Grasp mapping for Dexterous Robot Hand: A hybrid approach

Abstract

During past two decades many efforts have been made by different researchers in developing robotic grippers. Some of these grippers are robust and used for handling large objects. On the contrary, certain grippers are adroit enough even to handle biological cells. Wide varieties of grippers are now-a-days available featuring different kinematic ability, dexterity, mode of actuation, usage of sensors, maximum weight carrying capabilities and many more attributes. But they all accord to a single issue, i.e. inspiration. Essentially the goal of developing grippers focuses mainly on the manipulation ability of the human hand. Subsequently the designs have continuously become more and more complicated, which in turn have increased the programming complexity to keep abreast with the advances. Cognition in the field of robotics refers to sensing, generation and interpretation. To inculcate kinesthetic cognition to a robot hand unequivocally implies mapping. In this paper a hybrid methodology based on the existing grasp mapping algorithm has been proposed to increase the efficacy of the robotic hand.