Finger design automation for industrial robot grippers: A review <s> Grasp Synthesis (planning) <s> Abstract Recently a substantial amount of research has been done in the field of dextrous manipulation and hand manoeuvres. The main concern has been how to control robot hands so that they can execute manipulation tasks with the same dexterity and intuition as human hands. This paper surveys multi-fingered robot hand research and development topics which include robot hand design, object force distribution and control, grip transform, grasp stability and its synthesis, grasp stiffness and compliance motion and robot arm-hand coordination. Three main topics are presented in this article. The first is an introduction to the subject. The second concentrates on examples of mechanical manipulators used in research and the methods employed to control them. The third presents work which has been done on the field of object manipulation. <s> BIB001 </s> Finger design automation for industrial robot grippers: A review <s> Grasp Synthesis (planning) <s> This article presents a survey of the existing computational algorithms meant for achieving four important properties in autonomous multifingered robotic hands. The four properties are: dexterity, equilibrium, stability, and dynamic behavior The multifingered robotic hands must be controlled so as to possess these properties and hence be able to autonomously perform complex tasks in a way similar to human hands. Existing algorithms to achieve dexterity primarily involve solving an unconstrained linear programming problem where an objective function can be chosen to represent one or more of the currently known dexterity measures. Algorithms to achieve equilibrium also constitute solving a linear program ming problem wherein the positivity, friction, and joint torque constraints of all fingers are accounted for while optimizing the internal grasping forces. Stability algorithms aim at achiev ing positive definite grasp impedance matrices by solving for the required fingertip impedances. This problem reduces to a nonlinear programming problem. Dynamic behavior algorithms determine fingertip impedances, which, when achieved, lead to a desired dynamic behavior. This problem, too, becomes a linear programming problem. If a robotic hand has to acquire any or all of these proper ties, the corresponding algorithms should become integral parts of the hand control system. These algorithms are collectively referred to in this article as robot grasp synthesis algorithms. <s> BIB002 </s> Finger design automation for industrial robot grippers: A review <s> Grasp Synthesis (planning) <s> In this paper, we survey the field of robotic grasping and the work that has been done in this area over the last two decades, with a slight bias toward the development of the theoretical framework and analytical results in this area. <s> BIB003 </s> Finger design automation for industrial robot grippers: A review <s> Grasp Synthesis (planning) <s> A survey for grasping synthesis method with dexterous robot hand is presented in this paper. The difference of grasping characters is introduced between dexterous hand and underactuated hand. Especially the feature of self-adaptive enveloping grasp by underactuated finger mechanism is outlined as having good performance in grasping unknown objects. In order to generate valid grasps for unknown target objects and apply in real-time control system for underactuated robot hand, a grasping strategy for universal grasp tasks is proposed as based on human knowledge analysis. It is composed by off-line neural networks training section and on-line compute section. Firstly, daily grasped objects are used to build a sample space from human experience. Then, the discrete sample space is computed by a fuzzy clustering method. Finally, the data are used to generate grasp decision scheme by rough set mixed artificial neural networks. The choices of grasp configurations for the underactuated robot hand are simulated for with the aim to show the practical feasibility of the proposed modeling method. <s> BIB004 </s> Finger design automation for industrial robot grippers: A review <s> Grasp Synthesis (planning) <s> This overview presents computational algorithms for generating 3D object grasps with autonomous multi-fingered robotic hands. Robotic grasping has been an active research subject for decades, and a great deal of effort has been spent on grasp synthesis algorithms. Existing papers focus on reviewing the mechanics of grasping and the finger-object contact interactions Bicchi and Kumar (2000) [12] or robot hand design and their control Al-Gallaf et al. (1993) [70]. Robot grasp synthesis algorithms have been reviewed in Shimoga (1996) [71], but since then an important progress has been made toward applying learning techniques to the grasping problem. This overview focuses on analytical as well as empirical grasp synthesis approaches. <s> BIB005 </s> Finger design automation for industrial robot grippers: A review <s> Grasp Synthesis (planning) <s> We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar, or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally, for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations. <s> BIB006 </s> Finger design automation for industrial robot grippers: A review <s> Grasp Synthesis (planning) <s> In this paper we present an algorithm to determine the location of contact points to obtain force closure grasps on tree dimensional objects. The shape of the object is assumed to be given by a triangle mesh - a format widely used in CAD software. Our algorithm can handle an arbitrary number of contact points and does nor require any prior information about their initial locations. Through an iterative process, contact point locations are updated aiming at improving a commonly used grasp quality metric. The process is global in the sense that during the process the whole surface of the object can be explored, and contact point locations can cross sharp edges that usually represent a problem for optimization algorithms relying on smooth surface representations. Extensive simulation results illustrate the performance of the proposed method, outlining strengths and directions for further research. <s> BIB007
Grasp synthesizing can be defined as a search process in the space of possible grasp locations aimed at identifying closured grasps. In general, the grasp synthesis research area is noticeably wide and, in some sense, unstructured. None of the proposed approaches have been widely used and considered as the reference for comparison. BIB007 pointed out two of the most important reasons for this issue. First, there is agreement on neither the definition of standard objects for grasping nor the amount of available preliminary knowledge for grasp planning. The second reason is due to the fact that most of the authors do not make their implementation available for others. Therefore, benchmarking and experimental comparison between different methods is limited to a small portion of research. In the last two decades, several surveys BIB001 BIB002  BIB003 BIB004 BIB005 BIB006 have been carried out in order to somehow categorize and organize the research done in this area. BIB005 present a clear overview of the methods and algorithms that are proposed until 2012. They divide the research in grasp synthesis into analytical approaches and data-driven (also known as empirical or knowledge-based) approaches. This paper follows the same definition and categorization.