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

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 la

SUBMITTER: Abdulali A 

PROVIDER: S-EPMC9742418 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature