop control.
The developed system facilitates improved environmental interaction for compliant robotic systems.