Object grasping classification using K-Nearest Neighbour for enhance dexterity of robotic arm

Abstract

The artificial intelligence (AI) creation for the improvement of gripping/grasping is essential to improve the dexterity of the prosthetic arm. In this research article, the natural intelligence of an object holding on the basis of elasticity of materials is attempted to model incorporation with k-NN (k-Nearest Neighbour) based data classifier system. With a justified experiment, implementing by front finger piezo sensor mounted glove arrangement k-NN classified knowledge-based data has been generated. The captured data arranged according to the attributes of classes with simulation by efficient k-NN methodology. The present study enables towards the improvement of intelligence in a robotic arm model is the most useful associate for every automatic operation such as object material identification and placing at another predefined location.