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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

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Published on: April 21, 2023

Data-driven method for damage localization on soft robotic grippers based on motion dynamics.

Arsen Abdulali¹, Seppe Terryn^2,3, Bram Vanderborght²

¹Engineering Department, Cambridge, United Kingdom.

Frontiers in Robotics and AI

|December 15, 2022

Summary

This summary is machine-generated.

Detecting damage in soft robots is crucial for industrial use. This study introduces a non-invasive method using non-linear dynamics and a biLSTM network for accurate damage detection and localization in soft grippers.

Keywords:

LSTM damage detection damage localization data-driven modeling soft gripper

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Area of Science:

Robotics
Materials Science
Artificial Intelligence

Background:

Damage in soft robots, especially grippers, poses significant challenges in industrial applications, potentially leading to complete operational failure.
Current damage detection methods often rely on complex embedded sensor networks, compromising robotic flexibility and increasing fabrication complexity.
A non-invasive approach is needed to effectively monitor the integrity of soft robotic systems without altering their physical properties.

Purpose of the Study:

To propose and validate a non-invasive method for detecting and localizing damage in soft grippers.
To leverage changes in non-linear dynamics of the gripper as an indicator of damage.
To develop and evaluate a machine learning model for analyzing these dynamic changes.

Main Methods:

A non-invasive approach was developed by tracking changes in the non-linear dynamics of a soft gripper.
A classification model utilizing a bidirectional long short-time memory (biLSTM) network was implemented to analyze force and torque feedback signals.
Data was collected from a two-fingered Fin Ray gripper across 43 distinct damage configurations.

Main Results:

The biLSTM model achieved nearly perfect accuracy in damage detection.
The proposed method demonstrated 97% accuracy in localizing damage on the soft gripper.
Optimal roll angles and multiple gripper orientations significantly improved localization accuracy, exceeding 95%.

Conclusions:

Non-linear dynamics analysis offers a promising non-invasive strategy for soft robot damage assessment.
The biLSTM-based approach provides a robust and accurate solution for real-time damage detection and localization.
Minimal data, approximately 50 points representing two oscillation periods, is sufficient for effective damage localization.