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Grasp mapping for Dexterous Robot Hand: A hybrid approach

机译：敏捷机器人手的掌握映射：一种混合方法

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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.
机译：在过去的二十年中，不同的研究人员在开发机器人抓具方面做出了许多努力。这些抓手中的一些抓手坚固，可用于处理大型物体。相反，某些夹持器甚至足以处理生物细胞。如今，抓手种类繁多，具有不同的运动能力，灵巧性，致动方式，传感器使用方式，最大承重能力以及更多其他属性。但是它们都符合一个问题，即灵感。本质上，开发抓爪的目标主要集中在人手的操纵能力上。随后，设计不断变得越来越复杂，这反过来又增加了编程复杂度，以与时俱进。机器人技术领域的认知是指感知，生成和解释。将动觉认知灌输给机械手无疑意味着映射。本文提出了一种基于现有抓图算法的混合方法，以提高机械手的功效。

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