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

Published version
Peer-reviewed

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Authors

Abdulali, Arsen 
Terryn, Seppe 
Vanderborght, Bram 
Iida, Fumiya 

Abstract

Damage detection is one of the critical challenges in operating soft robots in an industrial setting. In repetitive tasks, even a small cut or fatigue can propagate to large damage ceasing the complete operation process. Although research has shown that damage detection can be performed through an embedded sensor network, this approach leads to complicated sensorized systems with additional wiring and equipment, made using complex fabrication processes and often compromising the flexibility of the soft robotic body. Alternatively, in this paper, we proposed a non-invasive approach for damage detection and localization on soft grippers. The essential idea is to track changes in non-linear dynamics of a gripper due to possible damage, where minor changes in material and morphology lead to large differences in the force and torque feedback over time. To test this concept, we developed a classification model based on a bidirectional long short-time memory (biLSTM) network that discovers patterns of dynamics changes in force and torque signals measured at the mounting point. To evaluate this model, we employed a two-fingered Fin Ray gripper and collected data for 43 damage configurations. The experimental results show nearly perfect damage detection accuracy and 97% of its localization. We have also tested the effect of the gripper orientation and the length of time-series data. By shaking the gripper with an optimal roll angle, the localization accuracy can exceed 95% and increase further with additional gripper orientations. The results also show that two periods of the gripper oscillation, i.e., roughly 50 data points, are enough to achieve a reasonable level of damage localization.

Description

Peer reviewed: True

Keywords

LSTM, damage detection, damage localization, data-driven modeling, soft gripper

Journal Title

Front Robot AI

Conference Name

Journal ISSN

2296-9144
2296-9144

Volume Title

Publisher

Frontiers Media SA
Sponsorship
European Commission Horizon 2020 (H2020) Future and Emerging Technologies (FET) (828818)