Finger design automation for industrial robot grippers: A review <s> Data-driven Approaches <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> BIB001 </s> Finger design automation for industrial robot grippers: A review <s> Data-driven Approaches <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> BIB002 </s> Finger design automation for industrial robot grippers: A review <s> Data-driven Approaches <s> Although there has been great progress in robot grasp planning, automatically generated grasp sets using a quality metric are not as robust as human-generated grasp sets when applied to real problems. Most previous research on grasp quality metrics has focused on measuring the quality of established grasp contacts after grasping, but it is difficult to reproduce the same planned final grasp configuration with a real robot hand, which makes the quality evaluation less useful in practice. In this study, we focus more on the grasping process, which usually involves changes in contact and object location, and explore the efficacy of using dynamic simulation in estimating the likely success or failure of a grasp in the real environment. Among many factors that can possibly affect the result of grasping, we particularly investigated the effect of considering object dynamics and pose uncertainty on the performance in estimating the actual grasp success rates measured from experiments. We observed that considering both dynamics and uncertainty improved the performance significantly, and when applied to automatic grasp set generation, this method generated more stable and natural grasp sets compared with a commonly used method based on kinematic simulation and force-closure analysis. <s> BIB003 </s> Finger design automation for industrial robot grippers: A review <s> Data-driven Approaches <s> In this paper, we suggest gripper quality metrics that indicate the performance of a gripper given an object CAD model and a task description. Those, we argue, can be used in the design and selection of an appropriate gripper when the task is known. We present three different gripper metrics that to some degree build on existing grasp quality metrics and demonstrate these on a selection of parallel jaw grippers. We furthermore demonstrate the performance of the metrics in three different industrial task contexts. <s> BIB004
In data-driven approaches, grasp candidates are sampled for the workpiece and then are ranked based on a specific metric. This methodology normally relies on some existing grasp experience that can be heuristic or is generated in simulation or on a real robot BIB001 . BIB002 review the studies done on data-driven approaches in the context grasp synthesis context. Their review concludes that most of the studies in this area are developed for robot hands which are not interesting for industrial applications. Among few approaches that are applicable to grippers as well, BIB003 introduce a new algorithm to automate generating possible grasp sets through a simulation-based method. Their approach is different from most other studies as it focuses on the grasp process instead of the final grasp configuration. The proposed method starts with defining a grasp with combination of the relative pose of the hand (or gripper) and grasping approach. Then the algorithm selects stable and realistic grasp sets which are more suitable for power grasps. BIB004 recently propose a date-driven method for industrial parallel-jaw grippers. Their algorithm starts with generating a grasp database by sampling on nearly parallel surfaces. Then a dynamics based grasp simulator is used for evaluating grasps. If the gripper can be placed in the sample grasping pose without colliding with the workpiece, the sample is moved to another database, called targets. In the final step of their algorithm, the grasp sets in the targets database are measured by three defined quality metrics. Pros: These methods in comparison to analytical approaches are computational cheap. Cons: The proposed approaches require a great deal of manual input and therefore are not applicable for finger design automation.