Position-Based ANN Impedance Control for A Three-Fingered Robot Hand

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 robot’s 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 𝐾𝑑. 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. 𝐾𝑑 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 𝐾𝑑 selection rule in future studies.