Development of a variable negative pressure Jamming Gripper through visual object size classification and Artificial Neural Network

Abstract

The Universal Jamming Gripper, made up of a coffee grain-filled balloon membrane and a vacuum pump, operates by applying an unvarying or constant negative pressure set by the manipulator on the target object for gripping, not taking into account the suitable amount of negative pressure depending on the size and weight of the target object. With the constant or same amount of negative pressure applied on the target objects of different sizes and weight, the gripper can diminish the structural integrity of some of these target objects. To eradicate this problem, the researchers developed a system to vary the negative pressure of the Universal Jamming Gripper through an artificial neural network depending on the size of the object, obtained through a vision-based object classification scheme, and weight obtained from a load cell connected to the computer. An Artificial Neural Network (ANN), trained with three inputs, such as the pixel area of one side of the target object, the pixel area of another side of the object and its weight, is used to automatically determine the optimum negative pressure needed to successfully grip the target object. After testing and experimentation, the ANN is proven to output the optimum negative pressure needed to successfully conform to and grip the target object, as evident with the 99.131% result from testing, based on the regression plot from MatLab.