Year: 2024
Type: article
Abstract: A feedforward ANN was previously developed for recognition of two objects i.e. a spongy ball and a plastic bottle but was verified through simulation only. In this work, the feasibility of the ANN model is tested by applying it to the robotβs impedance control which takes the exerted force at the finger as input while resulting in an output for the selection rule of the impedance stiffness parameter, named πΎπ. From the results, the different object textures can be distinguished by the ANN where the absolute peak values of measured rate force during contact with the ball reached 0.15 N and a slightly higher value of 0.32 N for the bottle. πΎπ values were found to switch between 1000 and 250 based on the ANN outputs for the ball and bottle, respectively, thus affecting the dynamics of the fingertip through modified position reference of the fingertip. However, it is also observed that the object was incorrectly classified in some moments when the exerted force was not sufficient due to the weak grasp of the object. This shows that the nonlinear factor from the hardware defects needs to be considered when refining the πΎπ selection rule in future studies.
Author: R.L.A. Shauri
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4402396924